chore: release v0.3.1

.github/workflows/ci.yml

@@ -6,6 +6,9 @@ on:
   pull_request:
     branches: [main, master]
 
+permissions:
+  contents: read
+
 env:
   CARGO_TERM_COLOR: always
 
@@ -275,7 +278,14 @@ jobs:
           fi
 
       - name: Publish
-        if: steps.check.outputs.skip == 'false' && github.ref == 'refs/heads/master'
+        if: steps.check.outputs.skip == 'false'
-        run: cargo publish
+        run: |
+          cargo publish --allow-dirty 2>&1 | tee publish_output.txt || {
+            if grep -q "already uploaded" publish_output.txt; then
+              echo "Version already published, skipping"
+              exit 0
+            fi
+            exit 1
+          }
         env:
           CARGO_REGISTRY_TOKEN: ${{ secrets.CARGO_REGISTRY_TOKEN }}

.github/workflows/scheduled.yml

@@ -5,6 +5,9 @@ on:
     - cron: "0 6 * * *" # Daily at 6:00 UTC
   workflow_dispatch: # Allow manual trigger
 
+permissions:
+  contents: read
+
 env:
   CARGO_TERM_COLOR: always
 

Cargo.toml

@@ -1,13 +1,16 @@
 [package]
 name = "optimizer"
-version = "0.2.0"
+version = "0.3.1"
 edition = "2024"
 rust-version = "1.88"
 license = "MIT"
 authors = ["Manuel Raimann <raimannma@outlook.de"]
 description = "A Rust library for optimization algorithms."
 repository = "https://github.com/raimannma/rust-optimizer"
-
+documentation = "https://docs.rs/optimizer"
+keywords = ["optimization", "hyperparameter", "tpe", "grid-search", "bayesian"]
+categories = ["algorithm", "science", "data-structures"]
+readme = "README.md"
 
 [dependencies]
 rand = "0.9"

README.md

@@ -1,6 +1,6 @@
 # optimizer
 
-A Rust library for black-box optimization using Tree-Parzen Estimator (TPE).
+A Rust library for black-box optimization with multiple sampling strategies.
 
 [![Docs](https://docs.rs/optimizer/badge.svg)](https://docs.rs/optimizer)
 [![Crates.io](https://img.shields.io/crates/v/optimizer.svg)](https://crates.io/crates/optimizer)
@@ -9,6 +9,10 @@ A Rust library for black-box optimization using Tree-Parzen Estimator (TPE).
 ## Features
 
 - Optuna-like API for hyperparameter optimization
+- Multiple sampling strategies:
+  - **Random Search** - Simple random sampling for baseline comparisons
+  - **TPE (Tree-Parzen Estimator)** - Bayesian optimization for efficient search
+  - **Grid Search** - Exhaustive search over a specified parameter grid
 - Float, integer, and categorical parameter types
 - Log-scale and stepped parameter sampling
 - Sync and async optimization with parallel trial evaluation
@@ -16,15 +20,16 @@ A Rust library for black-box optimization using Tree-Parzen Estimator (TPE).
 ## Quick Start
 
 ```rust
-use optimizer::{Direction, Study, TpeSampler};
+use optimizer::{Direction, Study};
+use optimizer::sampler::tpe::TpeSampler;
 
-let sampler = TpeSampler::builder().seed(42).build();
+let sampler = TpeSampler::builder().seed(42).build().unwrap();
 let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
 study
     .optimize_with_sampler(20, |trial| {
         let x = trial.suggest_float("x", -10.0, 10.0)?;
-        Ok::<_, optimizer::TpeError>(x * x)
+        Ok::<_, optimizer::Error>(x * x)
     })
     .unwrap();
 
@@ -32,6 +37,50 @@ let best = study.best_trial().unwrap();
 println!("Best value: {} at x={:?}", best.value, best.params);
 ```
 
+## Samplers
+
+### Random Search
+
+```rust
+use optimizer::{Direction, Study};
+use optimizer::sampler::random::RandomSampler;
+
+let study: Study<f64> = Study::with_sampler(
+    Direction::Minimize,
+    RandomSampler::with_seed(42),
+);
+```
+
+### TPE (Tree-Parzen Estimator)
+
+```rust
+use optimizer::{Direction, Study};
+use optimizer::sampler::tpe::TpeSampler;
+
+let sampler = TpeSampler::builder()
+    .gamma(0.15)           // Quantile for good/bad split
+    .n_startup_trials(20)  // Random trials before TPE kicks in
+    .n_ei_candidates(32)   // Candidates to evaluate
+    .seed(42)
+    .build()
+    .unwrap();
+
+let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+```
+
+### Grid Search
+
+```rust
+use optimizer::{Direction, Study};
+use optimizer::sampler::grid::GridSearchSampler;
+
+let sampler = GridSearchSampler::builder()
+    .n_points_per_param(10)  // Number of points per parameter dimension
+    .build();
+
+let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+```
+
 ## Feature Flags
 
 - `async` - Enable async optimization methods (requires tokio)

src/error.rs

@@ -1,10 +1,5 @@
-//! Error types for the optimizer library.
+#[derive(Debug, thiserror::Error)]
-
+pub enum Error {
-use thiserror::Error;
-
-/// The error type for TPE operations.
-#[derive(Debug, Error)]
-pub enum TpeError {
     /// Returned when the lower bound is greater than the upper bound.
     #[error("invalid bounds: low ({low}) must be less than or equal to high ({high})")]
     InvalidBounds {
@@ -38,7 +33,27 @@ pub enum TpeError {
     /// Returned when requesting the best trial but no trials have completed.
     #[error("no completed trials available")]
     NoCompletedTrials,
+
+    /// Returned when gamma is not in the valid range (0.0, 1.0).
+    #[error("invalid gamma: {0} must be in (0.0, 1.0)")]
+    InvalidGamma(f64),
+
+    /// Returned when bandwidth is not positive.
+    #[error("invalid bandwidth: {0} must be positive")]
+    InvalidBandwidth(f64),
+
+    /// Returned when KDE is created with empty samples.
+    #[error("KDE requires at least one sample")]
+    EmptySamples,
+
+    /// Returned when an internal invariant is violated.
+    #[error("internal error: {0}")]
+    Internal(&'static str),
+
+    /// Returned when an async task fails.
+    #[cfg(feature = "async")]
+    #[error("async task error: {0}")]
+    TaskError(String),
 }
 
-/// A specialized Result type for TPE operations.
+pub type Result<T> = core::result::Result<T, Error>;
-pub type Result<T> = std::result::Result<T, TpeError>;

src/kde.rs

@@ -5,6 +5,8 @@
 
 use rand::Rng;
 
+use crate::error::{Error, Result};
+
 /// A Gaussian kernel density estimator for continuous distributions.
 ///
 /// KDE estimates a probability density function from a set of samples by
@@ -28,7 +30,7 @@ use rand::Rng;
 /// let sample = kde.sample(&mut rng);
 /// ```
 #[derive(Clone, Debug)]
-pub struct KernelDensityEstimator {
+pub(crate) struct KernelDensityEstimator {
     /// The sample points used to construct the KDE.
     samples: Vec<f64>,
     /// The bandwidth (standard deviation) of the Gaussian kernels.
@@ -38,37 +40,45 @@ pub struct KernelDensityEstimator {
 impl KernelDensityEstimator {
     /// Creates a new KDE with automatic bandwidth selection using Scott's rule.
     ///
-    /// Scott's rule sets bandwidth = n^(-1/5) * std_dev, which works well
+    /// Scott's rule sets bandwidth = n^(-1/5) * `std_dev`, which works well
     /// for unimodal distributions close to normal.
     ///
-    /// # Panics
+    /// # Errors
     ///
-    /// Panics if `samples` is empty.
+    /// Returns `Error::EmptySamples` if `samples` is empty.
-    pub fn new(samples: Vec<f64>) -> Self {
+    pub(crate) fn new(samples: Vec<f64>) -> Result<Self> {
-        assert!(!samples.is_empty(), "KDE requires at least one sample");
+        if samples.is_empty() {
+            return Err(Error::EmptySamples);
+        }
 
         let bandwidth = Self::scotts_rule(&samples);
-        Self { samples, bandwidth }
+        Ok(Self { samples, bandwidth })
     }
 
     /// Creates a new KDE with a specified bandwidth.
     ///
     /// Use this when you want explicit control over the smoothing parameter.
     ///
-    /// # Panics
+    /// # Errors
     ///
-    /// Panics if `samples` is empty or `bandwidth` is not positive.
+    /// Returns `Error::EmptySamples` if `samples` is empty.
-    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Self {
+    /// Returns `Error::InvalidBandwidth` if `bandwidth` is not positive.
-        assert!(!samples.is_empty(), "KDE requires at least one sample");
+    pub(crate) fn with_bandwidth(samples: Vec<f64>, bandwidth: f64) -> Result<Self> {
-        assert!(bandwidth > 0.0, "Bandwidth must be positive");
+        if samples.is_empty() {
+            return Err(Error::EmptySamples);
+        }
+        if bandwidth <= 0.0 {
+            return Err(Error::InvalidBandwidth(bandwidth));
+        }
 
-        Self { samples, bandwidth }
+        Ok(Self { samples, bandwidth })
     }
 
     /// Computes bandwidth using Scott's rule.
     ///
     /// Scott's rule: h = n^(-1/5) * sigma
     /// where sigma is the sample standard deviation.
+    #[allow(clippy::cast_precision_loss)]
     fn scotts_rule(samples: &[f64]) -> f64 {
         let n = samples.len() as f64;
         let std_dev = Self::sample_std_dev(samples);
@@ -83,6 +93,7 @@ impl KernelDensityEstimator {
     }
 
     /// Computes the sample standard deviation.
+    #[allow(clippy::cast_precision_loss)]
     fn sample_std_dev(samples: &[f64]) -> f64 {
         let n = samples.len() as f64;
         let mean = samples.iter().sum::<f64>() / n;
@@ -95,13 +106,14 @@ impl KernelDensityEstimator {
     /// The density is computed as the average of Gaussian kernels centered
     /// at each sample point:
     ///
-    /// f(x) = (1/n) * sum_i K((x - x_i) / h)
+    /// f(x) = (1/n) * `sum_i` K((x - `x_i`) / h)
     ///
     /// where K is the standard Gaussian kernel and h is the bandwidth.
-    pub fn pdf(&self, x: f64) -> f64 {
+    #[allow(clippy::cast_precision_loss)]
+    pub(crate) fn pdf(&self, x: f64) -> f64 {
         let n = self.samples.len() as f64;
         let inv_bandwidth = 1.0 / self.bandwidth;
-        let normalization = inv_bandwidth / (2.0 * std::f64::consts::PI).sqrt();
+        let normalization = inv_bandwidth / (2.0 * core::f64::consts::PI).sqrt();
 
         let density: f64 = self
             .samples
@@ -120,7 +132,7 @@ impl KernelDensityEstimator {
     /// Sampling works by:
     /// 1. Uniformly selecting one of the kernel centers (samples)
     /// 2. Adding Gaussian noise with the bandwidth as standard deviation
-    pub fn sample<R: Rng>(&self, rng: &mut R) -> f64 {
+    pub(crate) fn sample<R: Rng>(&self, rng: &mut R) -> f64 {
         // Select a random sample to center the kernel on
         let idx = rng.random_range(0..self.samples.len());
         let center = self.samples[idx];
@@ -130,7 +142,7 @@ impl KernelDensityEstimator {
         let u1: f64 = rng.random();
         let u2: f64 = rng.random();
 
-        let z = (-2.0 * u1.ln()).sqrt() * (2.0 * std::f64::consts::PI * u2).cos();
+        let z = (-2.0 * u1.ln()).sqrt() * (2.0 * core::f64::consts::PI * u2).cos();
         center + z * self.bandwidth
     }
 
@@ -148,7 +160,7 @@ mod tests {
     #[test]
     fn test_kde_pdf_basic() {
         let samples = vec![0.0, 1.0, 2.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // Density should be positive everywhere
         assert!(kde.pdf(0.0) > 0.0);
@@ -164,17 +176,17 @@ mod tests {
     #[test]
     fn test_kde_pdf_integrates_to_one() {
         let samples = vec![0.0, 1.0, 2.0, 3.0, 4.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // Numerical integration over a wide range
         let n_points = 10000;
         let low = -10.0;
         let high = 15.0;
-        let dx = (high - low) / n_points as f64;
+        let dx = (high - low) / f64::from(n_points);
 
         let integral: f64 = (0..n_points)
             .map(|i| {
-                let x = low + (i as f64 + 0.5) * dx;
+                let x = low + (f64::from(i) + 0.5) * dx;
                 kde.pdf(x) * dx
             })
             .sum();
@@ -189,16 +201,16 @@ mod tests {
     #[test]
     fn test_kde_with_bandwidth() {
         let samples = vec![0.0, 1.0, 2.0];
-        let kde = KernelDensityEstimator::with_bandwidth(samples, 0.5);
+        let kde = KernelDensityEstimator::with_bandwidth(samples, 0.5).unwrap();
 
-        assert_eq!(kde.bandwidth(), 0.5);
+        assert!((kde.bandwidth() - 0.5).abs() < f64::EPSILON);
         assert!(kde.pdf(1.0) > 0.0);
     }
 
     #[test]
     fn test_kde_sample_in_reasonable_range() {
         let samples = vec![0.0, 1.0, 2.0, 3.0, 4.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
         let mut rng = rand::rng();
 
         // Samples should generally be in a reasonable range around the data
@@ -213,7 +225,7 @@ mod tests {
     #[test]
     fn test_kde_single_sample() {
         let samples = vec![5.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // Should have positive density near the sample
         assert!(kde.pdf(5.0) > 0.0);
@@ -223,7 +235,7 @@ mod tests {
     #[test]
     fn test_kde_identical_samples() {
         let samples = vec![3.0, 3.0, 3.0, 3.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // Should handle degenerate case with identical samples
         assert!(kde.bandwidth() > 0.0);
@@ -233,7 +245,7 @@ mod tests {
     #[test]
     fn test_scotts_rule_bandwidth() {
         let samples = vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0];
-        let kde = KernelDensityEstimator::new(samples);
+        let kde = KernelDensityEstimator::new(samples).unwrap();
 
         // n = 10, n^(-1/5) ≈ 0.631
         // std_dev ≈ 2.87
@@ -246,23 +258,23 @@ mod tests {
     }
 
     #[test]
-    #[should_panic(expected = "KDE requires at least one sample")]
     fn test_kde_empty_samples() {
         let samples: Vec<f64> = vec![];
-        KernelDensityEstimator::new(samples);
+        let result = KernelDensityEstimator::new(samples);
+        assert!(matches!(result, Err(Error::EmptySamples)));
     }
 
     #[test]
-    #[should_panic(expected = "Bandwidth must be positive")]
     fn test_kde_zero_bandwidth() {
         let samples = vec![1.0, 2.0, 3.0];
-        KernelDensityEstimator::with_bandwidth(samples, 0.0);
+        let result = KernelDensityEstimator::with_bandwidth(samples, 0.0);
+        assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
     }
 
     #[test]
-    #[should_panic(expected = "Bandwidth must be positive")]
     fn test_kde_negative_bandwidth() {
         let samples = vec![1.0, 2.0, 3.0];
-        KernelDensityEstimator::with_bandwidth(samples, -1.0);
+        let result = KernelDensityEstimator::with_bandwidth(samples, -1.0);
+        assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
     }
 }

src/lib.rs

@@ -1,7 +1,24 @@
-//! A Tree-Parzen Estimator (TPE) library for black-box optimization.
+#![forbid(unsafe_code)]
+#![deny(clippy::all)]
+#![deny(unreachable_pub)]
+#![deny(clippy::correctness)]
+#![deny(clippy::suspicious)]
+#![deny(clippy::style)]
+#![deny(clippy::complexity)]
+#![deny(clippy::perf)]
+#![deny(clippy::pedantic)]
+#![deny(clippy::std_instead_of_core)]
+
+//! A black-box optimization library with multiple sampling strategies.
 //!
 //! This library provides an Optuna-like API for hyperparameter optimization
-//! using the Tree-Parzen Estimator algorithm. It supports:
+//! with support for multiple sampling algorithms:
+//!
+//! - **Random Search** - Simple random sampling for baseline comparisons
+//! - **TPE (Tree-Parzen Estimator)** - Bayesian optimization for efficient search
+//! - **Grid Search** - Exhaustive search over a specified parameter grid
+//!
+//! Additional features include:
 //!
 //! - Float, integer, and categorical parameter types
 //! - Log-scale and stepped parameter sampling
@@ -11,17 +28,18 @@
 //! # Quick Start
 //!
 //! ```
-//! use optimizer::{Direction, Study, TpeSampler};
+//! use optimizer::sampler::tpe::TpeSampler;
+//! use optimizer::{Direction, Study};
 //!
 //! // Create a study with TPE sampler
-//! let sampler = TpeSampler::builder().seed(42).build();
+//! let sampler = TpeSampler::builder().seed(42).build().unwrap();
 //! let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 //!
 //! // Optimize x^2 for 20 trials
 //! study
 //!     .optimize_with_sampler(20, |trial| {
 //!         let x = trial.suggest_float("x", -10.0, 10.0)?;
-//!         Ok::<_, optimizer::TpeError>(x * x)
+//!         Ok::<_, optimizer::Error>(x * x)
 //!     })
 //!     .unwrap();
 //!
@@ -35,7 +53,9 @@
 //! A [`Study`] manages optimization trials. Create one with an optimization direction:
 //!
 //! ```
-//! use optimizer::{Direction, RandomSampler, Study, TpeSampler};
+//! use optimizer::sampler::random::RandomSampler;
+//! use optimizer::sampler::tpe::TpeSampler;
+//! use optimizer::{Direction, Study};
 //!
 //! // Minimize with default random sampler
 //! let study: Study<f64> = Study::new(Direction::Minimize);
@@ -72,24 +92,53 @@
 //!         let optimizer = trial.suggest_categorical("optimizer", &["sgd", "adam", "rmsprop"])?;
 //!
 //!         // Return objective value
-//!         Ok::<_, optimizer::TpeError>(x * n as f64)
+//!         Ok::<_, optimizer::Error>(x * n as f64)
 //!     })
 //!     .unwrap();
 //! ```
 //!
-//! # Configuring TPE
+//! # Available Samplers
 //!
-//! The [`TpeSampler`] can be configured using the builder pattern:
+//! ## Random Search
+//!
+//! The simplest sampling strategy, useful for baselines:
 //!
 //! ```
-//! use optimizer::TpeSampler;
+//! use optimizer::sampler::random::RandomSampler;
+//! use optimizer::{Direction, Study};
+//!
+//! let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(42));
+//! ```
+//!
+//! ## TPE (Tree-Parzen Estimator)
+//!
+//! Bayesian optimization that learns from previous trials:
+//!
+//! ```
+//! use optimizer::sampler::tpe::TpeSampler;
 //!
 //! let sampler = TpeSampler::builder()
 //!     .gamma(0.15)           // Quantile for good/bad split
 //!     .n_startup_trials(20)  // Random trials before TPE
 //!     .n_ei_candidates(32)   // Candidates to evaluate
 //!     .seed(42)              // Reproducibility
+//!     .build()
+//!     .unwrap();
+//! ```
+//!
+//! ## Grid Search
+//!
+//! Exhaustive search over a discretized parameter space:
+//!
+//! ```
+//! use optimizer::sampler::grid::GridSearchSampler;
+//! use optimizer::{Direction, Study};
+//!
+//! let sampler = GridSearchSampler::builder()
+//!     .n_points_per_param(10)  // Points per parameter dimension
 //!     .build();
+//!
+//! let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 //! ```
 //!
 //! # Async and Parallel Optimization
@@ -120,13 +169,13 @@ mod distribution;
 mod error;
 mod kde;
 mod param;
-mod sampler;
+pub mod sampler;
 mod study;
 mod trial;
 mod types;
 
-pub use error::{Result, TpeError};
+pub use error::{Error, Result};
-pub use sampler::{CompletedTrial, RandomSampler, Sampler, TpeSampler, TpeSamplerBuilder};
+pub use param::ParamValue;
 pub use study::Study;
-pub use trial::Trial;
+pub use trial::{SuggestableRange, Trial};
 pub use types::{Direction, TrialState};

src/sampler/mod.rs

@@ -1,13 +1,11 @@
 //! Sampler trait and implementations for parameter sampling.
 
-mod random;
+pub mod grid;
+pub mod random;
 pub mod tpe;
 
 use std::collections::HashMap;
 
-pub use random::RandomSampler;
-pub use tpe::{TpeSampler, TpeSamplerBuilder};
-
 use crate::distribution::Distribution;
 use crate::param::ParamValue;
 

src/sampler/random.rs

@@ -17,7 +17,7 @@ use crate::sampler::{CompletedTrial, Sampler};
 /// # Examples
 ///
 /// ```
-/// use optimizer::RandomSampler;
+/// use optimizer::sampler::random::RandomSampler;
 ///
 /// // Create with default RNG
 /// let sampler = RandomSampler::new();
@@ -31,6 +31,7 @@ pub struct RandomSampler {
 
 impl RandomSampler {
     /// Creates a new random sampler with a default random seed.
+    #[must_use]
     pub fn new() -> Self {
         Self {
             rng: Mutex::new(StdRng::from_os_rng()),
@@ -40,6 +41,7 @@ impl RandomSampler {
     /// Creates a new random sampler with a fixed seed for reproducibility.
     ///
     /// Using the same seed will produce the same sequence of sampled values.
+    #[must_use]
     pub fn with_seed(seed: u64) -> Self {
         Self {
             rng: Mutex::new(StdRng::seed_from_u64(seed)),
@@ -54,6 +56,7 @@ impl Default for RandomSampler {
 }
 
 impl Sampler for RandomSampler {
+    #[allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
     fn sample(
         &self,
         distribution: &Distribution,
@@ -110,6 +113,7 @@ impl Sampler for RandomSampler {
 }
 
 #[cfg(test)]
+#[allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
 mod tests {
     use super::*;
     use crate::distribution::{CategoricalDistribution, FloatDistribution, IntDistribution};

src/sampler/tpe.rs

@@ -9,6 +9,7 @@ use rand::rngs::StdRng;
 use rand::{Rng, SeedableRng};
 
 use crate::distribution::Distribution;
+use crate::error::{Error, Result};
 use crate::kde::KernelDensityEstimator;
 use crate::param::ParamValue;
 use crate::sampler::{CompletedTrial, Sampler};
@@ -27,7 +28,7 @@ use crate::sampler::{CompletedTrial, Sampler};
 /// # Examples
 ///
 /// ```
-/// use optimizer::TpeSampler;
+/// use optimizer::sampler::tpe::TpeSampler;
 ///
 /// // Create with default settings
 /// let sampler = TpeSampler::new();
@@ -38,7 +39,8 @@ use crate::sampler::{CompletedTrial, Sampler};
 ///     .n_startup_trials(20)
 ///     .n_ei_candidates(32)
 ///     .seed(42)
-///     .build();
+///     .build()
+///     .unwrap();
 /// ```
 pub struct TpeSampler {
     /// Fraction of trials to consider as "good" (gamma quantile).
@@ -58,9 +60,10 @@ impl TpeSampler {
     ///
     /// Default settings:
     /// - gamma: 0.25 (top 25% of trials are considered "good")
-    /// - n_startup_trials: 10 (random sampling for first 10 trials)
+    /// - `n_startup_trials`: 10 (random sampling for first 10 trials)
-    /// - n_ei_candidates: 24 (evaluate 24 candidates per sample)
+    /// - `n_ei_candidates`: 24 (evaluate 24 candidates per sample)
-    /// - kde_bandwidth: None (uses Scott's rule for automatic bandwidth)
+    /// - `kde_bandwidth`: None (uses Scott's rule for automatic bandwidth)
+    #[must_use]
     pub fn new() -> Self {
         Self {
             gamma: 0.25,
@@ -76,15 +79,17 @@ impl TpeSampler {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSampler;
+    /// use optimizer::sampler::tpe::TpeSampler;
     ///
     /// let sampler = TpeSampler::builder()
     ///     .gamma(0.15)
     ///     .n_startup_trials(20)
     ///     .n_ei_candidates(32)
     ///     .seed(42)
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    #[must_use]
     pub fn builder() -> TpeSamplerBuilder {
         TpeSamplerBuilder::new()
     }
@@ -99,22 +104,24 @@ impl TpeSampler {
     /// * `kde_bandwidth` - Optional fixed bandwidth for KDE. If None, uses Scott's rule.
     /// * `seed` - Optional seed for reproducibility.
     ///
-    /// # Panics
+    /// # Errors
     ///
-    /// Panics if gamma is not in (0.0, 1.0) or if kde_bandwidth is Some but not positive.
+    /// Returns `Error::InvalidGamma` if gamma is not in (0.0, 1.0).
+    /// Returns `Error::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
     pub fn with_config(
         gamma: f64,
         n_startup_trials: usize,
         n_ei_candidates: usize,
         kde_bandwidth: Option<f64>,
         seed: Option<u64>,
-    ) -> Self {
+    ) -> Result<Self> {
-        assert!(
+        if gamma <= 0.0 || gamma >= 1.0 {
-            gamma > 0.0 && gamma < 1.0,
+            return Err(Error::InvalidGamma(gamma));
-            "gamma must be in (0.0, 1.0), got {gamma}"
+        }
-        );
+        if let Some(bw) = kde_bandwidth
-        if let Some(bw) = kde_bandwidth {
+            && bw <= 0.0
-            assert!(bw > 0.0, "kde_bandwidth must be positive, got {bw}");
+        {
+            return Err(Error::InvalidBandwidth(bw));
         }
 
         let rng = match seed {
@@ -122,19 +129,24 @@ impl TpeSampler {
             None => StdRng::from_os_rng(),
         };
 
-        Self {
+        Ok(Self {
             gamma,
             n_startup_trials,
             n_ei_candidates,
             kde_bandwidth,
             rng: Mutex::new(rng),
-        }
+        })
     }
 
     /// Splits trials into good and bad groups based on the gamma quantile.
     ///
-    /// Returns (good_trials, bad_trials) where good_trials contains trials
+    /// Returns (`good_trials`, `bad_trials`) where `good_trials` contains trials
     /// with values below the gamma quantile (for minimization).
+    #[allow(
+        clippy::cast_precision_loss,
+        clippy::cast_possible_truncation,
+        clippy::cast_sign_loss
+    )]
     fn split_trials<'a>(
         &self,
         history: &'a [CompletedTrial],
@@ -149,7 +161,7 @@ impl TpeSampler {
             history[a]
                 .value
                 .partial_cmp(&history[b].value)
-                .unwrap_or(std::cmp::Ordering::Equal)
+                .unwrap_or(core::cmp::Ordering::Equal)
         });
 
         // Calculate the split point (gamma quantile)
@@ -171,6 +183,11 @@ impl TpeSampler {
     }
 
     /// Samples uniformly from a distribution (used during startup phase).
+    #[allow(
+        clippy::cast_possible_truncation,
+        clippy::cast_precision_loss,
+        clippy::unused_self
+    )]
     fn sample_uniform(&self, distribution: &Distribution, rng: &mut StdRng) -> ParamValue {
         match distribution {
             Distribution::Float(d) => {
@@ -241,6 +258,11 @@ impl TpeSampler {
             None => KernelDensityEstimator::new(bad_internal),
         };
 
+        // If KDE construction fails, fall back to uniform sampling
+        let (Ok(l_kde), Ok(g_kde)) = (l_kde, g_kde) else {
+            return rng.random_range(low..=high);
+        };
+
         // Generate candidates from l(x) and select the one with best l(x)/g(x) ratio
         let mut best_candidate = internal_low;
         let mut best_ratio = f64::NEG_INFINITY;
@@ -289,15 +311,19 @@ impl TpeSampler {
     }
 
     /// Samples using TPE for integer distributions.
-    #[allow(clippy::too_many_arguments)]
+    #[allow(
+        clippy::too_many_arguments,
+        clippy::cast_precision_loss,
+        clippy::cast_possible_truncation
+    )]
     fn sample_tpe_int(
         &self,
         low: i64,
         high: i64,
         log_scale: bool,
         step: Option<i64>,
-        good_values: Vec<i64>,
+        good_values: &[i64],
-        bad_values: Vec<i64>,
+        bad_values: &[i64],
         rng: &mut StdRng,
     ) -> i64 {
         // Convert to floats for KDE
@@ -331,23 +357,24 @@ impl TpeSampler {
     }
 
     /// Samples using TPE for categorical distributions.
+    #[allow(clippy::cast_precision_loss, clippy::unused_self)]
     fn sample_tpe_categorical(
         &self,
         n_choices: usize,
-        good_indices: Vec<usize>,
+        good_indices: &[usize],
-        bad_indices: Vec<usize>,
+        bad_indices: &[usize],
         rng: &mut StdRng,
     ) -> usize {
         // Count occurrences in good and bad groups
         let mut good_counts = vec![0usize; n_choices];
         let mut bad_counts = vec![0usize; n_choices];
 
-        for &idx in &good_indices {
+        for &idx in good_indices {
             if idx < n_choices {
                 good_counts[idx] += 1;
             }
         }
-        for &idx in &bad_indices {
+        for &idx in bad_indices {
             if idx < n_choices {
                 bad_counts[idx] += 1;
             }
@@ -395,14 +422,15 @@ impl Default for TpeSampler {
 /// # Examples
 ///
 /// ```
-/// use optimizer::TpeSamplerBuilder;
+/// use optimizer::sampler::tpe::TpeSamplerBuilder;
 ///
 /// let sampler = TpeSamplerBuilder::new()
 ///     .gamma(0.15)
 ///     .n_startup_trials(20)
 ///     .n_ei_candidates(32)
 ///     .seed(42)
-///     .build();
+///     .build()
+///     .unwrap();
 /// ```
 #[derive(Debug, Clone)]
 pub struct TpeSamplerBuilder {
@@ -418,10 +446,11 @@ impl TpeSamplerBuilder {
     ///
     /// Default settings:
     /// - gamma: 0.25 (top 25% of trials are considered "good")
-    /// - n_startup_trials: 10 (random sampling for first 10 trials)
+    /// - `n_startup_trials`: 10 (random sampling for first 10 trials)
-    /// - n_ei_candidates: 24 (evaluate 24 candidates per sample)
+    /// - `n_ei_candidates`: 24 (evaluate 24 candidates per sample)
-    /// - kde_bandwidth: None (uses Scott's rule for automatic bandwidth)
+    /// - `kde_bandwidth`: None (uses Scott's rule for automatic bandwidth)
     /// - seed: None (use OS-provided entropy)
+    #[must_use]
     pub fn new() -> Self {
         Self {
             gamma: 0.25,
@@ -441,24 +470,23 @@ impl TpeSamplerBuilder {
     ///
     /// * `gamma` - Quantile value, must be in (0.0, 1.0).
     ///
-    /// # Panics
-    ///
-    /// Panics if gamma is not in (0.0, 1.0).
-    ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .gamma(0.10)  // Use top 10% as "good" trials
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    ///
+    /// # Note
+    ///
+    /// Validation happens at `build()` time. If gamma is not in (0.0, 1.0),
+    /// `build()` will return `Err(Error::InvalidGamma)`.
+    #[must_use]
     pub fn gamma(mut self, gamma: f64) -> Self {
-        assert!(
-            gamma > 0.0 && gamma < 1.0,
-            "gamma must be in (0.0, 1.0), got {gamma}"
-        );
         self.gamma = gamma;
         self
     }
@@ -476,12 +504,14 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .n_startup_trials(20)  // Random sample first 20 trials
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    #[must_use]
     pub fn n_startup_trials(mut self, n: usize) -> Self {
         self.n_startup_trials = n;
         self
@@ -500,12 +530,14 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .n_ei_candidates(48)  // Evaluate more candidates
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    #[must_use]
     pub fn n_ei_candidates(mut self, n: usize) -> Self {
         self.n_ei_candidates = n;
         self
@@ -523,24 +555,23 @@ impl TpeSamplerBuilder {
     ///
     /// * `bandwidth` - The fixed bandwidth (standard deviation) for Gaussian kernels.
     ///
-    /// # Panics
-    ///
-    /// Panics if bandwidth is not positive.
-    ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .kde_bandwidth(0.5)  // Fixed bandwidth of 0.5
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    ///
+    /// # Note
+    ///
+    /// Validation happens at `build()` time. If bandwidth is not positive,
+    /// `build()` will return `Err(Error::InvalidBandwidth)`.
+    #[must_use]
     pub fn kde_bandwidth(mut self, bandwidth: f64) -> Self {
-        assert!(
-            bandwidth > 0.0,
-            "kde_bandwidth must be positive, got {bandwidth}"
-        );
         self.kde_bandwidth = Some(bandwidth);
         self
     }
@@ -554,12 +585,14 @@ impl TpeSamplerBuilder {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .seed(42)  // Reproducible results
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
+    #[must_use]
     pub fn seed(mut self, seed: u64) -> Self {
         self.seed = Some(seed);
         self
@@ -567,19 +600,25 @@ impl TpeSamplerBuilder {
 
     /// Builds the configured [`TpeSampler`].
     ///
+    /// # Errors
+    ///
+    /// Returns `Error::InvalidGamma` if gamma is not in (0.0, 1.0).
+    /// Returns `Error::InvalidBandwidth` if `kde_bandwidth` is Some but not positive.
+    ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::TpeSamplerBuilder;
+    /// use optimizer::sampler::tpe::TpeSamplerBuilder;
     ///
     /// let sampler = TpeSamplerBuilder::new()
     ///     .gamma(0.15)
     ///     .n_startup_trials(20)
     ///     .n_ei_candidates(32)
     ///     .seed(42)
-    ///     .build();
+    ///     .build()
+    ///     .unwrap();
     /// ```
-    pub fn build(self) -> TpeSampler {
+    pub fn build(self) -> Result<TpeSampler> {
         TpeSampler::with_config(
             self.gamma,
             self.n_startup_trials,
@@ -597,6 +636,7 @@ impl Default for TpeSamplerBuilder {
 }
 
 impl Sampler for TpeSampler {
+    #[allow(clippy::too_many_lines)]
     fn sample(
         &self,
         distribution: &Distribution,
@@ -693,8 +733,8 @@ impl Sampler for TpeSampler {
                     d.high,
                     d.log_scale,
                     d.step,
-                    good_values,
+                    &good_values,
-                    bad_values,
+                    &bad_values,
                     &mut rng,
                 );
                 ParamValue::Int(value)
@@ -725,7 +765,7 @@ impl Sampler for TpeSampler {
                 }
 
                 let index =
-                    self.sample_tpe_categorical(d.n_choices, good_indices, bad_indices, &mut rng);
+                    self.sample_tpe_categorical(d.n_choices, &good_indices, &bad_indices, &mut rng);
                 ParamValue::Categorical(index)
             }
         }
@@ -733,6 +773,11 @@ impl Sampler for TpeSampler {
 }
 
 #[cfg(test)]
+#[allow(
+    clippy::similar_names,
+    clippy::cast_sign_loss,
+    clippy::cast_precision_loss
+)]
 mod tests {
     use std::collections::HashMap;
 
@@ -756,34 +801,34 @@ mod tests {
     #[test]
     fn test_tpe_sampler_new() {
         let sampler = TpeSampler::new();
-        assert_eq!(sampler.gamma, 0.25);
+        assert!((sampler.gamma - 0.25).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 10);
         assert_eq!(sampler.n_ei_candidates, 24);
     }
 
     #[test]
     fn test_tpe_sampler_with_config() {
-        let sampler = TpeSampler::with_config(0.15, 20, 32, None, Some(42));
+        let sampler = TpeSampler::with_config(0.15, 20, 32, None, Some(42)).unwrap();
-        assert_eq!(sampler.gamma, 0.15);
+        assert!((sampler.gamma - 0.15).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 20);
         assert_eq!(sampler.n_ei_candidates, 32);
     }
 
     #[test]
-    #[should_panic(expected = "gamma must be in (0.0, 1.0)")]
     fn test_tpe_sampler_invalid_gamma_zero() {
-        TpeSampler::with_config(0.0, 10, 24, None, None);
+        let result = TpeSampler::with_config(0.0, 10, 24, None, None);
+        assert!(matches!(result, Err(Error::InvalidGamma(_))));
     }
 
     #[test]
-    #[should_panic(expected = "gamma must be in (0.0, 1.0)")]
     fn test_tpe_sampler_invalid_gamma_one() {
-        TpeSampler::with_config(1.0, 10, 24, None, None);
+        let result = TpeSampler::with_config(1.0, 10, 24, None, None);
+        assert!(matches!(result, Err(Error::InvalidGamma(_))));
     }
 
     #[test]
     fn test_tpe_startup_random_sampling() {
-        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42)).unwrap();
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
             high: 1.0,
@@ -806,7 +851,7 @@ mod tests {
 
     #[test]
     fn test_tpe_split_trials() {
-        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 10, 24, None, Some(42)).unwrap();
 
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
@@ -820,8 +865,8 @@ mod tests {
             .map(|i| {
                 create_trial(
                     i as u64,
-                    i as f64,
+                    f64::from(i),
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
                 )
             })
             .collect();
@@ -840,7 +885,7 @@ mod tests {
 
     #[test]
     fn test_tpe_samples_float_with_history() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
 
         let dist = Distribution::Float(FloatDistribution {
             low: 0.0,
@@ -852,7 +897,7 @@ mod tests {
         // Create history where low values (near 0.2) are "good"
         let history: Vec<CompletedTrial> = (0..20)
             .map(|i| {
-                let x = i as f64 / 20.0;
+                let x = f64::from(i) / 20.0;
                 // Objective is (x - 0.2)^2, minimized at x=0.2
                 let value = (x - 0.2).powi(2);
                 create_trial(
@@ -882,7 +927,7 @@ mod tests {
 
     #[test]
     fn test_tpe_categorical_sampling() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
 
         let dist = Distribution::Categorical(CategoricalDistribution { n_choices: 4 });
 
@@ -922,7 +967,7 @@ mod tests {
 
     #[test]
     fn test_tpe_int_sampling() {
-        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42));
+        let sampler = TpeSampler::with_config(0.25, 5, 24, None, Some(42)).unwrap();
 
         let dist = Distribution::Int(IntDistribution {
             low: 0,
@@ -968,14 +1013,14 @@ mod tests {
             .map(|i| {
                 create_trial(
                     i as u64,
-                    i as f64,
+                    f64::from(i),
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
                 )
             })
             .collect();
 
-        let sampler1 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345));
+        let sampler1 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345)).unwrap();
-        let sampler2 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345));
+        let sampler2 = TpeSampler::with_config(0.25, 5, 24, None, Some(12345)).unwrap();
 
         for i in 0..10 {
             let v1 = sampler1.sample(&dist, i, &history);
@@ -987,8 +1032,8 @@ mod tests {
     #[test]
     fn test_tpe_sampler_builder_default() {
         let builder = TpeSamplerBuilder::new();
-        let sampler = builder.build();
+        let sampler = builder.build().unwrap();
-        assert_eq!(sampler.gamma, 0.25);
+        assert!((sampler.gamma - 0.25).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 10);
         assert_eq!(sampler.n_ei_candidates, 24);
     }
@@ -1000,8 +1045,9 @@ mod tests {
             .n_startup_trials(20)
             .n_ei_candidates(32)
             .seed(42)
-            .build();
+            .build()
-        assert_eq!(sampler.gamma, 0.15);
+            .unwrap();
+        assert!((sampler.gamma - 0.15).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 20);
         assert_eq!(sampler.n_ei_candidates, 32);
     }
@@ -1012,8 +1058,9 @@ mod tests {
             .gamma(0.10)
             .n_startup_trials(15)
             .n_ei_candidates(48)
-            .build();
+            .build()
-        assert_eq!(sampler.gamma, 0.10);
+            .unwrap();
+        assert!((sampler.gamma - 0.10).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 15);
         assert_eq!(sampler.n_ei_candidates, 48);
     }
@@ -1021,16 +1068,16 @@ mod tests {
     #[test]
     fn test_tpe_sampler_builder_partial() {
         // Test setting only some options
-        let sampler = TpeSamplerBuilder::new().gamma(0.20).build();
+        let sampler = TpeSamplerBuilder::new().gamma(0.20).build().unwrap();
-        assert_eq!(sampler.gamma, 0.20);
+        assert!((sampler.gamma - 0.20).abs() < f64::EPSILON);
         assert_eq!(sampler.n_startup_trials, 10); // default
         assert_eq!(sampler.n_ei_candidates, 24); // default
     }
 
     #[test]
-    #[should_panic(expected = "gamma must be in (0.0, 1.0)")]
     fn test_tpe_sampler_builder_invalid_gamma() {
-        TpeSamplerBuilder::new().gamma(1.5).build();
+        let result = TpeSamplerBuilder::new().gamma(1.5).build();
+        assert!(matches!(result, Err(Error::InvalidGamma(_))));
     }
 
     #[test]
@@ -1042,12 +1089,12 @@ mod tests {
             step: None,
         });
 
-        let history: Vec<CompletedTrial> = (0..20)
+        let history: Vec<CompletedTrial> = (0..20u32)
             .map(|i| {
                 create_trial(
-                    i as u64,
+                    u64::from(i),
-                    i as f64,
+                    f64::from(i),
-                    vec![("x", ParamValue::Float(i as f64 / 20.0), dist.clone())],
+                    vec![("x", ParamValue::Float(f64::from(i) / 20.0), dist.clone())],
                 )
             })
             .collect();
@@ -1055,11 +1102,13 @@ mod tests {
         let sampler1 = TpeSampler::builder()
             .seed(99999)
             .n_startup_trials(5)
-            .build();
+            .build()
+            .unwrap();
         let sampler2 = TpeSampler::builder()
             .seed(99999)
             .n_startup_trials(5)
-            .build();
+            .build()
+            .unwrap();
 
         for i in 0..10 {
             let v1 = sampler1.sample(&dist, i, &history);

src/study.rs

@@ -1,14 +1,15 @@
 //! Study implementation for managing optimization trials.
 
 #[cfg(feature = "async")]
-use std::future::Future;
+use core::future::Future;
-use std::ops::ControlFlow;
+use core::ops::ControlFlow;
+use core::sync::atomic::{AtomicU64, Ordering};
 use std::sync::Arc;
-use std::sync::atomic::{AtomicU64, Ordering};
 
 use parking_lot::RwLock;
 
-use crate::sampler::{CompletedTrial, RandomSampler, Sampler};
+use crate::sampler::random::RandomSampler;
+use crate::sampler::{CompletedTrial, Sampler};
 use crate::trial::Trial;
 use crate::types::Direction;
 
@@ -64,6 +65,7 @@ where
     /// let study: Study<f64> = Study::new(Direction::Minimize);
     /// assert_eq!(study.direction(), Direction::Minimize);
     /// ```
+    #[must_use]
     pub fn new(direction: Direction) -> Self {
         Self::with_sampler(direction, RandomSampler::new())
     }
@@ -78,7 +80,8 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// let sampler = RandomSampler::with_seed(42);
     /// let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
@@ -107,7 +110,8 @@ where
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, Study, TpeSampler};
+    /// use optimizer::sampler::tpe::TpeSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// let mut study: Study<f64> = Study::new(Direction::Minimize);
     /// study.set_sampler(TpeSampler::new());
@@ -271,7 +275,7 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if no trials have been completed.
+    /// Returns `Error::NoCompletedTrials` if no trials have been completed.
     ///
     /// # Examples
     ///
@@ -301,7 +305,7 @@ where
         let trials = self.completed_trials.read();
 
         if trials.is_empty() {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         let best = trials
@@ -313,17 +317,15 @@ where
                 match self.direction {
                     Direction::Minimize => {
                         // Reverse ordering: smaller values are "greater" for max_by
-                        ordering
+                        ordering.map_or(core::cmp::Ordering::Equal, core::cmp::Ordering::reverse)
-                            .map(|o| o.reverse())
-                            .unwrap_or(std::cmp::Ordering::Equal)
                     }
                     Direction::Maximize => {
                         // Normal ordering: larger values are "greater" for max_by
-                        ordering.unwrap_or(std::cmp::Ordering::Equal)
+                        ordering.unwrap_or(core::cmp::Ordering::Equal)
                     }
                 }
             })
-            .expect("trials is not empty");
+            .ok_or(crate::Error::NoCompletedTrials)?;
 
         Ok(best.clone())
     }
@@ -336,7 +338,7 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if no trials have been completed.
+    /// Returns `Error::NoCompletedTrials` if no trials have been completed.
     ///
     /// # Examples
     ///
@@ -385,12 +387,13 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
     ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // Minimize x^2
     /// let sampler = RandomSampler::with_seed(42);
@@ -399,7 +402,7 @@ where
     /// study
     ///     .optimize(10, |trial| {
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///         Ok::<_, optimizer::TpeError>(x * x)
+    ///         Ok::<_, optimizer::Error>(x * x)
     ///     })
     ///     .unwrap();
     ///
@@ -410,7 +413,7 @@ where
     /// ```
     pub fn optimize<F, E>(&self, n_trials: usize, mut objective: F) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> std::result::Result<V, E>,
+        F: FnMut(&mut Trial) -> core::result::Result<V, E>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -428,7 +431,7 @@ where
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -452,12 +455,13 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
     ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -470,7 +474,7 @@ where
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
     ///         // Simulate async work (e.g., network request)
     ///         let value = x * x;
-    ///         Ok::<_, optimizer::TpeError>((trial, value))
+    ///         Ok::<_, optimizer::Error>((trial, value))
     ///     })
     ///     .await?;
     ///
@@ -487,7 +491,7 @@ where
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut,
-        Fut: Future<Output = std::result::Result<(Trial, V), E>>,
+        Fut: Future<Output = core::result::Result<(Trial, V), E>>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -507,7 +511,7 @@ where
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -532,12 +536,14 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::TaskError` if the semaphore is closed or a spawned task panics.
     ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -550,7 +556,7 @@ where
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
     ///         // Async objective function (e.g., network request)
     ///         let value = x * x;
-    ///         Ok::<_, optimizer::TpeError>((trial, value))
+    ///         Ok::<_, optimizer::Error>((trial, value))
     ///     })
     ///     .await?;
     ///
@@ -568,7 +574,7 @@ where
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut + Send + Sync + 'static,
-        Fut: Future<Output = std::result::Result<(Trial, V), E>> + Send,
+        Fut: Future<Output = core::result::Result<(Trial, V), E>> + Send,
         E: ToString + Send + 'static,
         V: Send + 'static,
     {
@@ -580,7 +586,11 @@ where
         let mut handles = Vec::with_capacity(n_trials);
 
         for _ in 0..n_trials {
-            let permit = semaphore.clone().acquire_owned().await.unwrap();
+            let permit = semaphore
+                .clone()
+                .acquire_owned()
+                .await
+                .map_err(|e| crate::Error::TaskError(e.to_string()))?;
             let trial = self.create_trial();
             let objective = Arc::clone(&objective);
 
@@ -595,7 +605,10 @@ where
 
         // Wait for all tasks and record results
         for handle in handles {
-            match handle.await.unwrap() {
+            match handle
+                .await
+                .map_err(|e| crate::Error::TaskError(e.to_string()))?
+            {
                 Ok((trial, value)) => {
                     self.complete_trial(trial, value);
                 }
@@ -607,7 +620,7 @@ where
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -630,15 +643,17 @@ where
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if no trials completed successfully
+    /// Returns `Error::NoCompletedTrials` if no trials completed successfully
     /// before optimization stopped (either by completing all trials or early stopping).
+    /// Returns `Error::Internal` if a completed trial is not found after adding (internal invariant violation).
     ///
     /// # Examples
     ///
     /// ```
     /// use std::ops::ControlFlow;
     ///
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // Stop early when we find a good enough value
     /// let sampler = RandomSampler::with_seed(42);
@@ -649,7 +664,7 @@ where
     ///         100,
     ///         |trial| {
     ///             let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///             Ok::<_, optimizer::TpeError>(x * x)
+    ///             Ok::<_, optimizer::Error>(x * x)
     ///         },
     ///         |_study, completed_trial| {
     ///             // Stop early if we find a value less than 1.0
@@ -673,7 +688,7 @@ where
     ) -> crate::Result<()>
     where
         V: Clone,
-        F: FnMut(&mut Trial) -> std::result::Result<V, E>,
+        F: FnMut(&mut Trial) -> core::result::Result<V, E>,
         C: FnMut(&Study<V>, &CompletedTrial<V>) -> ControlFlow<()>,
         E: ToString,
     {
@@ -686,7 +701,11 @@ where
 
                     // Get the just-completed trial for the callback
                     let trials = self.completed_trials.read();
-                    let completed = trials.last().expect("just added a trial");
+                    let Some(completed) = trials.last() else {
+                        return Err(crate::Error::Internal(
+                            "completed trial not found after adding",
+                        ));
+                    };
 
                     // Call the callback and check if we should stop
                     // Note: We need to drop the read lock before calling callback
@@ -706,7 +725,7 @@ where
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -727,7 +746,8 @@ impl Study<f64> {
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // With a seeded sampler for reproducibility
     /// let sampler = RandomSampler::with_seed(42);
@@ -763,12 +783,13 @@ impl Study<f64> {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
     ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // Minimize x^2 with sampler integration
     /// let sampler = RandomSampler::with_seed(42);
@@ -777,7 +798,7 @@ impl Study<f64> {
     /// study
     ///     .optimize_with_sampler(10, |trial| {
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///         Ok::<_, optimizer::TpeError>(x * x)
+    ///         Ok::<_, optimizer::Error>(x * x)
     ///     })
     ///     .unwrap();
     ///
@@ -790,7 +811,7 @@ impl Study<f64> {
         mut objective: F,
     ) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> std::result::Result<f64, E>,
+        F: FnMut(&mut Trial) -> core::result::Result<f64, E>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -808,7 +829,7 @@ impl Study<f64> {
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -830,14 +851,16 @@ impl Study<f64> {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if no trials completed successfully.
+    /// Returns `Error::NoCompletedTrials` if no trials completed successfully.
+    /// Returns `Error::Internal` if a completed trial is not found after adding (internal invariant violation).
     ///
     /// # Examples
     ///
     /// ```
     /// use std::ops::ControlFlow;
     ///
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// // Optimize with sampler integration and early stopping
     /// let sampler = RandomSampler::with_seed(42);
@@ -848,7 +871,7 @@ impl Study<f64> {
     ///         100,
     ///         |trial| {
     ///             let x = trial.suggest_float("x", -10.0, 10.0)?;
-    ///             Ok::<_, optimizer::TpeError>(x * x)
+    ///             Ok::<_, optimizer::Error>(x * x)
     ///         },
     ///         |study, _completed_trial| {
     ///             // Stop after finding 5 good trials
@@ -870,7 +893,7 @@ impl Study<f64> {
         mut callback: C,
     ) -> crate::Result<()>
     where
-        F: FnMut(&mut Trial) -> std::result::Result<f64, E>,
+        F: FnMut(&mut Trial) -> core::result::Result<f64, E>,
         C: FnMut(&Study<f64>, &CompletedTrial<f64>) -> ControlFlow<()>,
         E: ToString,
     {
@@ -883,7 +906,11 @@ impl Study<f64> {
 
                     // Get the just-completed trial for the callback
                     let trials = self.completed_trials.read();
-                    let completed = trials.last().expect("just added a trial");
+                    let Some(completed) = trials.last() else {
+                        return Err(crate::Error::Internal(
+                            "completed trial not found after adding",
+                        ));
+                    };
 
                     // Call the callback and check if we should stop
                     // Note: We need to drop the read lock before calling callback
@@ -903,7 +930,7 @@ impl Study<f64> {
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -926,12 +953,13 @@ impl Study<f64> {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
     ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -944,7 +972,7 @@ impl Study<f64> {
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
     ///         // Simulate async work (e.g., network request)
     ///         let value = x * x;
-    ///         Ok::<_, optimizer::TpeError>((trial, value))
+    ///         Ok::<_, optimizer::Error>((trial, value))
     ///     })
     ///     .await?;
     ///
@@ -961,7 +989,7 @@ impl Study<f64> {
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut,
-        Fut: Future<Output = std::result::Result<(Trial, f64), E>>,
+        Fut: Future<Output = core::result::Result<(Trial, f64), E>>,
         E: ToString,
     {
         for _ in 0..n_trials {
@@ -981,7 +1009,7 @@ impl Study<f64> {
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())
@@ -1006,12 +1034,14 @@ impl Study<f64> {
     ///
     /// # Errors
     ///
-    /// Returns `TpeError::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::NoCompletedTrials` if all trials failed (no successful trials).
+    /// Returns `Error::TaskError` if the semaphore is closed or a spawned task panics.
     ///
     /// # Examples
     ///
     /// ```
-    /// use optimizer::{Direction, RandomSampler, Study};
+    /// use optimizer::sampler::random::RandomSampler;
+    /// use optimizer::{Direction, Study};
     ///
     /// # #[cfg(feature = "async")]
     /// # async fn example() -> optimizer::Result<()> {
@@ -1024,7 +1054,7 @@ impl Study<f64> {
     ///         let x = trial.suggest_float("x", -10.0, 10.0)?;
     ///         // Async objective function (e.g., network request)
     ///         let value = x * x;
-    ///         Ok::<_, optimizer::TpeError>((trial, value))
+    ///         Ok::<_, optimizer::Error>((trial, value))
     ///     })
     ///     .await?;
     ///
@@ -1042,7 +1072,7 @@ impl Study<f64> {
     ) -> crate::Result<()>
     where
         F: Fn(Trial) -> Fut + Send + Sync + 'static,
-        Fut: Future<Output = std::result::Result<(Trial, f64), E>> + Send,
+        Fut: Future<Output = core::result::Result<(Trial, f64), E>> + Send,
         E: ToString + Send + 'static,
     {
         use tokio::sync::Semaphore;
@@ -1053,7 +1083,11 @@ impl Study<f64> {
         let mut handles = Vec::with_capacity(n_trials);
 
         for _ in 0..n_trials {
-            let permit = semaphore.clone().acquire_owned().await.unwrap();
+            let permit = semaphore
+                .clone()
+                .acquire_owned()
+                .await
+                .map_err(|e| crate::Error::TaskError(e.to_string()))?;
             let trial = self.create_trial_with_sampler();
             let objective = Arc::clone(&objective);
 
@@ -1068,7 +1102,10 @@ impl Study<f64> {
 
         // Wait for all tasks and record results
         for handle in handles {
-            match handle.await.unwrap() {
+            match handle
+                .await
+                .map_err(|e| crate::Error::TaskError(e.to_string()))?
+            {
                 Ok((trial, value)) => {
                     self.complete_trial(trial, value);
                 }
@@ -1080,7 +1117,7 @@ impl Study<f64> {
 
         // Return error if no trials succeeded
         if self.n_trials() == 0 {
-            return Err(crate::TpeError::NoCompletedTrials);
+            return Err(crate::Error::NoCompletedTrials);
         }
 
         Ok(())

src/trial.rs

@@ -1,5 +1,6 @@
 //! Trial implementation for tracking sampled parameters and trial state.
 
+use core::ops::{Range, RangeInclusive};
 use std::collections::HashMap;
 use std::sync::Arc;
 
@@ -8,11 +9,69 @@ use parking_lot::RwLock;
 use crate::distribution::{
     CategoricalDistribution, Distribution, FloatDistribution, IntDistribution,
 };
-use crate::error::{Result, TpeError};
+use crate::error::{Error, Result};
 use crate::param::ParamValue;
 use crate::sampler::{CompletedTrial, Sampler};
 use crate::types::TrialState;
 
+/// A trait for types that can be used with [`Trial::suggest_range`].
+///
+/// This trait is implemented for [`Range`] and [`RangeInclusive`] over `f64` and `i64`.
+/// It allows using Rust's range syntax directly with the optimizer.
+///
+/// # Supported Range Types
+///
+/// | Range Type | Example | Description |
+/// |------------|---------|-------------|
+/// | `Range<f64>` | `0.0..1.0` | Float range (end-exclusive, treated as inclusive for continuous sampling) |
+/// | `RangeInclusive<f64>` | `0.0..=1.0` | Float range (end-inclusive) |
+/// | `Range<i64>` | `1..10` | Integer range from 1 to 9 (end-exclusive) |
+/// | `RangeInclusive<i64>` | `1..=10` | Integer range from 1 to 10 (end-inclusive) |
+pub trait SuggestableRange {
+    /// The output type when suggesting from this range.
+    type Output;
+
+    /// Suggests a value from this range using the given trial.
+    ///
+    /// # Errors
+    ///
+    /// Returns an error if the range is invalid (e.g., empty or low > high).
+    fn suggest(self, trial: &mut Trial, name: String) -> Result<Self::Output>;
+}
+
+impl SuggestableRange for Range<f64> {
+    type Output = f64;
+
+    fn suggest(self, trial: &mut Trial, name: String) -> Result<f64> {
+        trial.suggest_float(name, self.start, self.end)
+    }
+}
+
+impl SuggestableRange for RangeInclusive<f64> {
+    type Output = f64;
+
+    fn suggest(self, trial: &mut Trial, name: String) -> Result<f64> {
+        trial.suggest_float(name, *self.start(), *self.end())
+    }
+}
+
+impl SuggestableRange for Range<i64> {
+    type Output = i64;
+
+    fn suggest(self, trial: &mut Trial, name: String) -> Result<i64> {
+        // Range is exclusive on the end, so subtract 1
+        trial.suggest_int(name, self.start, self.end.saturating_sub(1))
+    }
+}
+
+impl SuggestableRange for RangeInclusive<i64> {
+    type Output = i64;
+
+    fn suggest(self, trial: &mut Trial, name: String) -> Result<i64> {
+        trial.suggest_int(name, *self.start(), *self.end())
+    }
+}
+
 /// A trial represents a single evaluation of the objective function.
 ///
 /// Each trial has a unique ID and stores the sampled parameters along with
@@ -37,8 +96,8 @@ pub struct Trial {
     history: Option<Arc<RwLock<Vec<CompletedTrial<f64>>>>>,
 }
 
-impl std::fmt::Debug for Trial {
+impl core::fmt::Debug for Trial {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
         f.debug_struct("Trial")
             .field("id", &self.id)
             .field("state", &self.state)
@@ -71,6 +130,7 @@ impl Trial {
     /// let trial = Trial::new(0);
     /// assert_eq!(trial.id(), 0);
     /// ```
+    #[must_use]
     pub fn new(id: u64) -> Self {
         Self {
             id,
@@ -110,7 +170,7 @@ impl Trial {
     /// Samples a value from the given distribution using the sampler.
     ///
     /// If the trial has a sampler, it delegates to the sampler's sample method
-    /// with the history of completed trials. Otherwise, it uses the RandomSampler
+    /// with the history of completed trials. Otherwise, it uses the `RandomSampler`
     /// as a fallback.
     fn sample_value(&self, distribution: &Distribution) -> ParamValue {
         if let (Some(sampler), Some(history)) = (&self.sampler, &self.history) {
@@ -118,28 +178,32 @@ impl Trial {
             sampler.sample(distribution, self.id, &history_guard)
         } else {
             // Fallback to RandomSampler when no sampler is configured
-            use crate::sampler::RandomSampler;
+            use crate::sampler::random::RandomSampler;
             let fallback = RandomSampler::new();
             fallback.sample(distribution, self.id, &[])
         }
     }
 
     /// Returns the unique ID of this trial.
+    #[must_use]
     pub fn id(&self) -> u64 {
         self.id
     }
 
     /// Returns the current state of this trial.
+    #[must_use]
     pub fn state(&self) -> TrialState {
         self.state
     }
 
     /// Returns a reference to the sampled parameters.
+    #[must_use]
     pub fn params(&self) -> &HashMap<String, ParamValue> {
         &self.params
     }
 
     /// Returns a reference to the parameter distributions.
+    #[must_use]
     pub fn distributions(&self) -> &HashMap<String, Distribution> {
         &self.distributions
     }
@@ -185,7 +249,7 @@ impl Trial {
     /// ```
     pub fn suggest_float(&mut self, name: impl Into<String>, low: f64, high: f64) -> Result<f64> {
         if low > high {
-            return Err(TpeError::InvalidBounds { low, high });
+            return Err(Error::InvalidBounds { low, high });
         }
 
         let name = name.into();
@@ -200,8 +264,8 @@ impl Trial {
         if let Some(existing_dist) = self.distributions.get(&name) {
             // Verify the distribution matches
             if let Distribution::Float(existing) = existing_dist
-                && existing.low == low
+                && (existing.low - low).abs() < f64::EPSILON
-                && existing.high == high
+                && (existing.high - high).abs() < f64::EPSILON
                 && !existing.log_scale
                 && existing.step.is_none()
             {
@@ -211,7 +275,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -220,9 +284,10 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Float(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
-            ParamValue::Float(v) => v,
+            return Err(Error::Internal(
-            _ => unreachable!("Float distribution should return Float value"),
+                "Float distribution should return Float value",
+            ));
         };
 
         // Store distribution and value
@@ -237,7 +302,7 @@ impl Trial {
     /// The value is sampled uniformly in log space, which is useful for parameters
     /// that span multiple orders of magnitude (e.g., learning rates).
     ///
-    /// If the parameter has already been sampled with the same bounds and log_scale=true,
+    /// If the parameter has already been sampled with the same bounds and `log_scale=true`,
     /// the cached value is returned. If the parameter was sampled with different configuration,
     /// a `ParameterConflict` error is returned.
     ///
@@ -277,11 +342,11 @@ impl Trial {
         high: f64,
     ) -> Result<f64> {
         if low <= 0.0 {
-            return Err(TpeError::InvalidLogBounds);
+            return Err(Error::InvalidLogBounds);
         }
 
         if low > high {
-            return Err(TpeError::InvalidBounds { low, high });
+            return Err(Error::InvalidBounds { low, high });
         }
 
         let name = name.into();
@@ -296,8 +361,8 @@ impl Trial {
         if let Some(existing_dist) = self.distributions.get(&name) {
             // Verify the distribution matches
             if let Distribution::Float(existing) = existing_dist
-                && existing.low == low
+                && (existing.low - low).abs() < f64::EPSILON
-                && existing.high == high
+                && (existing.high - high).abs() < f64::EPSILON
                 && existing.log_scale
                 && existing.step.is_none()
             {
@@ -307,7 +372,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -316,9 +381,10 @@ impl Trial {
 
         // Sample using the sampler (sampler handles log-scale transformation)
         let dist = Distribution::Float(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
-            ParamValue::Float(v) => v,
+            return Err(Error::Internal(
-            _ => unreachable!("Float distribution should return Float value"),
+                "Float distribution should return Float value",
+            ));
         };
 
         // Store distribution and value
@@ -373,11 +439,11 @@ impl Trial {
         step: f64,
     ) -> Result<f64> {
         if step <= 0.0 {
-            return Err(TpeError::InvalidStep);
+            return Err(Error::InvalidStep);
         }
 
         if low > high {
-            return Err(TpeError::InvalidBounds { low, high });
+            return Err(Error::InvalidBounds { low, high });
         }
 
         let name = name.into();
@@ -392,8 +458,8 @@ impl Trial {
         if let Some(existing_dist) = self.distributions.get(&name) {
             // Verify the distribution matches
             if let Distribution::Float(existing) = existing_dist
-                && existing.low == low
+                && (existing.low - low).abs() < f64::EPSILON
-                && existing.high == high
+                && (existing.high - high).abs() < f64::EPSILON
                 && !existing.log_scale
                 && existing.step == Some(step)
             {
@@ -403,7 +469,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -412,9 +478,10 @@ impl Trial {
 
         // Sample using the sampler (sampler handles step-grid)
         let dist = Distribution::Float(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Float(value) = self.sample_value(&dist) else {
-            ParamValue::Float(v) => v,
+            return Err(Error::Internal(
-            _ => unreachable!("Float distribution should return Float value"),
+                "Float distribution should return Float value",
+            ));
         };
 
         // Store distribution and value
@@ -455,9 +522,10 @@ impl Trial {
     /// let n2 = trial.suggest_int("n_layers", 1, 10).unwrap();
     /// assert_eq!(n, n2);
     /// ```
+    #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int(&mut self, name: impl Into<String>, low: i64, high: i64) -> Result<i64> {
         if low > high {
-            return Err(TpeError::InvalidBounds {
+            return Err(Error::InvalidBounds {
                 low: low as f64,
                 high: high as f64,
             });
@@ -486,7 +554,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -495,9 +563,8 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Int(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
-            ParamValue::Int(v) => v,
+            return Err(Error::Internal("Int distribution should return Int value"));
-            _ => unreachable!("Int distribution should return Int value"),
         };
 
         // Store distribution and value
@@ -512,7 +579,7 @@ impl Trial {
     /// The value is sampled uniformly in log space, which is useful for parameters
     /// that span multiple orders of magnitude (e.g., batch sizes).
     ///
-    /// If the parameter has already been sampled with the same bounds and log_scale=true,
+    /// If the parameter has already been sampled with the same bounds and `log_scale=true`,
     /// the cached value is returned. If the parameter was sampled with different configuration,
     /// a `ParameterConflict` error is returned.
     ///
@@ -541,13 +608,14 @@ impl Trial {
     /// let batch_size2 = trial.suggest_int_log("batch_size", 1, 1024).unwrap();
     /// assert_eq!(batch_size, batch_size2);
     /// ```
+    #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int_log(&mut self, name: impl Into<String>, low: i64, high: i64) -> Result<i64> {
         if low < 1 {
-            return Err(TpeError::InvalidLogBounds);
+            return Err(Error::InvalidLogBounds);
         }
 
         if low > high {
-            return Err(TpeError::InvalidBounds {
+            return Err(Error::InvalidBounds {
                 low: low as f64,
                 high: high as f64,
             });
@@ -576,7 +644,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -585,9 +653,8 @@ impl Trial {
 
         // Sample using the sampler (sampler handles log-scale transformation)
         let dist = Distribution::Int(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
-            ParamValue::Int(v) => v,
+            return Err(Error::Internal("Int distribution should return Int value"));
-            _ => unreachable!("Int distribution should return Int value"),
         };
 
         // Store distribution and value
@@ -638,6 +705,7 @@ impl Trial {
     ///     .unwrap();
     /// assert_eq!(n, n2);
     /// ```
+    #[allow(clippy::cast_precision_loss)]
     pub fn suggest_int_step(
         &mut self,
         name: impl Into<String>,
@@ -646,11 +714,11 @@ impl Trial {
         step: i64,
     ) -> Result<i64> {
         if step <= 0 {
-            return Err(TpeError::InvalidStep);
+            return Err(Error::InvalidStep);
         }
 
         if low > high {
-            return Err(TpeError::InvalidBounds {
+            return Err(Error::InvalidBounds {
                 low: low as f64,
                 high: high as f64,
             });
@@ -679,7 +747,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different bounds or type"
                     .to_string(),
@@ -688,9 +756,8 @@ impl Trial {
 
         // Sample using the sampler (sampler handles step-grid)
         let dist = Distribution::Int(distribution);
-        let value = match self.sample_value(&dist) {
+        let ParamValue::Int(value) = self.sample_value(&dist) else {
-            ParamValue::Int(v) => v,
+            return Err(Error::Internal("Int distribution should return Int value"));
-            _ => unreachable!("Int distribution should return Int value"),
         };
 
         // Store distribution and value
@@ -746,7 +813,7 @@ impl Trial {
         choices: &[T],
     ) -> Result<T> {
         if choices.is_empty() {
-            return Err(TpeError::EmptyChoices);
+            return Err(Error::EmptyChoices);
         }
 
         let name = name.into();
@@ -765,7 +832,7 @@ impl Trial {
                 }
             }
             // Distribution exists but doesn't match
-            return Err(TpeError::ParameterConflict {
+            return Err(Error::ParameterConflict {
                 name,
                 reason: "parameter was previously sampled with different number of choices or type"
                     .to_string(),
@@ -774,9 +841,10 @@ impl Trial {
 
         // Sample using the sampler
         let dist = Distribution::Categorical(distribution);
-        let index = match self.sample_value(&dist) {
+        let ParamValue::Categorical(index) = self.sample_value(&dist) else {
-            ParamValue::Categorical(idx) => idx,
+            return Err(Error::Internal(
-            _ => unreachable!("Categorical distribution should return Categorical value"),
+                "Categorical distribution should return Categorical value",
+            ));
         };
 
         // Store distribution and value (store the index)
@@ -785,4 +853,95 @@ impl Trial {
 
         Ok(choices[index].clone())
     }
+
+    /// Suggests a boolean parameter.
+    ///
+    /// The value is selected uniformly at random from `{false, true}`.
+    /// This is equivalent to calling `suggest_categorical(name, &[false, true])`.
+    ///
+    /// If the parameter has already been sampled, the cached value is returned.
+    /// If the parameter was sampled with a different type, a `ParameterConflict` error is returned.
+    ///
+    /// # Arguments
+    ///
+    /// * `name` - The name of the parameter.
+    ///
+    /// # Errors
+    ///
+    /// Returns `ParameterConflict` if the parameter was previously sampled with a different type.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// use optimizer::Trial;
+    ///
+    /// let mut trial = Trial::new(0);
+    /// let use_dropout = trial.suggest_bool("use_dropout").unwrap();
+    /// assert!(use_dropout == true || use_dropout == false);
+    ///
+    /// // Calling again returns cached value
+    /// let use_dropout2 = trial.suggest_bool("use_dropout").unwrap();
+    /// assert_eq!(use_dropout, use_dropout2);
+    /// ```
+    pub fn suggest_bool(&mut self, name: impl Into<String>) -> Result<bool> {
+        self.suggest_categorical(name, &[false, true])
+    }
+
+    /// Suggests a parameter value from a range.
+    ///
+    /// This method accepts both [`Range`] (`..`) and [`RangeInclusive`] (`..=`)
+    /// for both `f64` and `i64` types, allowing natural Rust range syntax.
+    ///
+    /// For integer ranges, note that `Range` (`..`) is end-exclusive while
+    /// `RangeInclusive` (`..=`) is end-inclusive, matching Rust's semantics.
+    ///
+    /// If the parameter has already been sampled with the same bounds, the cached value is returned.
+    /// If the parameter was sampled with different bounds, a `ParameterConflict` error is returned.
+    ///
+    /// # Arguments
+    ///
+    /// * `name` - The name of the parameter.
+    /// * `range` - The range to sample from.
+    ///
+    /// # Type Parameters
+    ///
+    /// * `R` - A range type implementing [`SuggestableRange`].
+    ///
+    /// # Errors
+    ///
+    /// Returns `InvalidBounds` if the range is invalid (e.g., low > high or empty integer range).
+    /// Returns `ParameterConflict` if the parameter was previously sampled with different bounds.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// use optimizer::Trial;
+    ///
+    /// let mut trial = Trial::new(0);
+    ///
+    /// // Float ranges
+    /// let x = trial.suggest_range("x", 0.0..1.0).unwrap();
+    /// assert!(x >= 0.0 && x <= 1.0);
+    ///
+    /// let y = trial.suggest_range("y", 0.0..=1.0).unwrap();
+    /// assert!(y >= 0.0 && y <= 1.0);
+    ///
+    /// // Integer ranges
+    /// let n = trial.suggest_range("n", 1_i64..10).unwrap(); // 1 to 9 inclusive
+    /// assert!(n >= 1 && n <= 9);
+    ///
+    /// let m = trial.suggest_range("m", 1_i64..=10).unwrap(); // 1 to 10 inclusive
+    /// assert!(m >= 1 && m <= 10);
+    ///
+    /// // Calling again with same range returns cached value
+    /// let x2 = trial.suggest_range("x", 0.0..1.0).unwrap();
+    /// assert_eq!(x, x2);
+    /// ```
+    pub fn suggest_range<R: SuggestableRange>(
+        &mut self,
+        name: impl Into<String>,
+        range: R,
+    ) -> Result<R::Output> {
+        range.suggest(self, name.into())
+    }
 }

tests/async_tests.rs

@@ -4,7 +4,9 @@
 
 #![cfg(feature = "async")]
 
-use optimizer::{Direction, RandomSampler, Study, TpeError, TpeSampler};
+use optimizer::sampler::random::RandomSampler;
+use optimizer::sampler::tpe::TpeSampler;
+use optimizer::{Direction, Error, Study};
 
 #[tokio::test]
 async fn test_optimize_async_basic() {
@@ -14,7 +16,7 @@ async fn test_optimize_async_basic() {
     study
         .optimize_async(10, |mut trial| async move {
             let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, TpeError>((trial, x * x))
+            Ok::<_, Error>((trial, x * x))
         })
         .await
         .expect("async optimization should succeed");
@@ -26,14 +28,18 @@ async fn test_optimize_async_basic() {
 
 #[tokio::test]
 async fn test_optimize_async_with_sampler() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
     study
         .optimize_async_with_sampler(15, |mut trial| async move {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, TpeError>((trial, x * x))
+            Ok::<_, Error>((trial, x * x))
         })
         .await
         .expect("async optimization with sampler should succeed");
@@ -51,7 +57,7 @@ async fn test_optimize_parallel() {
     study
         .optimize_parallel(20, 4, |mut trial| async move {
             let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, TpeError>((trial, x * x))
+            Ok::<_, Error>((trial, x * x))
         })
         .await
         .expect("parallel optimization should succeed");
@@ -61,7 +67,11 @@ async fn test_optimize_parallel() {
 
 #[tokio::test]
 async fn test_optimize_parallel_with_sampler() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -69,7 +79,7 @@ async fn test_optimize_parallel_with_sampler() {
         .optimize_parallel_with_sampler(15, 3, |mut trial| async move {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
             let y = trial.suggest_float("y", -5.0, 5.0)?;
-            Ok::<_, TpeError>((trial, x * x + y * y))
+            Ok::<_, Error>((trial, x * x + y * y))
         })
         .await
         .expect("parallel optimization with sampler should succeed");
@@ -89,7 +99,7 @@ async fn test_optimize_async_all_failures() {
         .await;
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -106,7 +116,7 @@ async fn test_optimize_async_with_sampler_all_failures() {
         .await;
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -123,7 +133,7 @@ async fn test_optimize_parallel_all_failures() {
         .await;
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -140,7 +150,7 @@ async fn test_optimize_parallel_with_sampler_all_failures() {
         .await;
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -158,9 +168,9 @@ async fn test_optimize_async_partial_failures() {
             async move {
                 if count.is_multiple_of(2) {
                     let x = trial.suggest_float("x", 0.0, 10.0)?;
-                    Ok::<_, TpeError>((trial, x))
+                    Ok::<_, Error>((trial, x))
                 } else {
-                    Err(TpeError::NoCompletedTrials) // Use as error type
+                    Err(Error::NoCompletedTrials) // Use as error type
                 }
             }
         })
@@ -180,7 +190,7 @@ async fn test_optimize_parallel_high_concurrency() {
     study
         .optimize_parallel(5, 10, |mut trial| async move {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>((trial, x))
+            Ok::<_, Error>((trial, x))
         })
         .await
         .expect("should handle high concurrency");
@@ -197,7 +207,7 @@ async fn test_optimize_parallel_single_concurrency() {
     study
         .optimize_parallel(10, 1, |mut trial| async move {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>((trial, x))
+            Ok::<_, Error>((trial, x))
         })
         .await
         .expect("should work with single concurrency");

tests/integration.rs

@@ -1,6 +1,14 @@
 //! Integration tests for the optimizer library.
 
-use optimizer::{Direction, RandomSampler, Study, TpeError, TpeSampler, Trial};
+#![allow(
+    clippy::cast_sign_loss,
+    clippy::cast_precision_loss,
+    clippy::cast_possible_truncation
+)]
+
+use optimizer::sampler::random::RandomSampler;
+use optimizer::sampler::tpe::TpeSampler;
+use optimizer::{Direction, Error, Study, Trial};
 
 // =============================================================================
 // Test: optimize simple quadratic function with TPE, finds near-optimal
@@ -14,14 +22,15 @@ fn test_tpe_optimizes_quadratic_function() {
         .seed(42)
         .n_startup_trials(5) // Quick startup for test
         .n_ei_candidates(24)
-        .build();
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
     study
         .optimize_with_sampler(50, |trial| {
             let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, TpeError>((x - 3.0).powi(2))
+            Ok::<_, Error>((x - 3.0).powi(2))
         })
         .expect("optimization should succeed");
 
@@ -40,7 +49,11 @@ fn test_tpe_optimizes_quadratic_function() {
 fn test_tpe_optimizes_multivariate_function() {
     // Minimize f(x, y) = x^2 + y^2 where x, y ∈ [-5, 5]
     // Optimal: (0, 0), f(0, 0) = 0
-    let sampler = TpeSampler::builder().seed(123).n_startup_trials(10).build();
+    let sampler = TpeSampler::builder()
+        .seed(123)
+        .n_startup_trials(10)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -48,7 +61,7 @@ fn test_tpe_optimizes_multivariate_function() {
         .optimize_with_sampler(100, |trial| {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
             let y = trial.suggest_float("y", -5.0, 5.0)?;
-            Ok::<_, TpeError>(x * x + y * y)
+            Ok::<_, Error>(x * x + y * y)
         })
         .expect("optimization should succeed");
 
@@ -66,14 +79,18 @@ fn test_tpe_optimizes_multivariate_function() {
 fn test_tpe_maximization() {
     // Maximize f(x) = -(x - 2)^2 + 10 where x ∈ [-10, 10]
     // Optimal: x = 2, f(2) = 10
-    let sampler = TpeSampler::builder().seed(456).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(456)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
 
     study
         .optimize_with_sampler(50, |trial| {
             let x = trial.suggest_float("x", -10.0, 10.0)?;
-            Ok::<_, TpeError>(-(x - 2.0).powi(2) + 10.0)
+            Ok::<_, Error>(-(x - 2.0).powi(2) + 10.0)
         })
         .expect("optimization should succeed");
 
@@ -106,7 +123,7 @@ fn test_random_sampler_uniform_float_distribution() {
         .optimize(n_samples, |trial| {
             let x = trial.suggest_float("x", 0.0, 1.0)?;
             samples.push(x);
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -135,7 +152,7 @@ fn test_random_sampler_uniform_float_distribution() {
 fn test_random_sampler_uniform_int_distribution() {
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(123));
 
-    let n_samples = 1000;
+    let n_samples = 5000;
     let mut counts = [0u32; 10]; // counts for values 1-10
 
     study
@@ -143,16 +160,18 @@ fn test_random_sampler_uniform_int_distribution() {
             let n = trial.suggest_int("n", 1, 10)?;
             assert!((1..=10).contains(&n), "sample {n} out of range [1, 10]");
             counts[(n - 1) as usize] += 1;
-            Ok::<_, TpeError>(n as f64)
+            Ok::<_, Error>(n as f64)
         })
         .unwrap();
 
     // Each value should appear roughly n_samples / 10 times
+    // With 5000 samples, expected ~500 per bucket, std dev ~21
+    // 20% tolerance allows for ~4.5 std devs which is very safe
     let expected = n_samples as f64 / 10.0;
     for (i, &count) in counts.iter().enumerate() {
         let diff = (count as f64 - expected).abs() / expected;
         assert!(
-            diff < 0.3,
+            diff < 0.2,
             "value {} appeared {} times, expected ~{}, diff = {:.1}%",
             i + 1,
             count,
@@ -166,7 +185,7 @@ fn test_random_sampler_uniform_int_distribution() {
 fn test_random_sampler_uniform_categorical_distribution() {
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(456));
 
-    let n_samples = 1000;
+    let n_samples = 2000;
     let mut counts = [0u32; 4];
     let choices = ["a", "b", "c", "d"];
 
@@ -175,16 +194,18 @@ fn test_random_sampler_uniform_categorical_distribution() {
             let choice = trial.suggest_categorical("cat", &choices)?;
             let idx = choices.iter().position(|&c| c == choice).unwrap();
             counts[idx] += 1;
-            Ok::<_, TpeError>(idx as f64)
+            Ok::<_, Error>(idx as f64)
         })
         .unwrap();
 
     // Each category should appear roughly n_samples / 4 times
+    // With 2000 samples, expected ~500 per bucket, std dev ~19
+    // 15% tolerance allows for ~4 std devs which is very safe
     let expected = n_samples as f64 / 4.0;
     for (i, &count) in counts.iter().enumerate() {
         let diff = (count as f64 - expected).abs() / expected;
         assert!(
-            diff < 0.25,
+            diff < 0.15,
             "category {} appeared {} times, expected ~{}, diff = {:.1}%",
             i,
             count,
@@ -210,7 +231,7 @@ fn test_random_sampler_reproducibility() {
         .optimize_with_sampler(100, |trial| {
             let x = trial.suggest_float("x", 0.0, 100.0)?;
             values1.push(x);
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -218,7 +239,7 @@ fn test_random_sampler_reproducibility() {
         .optimize_with_sampler(100, |trial| {
             let x = trial.suggest_float("x", 0.0, 100.0)?;
             values2.push(x);
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -350,7 +371,7 @@ fn test_parameter_conflict_float_different_bounds() {
     trial.suggest_float("x", 0.0, 1.0).unwrap();
     let result = trial.suggest_float("x", 0.0, 2.0); // Different upper bound
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -360,7 +381,7 @@ fn test_parameter_conflict_float_vs_log() {
     trial.suggest_float("x", 0.1, 1.0).unwrap();
     let result = trial.suggest_float_log("x", 0.1, 1.0); // Same bounds but log scale
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -370,7 +391,7 @@ fn test_parameter_conflict_float_vs_step() {
     trial.suggest_float("x", 0.0, 1.0).unwrap();
     let result = trial.suggest_float_step("x", 0.0, 1.0, 0.1); // Same bounds but with step
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -380,7 +401,7 @@ fn test_parameter_conflict_int_different_bounds() {
     trial.suggest_int("n", 1, 10).unwrap();
     let result = trial.suggest_int("n", 1, 20); // Different upper bound
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -390,7 +411,7 @@ fn test_parameter_conflict_int_vs_log() {
     trial.suggest_int("n", 1, 100).unwrap();
     let result = trial.suggest_int_log("n", 1, 100); // Same bounds but log scale
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -400,7 +421,7 @@ fn test_parameter_conflict_categorical_different_n_choices() {
     trial.suggest_categorical("opt", &["a", "b", "c"]).unwrap();
     let result = trial.suggest_categorical("opt", &["x", "y"]); // Different number of choices
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -410,7 +431,7 @@ fn test_parameter_conflict_float_vs_int() {
     trial.suggest_float("x", 0.0, 10.0).unwrap();
     let result = trial.suggest_int("x", 0, 10); // Different type
 
-    assert!(matches!(result, Err(TpeError::ParameterConflict { .. })));
+    assert!(matches!(result, Err(Error::ParameterConflict { .. })));
 }
 
 #[test]
@@ -421,7 +442,7 @@ fn test_parameter_conflict_returns_name() {
     let result = trial.suggest_float("my_param", 0.0, 2.0);
 
     match result {
-        Err(TpeError::ParameterConflict { name, .. }) => {
+        Err(Error::ParameterConflict { name, .. }) => {
             assert_eq!(name, "my_param");
         }
         _ => panic!("expected ParameterConflict error"),
@@ -439,7 +460,7 @@ fn test_empty_categorical_returns_error() {
 
     let result = trial.suggest_categorical("opt", empty);
 
-    assert!(matches!(result, Err(TpeError::EmptyChoices)));
+    assert!(matches!(result, Err(Error::EmptyChoices)));
 }
 
 #[test]
@@ -449,7 +470,7 @@ fn test_empty_categorical_vec_returns_error() {
 
     let result = trial.suggest_categorical("numbers", &empty);
 
-    assert!(matches!(result, Err(TpeError::EmptyChoices)));
+    assert!(matches!(result, Err(Error::EmptyChoices)));
 }
 
 // =============================================================================
@@ -463,7 +484,7 @@ fn test_study_basic_workflow() {
     study
         .optimize(10, |trial| {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, TpeError>(x * x)
+            Ok::<_, Error>(x * x)
         })
         .expect("optimization should succeed");
 
@@ -500,7 +521,7 @@ fn test_no_completed_trials_error() {
     let study: Study<f64> = Study::new(Direction::Minimize);
 
     let result = study.best_trial();
-    assert!(matches!(result, Err(TpeError::NoCompletedTrials)));
+    assert!(matches!(result, Err(Error::NoCompletedTrials)));
 }
 
 #[test]
@@ -509,11 +530,11 @@ fn test_invalid_bounds_errors() {
 
     // low > high for float
     let result = trial.suggest_float("x", 10.0, 5.0);
-    assert!(matches!(result, Err(TpeError::InvalidBounds { .. })));
+    assert!(matches!(result, Err(Error::InvalidBounds { .. })));
 
     // low > high for int
     let result = trial.suggest_int("n", 100, 50);
-    assert!(matches!(result, Err(TpeError::InvalidBounds { .. })));
+    assert!(matches!(result, Err(Error::InvalidBounds { .. })));
 }
 
 #[test]
@@ -522,14 +543,14 @@ fn test_invalid_log_bounds_errors() {
 
     // low <= 0 for log float
     let result = trial.suggest_float_log("x", 0.0, 1.0);
-    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
+    assert!(matches!(result, Err(Error::InvalidLogBounds)));
 
     let result = trial.suggest_float_log("y", -1.0, 1.0);
-    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
+    assert!(matches!(result, Err(Error::InvalidLogBounds)));
 
     // low < 1 for log int
     let result = trial.suggest_int_log("n", 0, 100);
-    assert!(matches!(result, Err(TpeError::InvalidLogBounds)));
+    assert!(matches!(result, Err(Error::InvalidLogBounds)));
 }
 
 #[test]
@@ -538,19 +559,23 @@ fn test_invalid_step_errors() {
 
     // step <= 0 for float
     let result = trial.suggest_float_step("x", 0.0, 1.0, 0.0);
-    assert!(matches!(result, Err(TpeError::InvalidStep)));
+    assert!(matches!(result, Err(Error::InvalidStep)));
 
     let result = trial.suggest_float_step("y", 0.0, 1.0, -0.1);
-    assert!(matches!(result, Err(TpeError::InvalidStep)));
+    assert!(matches!(result, Err(Error::InvalidStep)));
 
     // step <= 0 for int
     let result = trial.suggest_int_step("n", 0, 100, 0);
-    assert!(matches!(result, Err(TpeError::InvalidStep)));
+    assert!(matches!(result, Err(Error::InvalidStep)));
 }
 
 #[test]
 fn test_tpe_with_categorical_parameter() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Maximize, sampler);
 
@@ -567,7 +592,7 @@ fn test_tpe_with_categorical_parameter() {
                 "cubic" => -((x - 1.0).powi(2)) + 10.0, // peak at x=1, max value 10
                 _ => unreachable!(),
             };
-            Ok::<_, TpeError>(value)
+            Ok::<_, Error>(value)
         })
         .expect("optimization should succeed");
 
@@ -582,7 +607,11 @@ fn test_tpe_with_categorical_parameter() {
 
 #[test]
 fn test_tpe_with_integer_parameters() {
-    let sampler = TpeSampler::builder().seed(789).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(789)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -590,7 +619,7 @@ fn test_tpe_with_integer_parameters() {
     study
         .optimize_with_sampler(30, |trial| {
             let n = trial.suggest_int("n", 1, 10)?;
-            Ok::<_, TpeError>(((n - 7) as f64).powi(2))
+            Ok::<_, Error>(((n - 7) as f64).powi(2))
         })
         .expect("optimization should succeed");
 
@@ -618,7 +647,7 @@ fn test_callback_early_stopping() {
             |trial| {
                 trials_run.set(trials_run.get() + 1);
                 let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, TpeError>(x)
+                Ok::<_, Error>(x)
             },
             |_study, _trial| {
                 // Stop after 5 trials
@@ -641,7 +670,7 @@ fn test_study_trials_iteration() {
     study
         .optimize(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -733,7 +762,7 @@ fn test_best_value() {
     study
         .optimize(10, |trial| {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -756,14 +785,18 @@ fn test_study_set_sampler() {
     let mut study: Study<f64> = Study::new(Direction::Minimize);
 
     // Initially uses RandomSampler, now switch to TPE
-    let tpe = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let tpe = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
     study.set_sampler(tpe);
 
     // Should work with the new sampler
     study
         .optimize_with_sampler(10, |trial| {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, TpeError>(x * x)
+            Ok::<_, Error>(x * x)
         })
         .expect("optimization should succeed with new sampler");
 
@@ -778,7 +811,7 @@ fn test_study_with_i32_value_type() {
     study
         .optimize(10, |trial| {
             let x = trial.suggest_int("x", -10, 10)?;
-            Ok::<_, TpeError>(x.abs() as i32)
+            Ok::<_, Error>(x.abs() as i32)
         })
         .expect("optimization should succeed");
 
@@ -795,7 +828,7 @@ fn test_optimize_all_trials_fail() {
     let result = study.optimize(5, |_trial| Err::<f64, &str>("always fails"));
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -813,7 +846,7 @@ fn test_optimize_with_callback_all_trials_fail() {
     );
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -825,7 +858,7 @@ fn test_optimize_with_sampler_all_trials_fail() {
     let result = study.optimize_with_sampler(5, |_trial| Err::<f64, &str>("always fails"));
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -843,7 +876,7 @@ fn test_optimize_with_callback_sampler_all_trials_fail() {
     );
 
     assert!(
-        matches!(result, Err(TpeError::NoCompletedTrials)),
+        matches!(result, Err(Error::NoCompletedTrials)),
         "should return NoCompletedTrials when all trials fail"
     );
 }
@@ -863,17 +896,17 @@ fn test_trial_debug_format() {
 
 #[test]
 fn test_tpe_sampler_builder_default_trait() {
-    use optimizer::TpeSamplerBuilder;
+    use optimizer::sampler::tpe::TpeSamplerBuilder;
 
     let builder = TpeSamplerBuilder::default();
-    let sampler = builder.build();
+    let sampler = builder.build().unwrap();
 
     // Should have default values
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
     study
         .optimize_with_sampler(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -888,7 +921,7 @@ fn test_tpe_sampler_default_trait() {
     study
         .optimize_with_sampler(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 1.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -901,14 +934,15 @@ fn test_tpe_with_fixed_kde_bandwidth() {
         .seed(42)
         .n_startup_trials(5)
         .kde_bandwidth(0.5)
-        .build();
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
     study
         .optimize_with_sampler(20, |trial| {
             let x = trial.suggest_float("x", -5.0, 5.0)?;
-            Ok::<_, TpeError>(x * x)
+            Ok::<_, Error>(x * x)
         })
         .expect("optimization should succeed");
 
@@ -917,9 +951,9 @@ fn test_tpe_with_fixed_kde_bandwidth() {
 }
 
 #[test]
-#[should_panic(expected = "kde_bandwidth must be positive")]
 fn test_tpe_sampler_invalid_kde_bandwidth() {
-    TpeSampler::with_config(0.25, 10, 24, Some(-1.0), None);
+    let result = TpeSampler::with_config(0.25, 10, 24, Some(-1.0), None);
+    assert!(matches!(result, Err(Error::InvalidBandwidth(_))));
 }
 
 #[test]
@@ -928,14 +962,15 @@ fn test_tpe_split_trials_with_two_trials() {
     let sampler = TpeSampler::builder()
         .seed(42)
         .n_startup_trials(2) // TPE kicks in after 2 trials
-        .build();
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
     study
         .optimize_with_sampler(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .expect("optimization should succeed with small history");
 
@@ -944,7 +979,11 @@ fn test_tpe_split_trials_with_two_trials() {
 
 #[test]
 fn test_tpe_with_log_scale_int() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -952,7 +991,7 @@ fn test_tpe_with_log_scale_int() {
         .optimize_with_sampler(20, |trial| {
             let batch_size = trial.suggest_int_log("batch_size", 1, 1024)?;
             // Optimal around batch_size = 32
-            Ok::<_, TpeError>(((batch_size as f64).log2() - 5.0).powi(2))
+            Ok::<_, Error>(((batch_size as f64).log2() - 5.0).powi(2))
         })
         .expect("optimization should succeed");
 
@@ -962,7 +1001,11 @@ fn test_tpe_with_log_scale_int() {
 
 #[test]
 fn test_tpe_with_step_distributions() {
-    let sampler = TpeSampler::builder().seed(42).n_startup_trials(5).build();
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -970,7 +1013,7 @@ fn test_tpe_with_step_distributions() {
         .optimize_with_sampler(20, |trial| {
             let x = trial.suggest_float_step("x", 0.0, 10.0, 0.5)?;
             let n = trial.suggest_int_step("n", 0, 100, 10)?;
-            Ok::<_, TpeError>((x - 5.0).powi(2) + ((n - 50) as f64).powi(2))
+            Ok::<_, Error>((x - 5.0).powi(2) + ((n - 50) as f64).powi(2))
         })
         .expect("optimization should succeed");
 
@@ -1040,7 +1083,8 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
         .seed(42)
         .n_startup_trials(5)
         .gamma(0.1) // Very small gamma means few "good" trials
-        .build();
+        .build()
+        .unwrap();
 
     let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
 
@@ -1048,7 +1092,7 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
     study
         .optimize_with_sampler(10, |trial| {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -1056,7 +1100,7 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
     study
         .optimize_with_sampler(5, |trial| {
             let y = trial.suggest_float("y", 0.0, 10.0)?;
-            Ok::<_, TpeError>(y)
+            Ok::<_, Error>(y)
         })
         .unwrap();
 
@@ -1074,7 +1118,7 @@ fn test_callback_early_stopping_on_first_trial() {
             100,
             |trial| {
                 let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, TpeError>(x)
+                Ok::<_, Error>(x)
             },
             |_study, _trial| {
                 // Stop immediately after first trial
@@ -1098,7 +1142,7 @@ fn test_callback_sampler_early_stopping() {
             100,
             |trial| {
                 let x = trial.suggest_float("x", 0.0, 10.0)?;
-                Ok::<_, TpeError>(x)
+                Ok::<_, Error>(x)
             },
             |study, _trial| {
                 if study.n_trials() >= 3 {
@@ -1134,7 +1178,7 @@ fn test_best_trial_with_nan_values() {
     study
         .optimize(5, |trial| {
             let x = trial.suggest_float("x", 0.0, 10.0)?;
-            Ok::<_, TpeError>(x)
+            Ok::<_, Error>(x)
         })
         .unwrap();
 
@@ -1142,3 +1186,228 @@ fn test_best_trial_with_nan_values() {
     let best = study.best_trial();
     assert!(best.is_ok());
 }
+
+// =============================================================================
+// Tests for suggest_bool
+// =============================================================================
+
+#[test]
+fn test_suggest_bool_returns_boolean() {
+    let mut trial = Trial::new(0);
+
+    let flag = trial.suggest_bool("use_feature").unwrap();
+    assert!(flag == true || flag == false);
+}
+
+#[test]
+fn test_suggest_bool_caching() {
+    let mut trial = Trial::new(0);
+
+    let b1 = trial.suggest_bool("flag").unwrap();
+    let b2 = trial.suggest_bool("flag").unwrap();
+
+    assert_eq!(b1, b2, "repeated suggest_bool should return cached value");
+}
+
+#[test]
+fn test_suggest_bool_multiple_parameters() {
+    let mut trial = Trial::new(0);
+
+    let a = trial.suggest_bool("use_dropout").unwrap();
+    let b = trial.suggest_bool("use_batchnorm").unwrap();
+    let c = trial.suggest_bool("use_skip_connections").unwrap();
+
+    // All should be cached independently
+    assert_eq!(a, trial.suggest_bool("use_dropout").unwrap());
+    assert_eq!(b, trial.suggest_bool("use_batchnorm").unwrap());
+    assert_eq!(c, trial.suggest_bool("use_skip_connections").unwrap());
+}
+
+#[test]
+fn test_suggest_bool_in_optimization() {
+    let study: Study<f64> = Study::new(Direction::Minimize);
+
+    study
+        .optimize(10, |trial| {
+            let use_feature = trial.suggest_bool("use_feature")?;
+            let x = trial.suggest_float("x", 0.0, 10.0)?;
+
+            // Objective depends on boolean flag
+            let value = if use_feature { x } else { x * 2.0 };
+            Ok::<_, Error>(value)
+        })
+        .unwrap();
+
+    assert_eq!(study.n_trials(), 10);
+}
+
+#[test]
+fn test_suggest_bool_with_tpe() {
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
+
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    study
+        .optimize_with_sampler(20, |trial| {
+            let use_large = trial.suggest_bool("use_large")?;
+            let base = if use_large { 10.0 } else { 1.0 };
+            let x = trial.suggest_float("x", 0.0, base)?;
+            Ok::<_, Error>(x)
+        })
+        .unwrap();
+
+    let best = study.best_trial().unwrap();
+    // Best should prefer use_large=false for smaller range
+    assert!(best.value < 5.0);
+}
+
+// =============================================================================
+// Tests for suggest_range
+// =============================================================================
+
+#[test]
+fn test_suggest_range_float_exclusive() {
+    let mut trial = Trial::new(0);
+
+    let x = trial.suggest_range("x", 0.0..1.0).unwrap();
+    assert!(x >= 0.0 && x <= 1.0, "value {x} out of range 0.0..1.0");
+}
+
+#[test]
+fn test_suggest_range_float_inclusive() {
+    let mut trial = Trial::new(0);
+
+    let x = trial.suggest_range("x", 0.0..=1.0).unwrap();
+    assert!(x >= 0.0 && x <= 1.0, "value {x} out of range 0.0..=1.0");
+}
+
+#[test]
+fn test_suggest_range_int_exclusive() {
+    let mut trial = Trial::new(0);
+
+    // 1..10 means 1 to 9 inclusive
+    let n = trial.suggest_range("n", 1_i64..10).unwrap();
+    assert!(n >= 1 && n <= 9, "value {n} out of range 1..10 (should be 1-9)");
+}
+
+#[test]
+fn test_suggest_range_int_inclusive() {
+    let mut trial = Trial::new(0);
+
+    // 1..=10 means 1 to 10 inclusive
+    let n = trial.suggest_range("n", 1_i64..=10).unwrap();
+    assert!(
+        n >= 1 && n <= 10,
+        "value {n} out of range 1..=10 (should be 1-10)"
+    );
+}
+
+#[test]
+fn test_suggest_range_caching_float() {
+    let mut trial = Trial::new(0);
+
+    let x1 = trial.suggest_range("x", 0.0..1.0).unwrap();
+    let x2 = trial.suggest_range("x", 0.0..1.0).unwrap();
+
+    assert_eq!(x1, x2, "repeated suggest_range should return cached value");
+}
+
+#[test]
+fn test_suggest_range_caching_int() {
+    let mut trial = Trial::new(0);
+
+    let n1 = trial.suggest_range("n", 1_i64..=100).unwrap();
+    let n2 = trial.suggest_range("n", 1_i64..=100).unwrap();
+
+    assert_eq!(n1, n2, "repeated suggest_range should return cached value");
+}
+
+#[test]
+fn test_suggest_range_multiple_parameters() {
+    let mut trial = Trial::new(0);
+
+    let x = trial.suggest_range("x", 0.0..1.0).unwrap();
+    let y = trial.suggest_range("y", -5.0..=5.0).unwrap();
+    let n = trial.suggest_range("n", 1_i64..10).unwrap();
+    let m = trial.suggest_range("m", 100_i64..=200).unwrap();
+
+    // All should be cached independently
+    assert_eq!(x, trial.suggest_range("x", 0.0..1.0).unwrap());
+    assert_eq!(y, trial.suggest_range("y", -5.0..=5.0).unwrap());
+    assert_eq!(n, trial.suggest_range("n", 1_i64..10).unwrap());
+    assert_eq!(m, trial.suggest_range("m", 100_i64..=200).unwrap());
+}
+
+#[test]
+fn test_suggest_range_in_optimization() {
+    let study: Study<f64> = Study::new(Direction::Minimize);
+
+    study
+        .optimize(10, |trial| {
+            let x = trial.suggest_range("x", -10.0..10.0)?;
+            let n = trial.suggest_range("n", 1_i64..=5)?;
+            Ok::<_, Error>(x * x + n as f64)
+        })
+        .unwrap();
+
+    assert_eq!(study.n_trials(), 10);
+}
+
+#[test]
+fn test_suggest_range_with_tpe() {
+    let sampler = TpeSampler::builder()
+        .seed(42)
+        .n_startup_trials(5)
+        .build()
+        .unwrap();
+
+    let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
+
+    study
+        .optimize_with_sampler(30, |trial| {
+            let x = trial.suggest_range("x", -5.0..=5.0)?;
+            let n = trial.suggest_range("n", 1_i64..=10)?;
+            Ok::<_, Error>(x * x + (n as f64 - 5.0).powi(2))
+        })
+        .unwrap();
+
+    let best = study.best_trial().unwrap();
+    // TPE should find near-optimal solution
+    assert!(best.value < 10.0, "TPE should find good solution");
+}
+
+#[test]
+fn test_suggest_range_empty_int_range_error() {
+    let mut trial = Trial::new(0);
+
+    // 5..5 is empty (no valid integers)
+    let result = trial.suggest_range("n", 5_i64..5);
+    assert!(
+        matches!(result, Err(Error::InvalidBounds { .. })),
+        "empty range should return InvalidBounds error"
+    );
+}
+
+#[test]
+fn test_suggest_range_single_value_int() {
+    let mut trial = Trial::new(0);
+
+    // 5..=5 has exactly one value: 5
+    let n = trial.suggest_range("n", 5_i64..=5).unwrap();
+    assert_eq!(n, 5, "single-value range should return that value");
+}
+
+#[test]
+fn test_suggest_range_single_value_float() {
+    let mut trial = Trial::new(0);
+
+    let x = trial.suggest_range("x", 3.14..=3.14).unwrap();
+    assert!(
+        (x - 3.14).abs() < f64::EPSILON,
+        "single-value range should return that value"
+    );
+}