Files
optimiz-rs/src/functional.rs
T
Melvin Avarez 79f51e4775 Release v0.2.0: Comprehensive DE, Mathematical Toolkit, Optimal Control
Major Features:
• Comprehensive Differential Evolution with 5 strategies (rand1, best1, currenttobest1, rand2, best2)
• Adaptive jDE algorithm for self-tuning F and CR parameters
• Convergence tracking with history records and early stopping
• Mathematical toolkit module (780 lines): gradient, hessian, jacobian, statistics, linear algebra
• Optimal control framework: HJB solvers, regime switching, jump diffusion, MRSJD
• Sparse optimization: Sparse PCA, Box-Tao decomposition, ADMM, Elastic Net
• Rayon parallelization infrastructure (ready for pure Rust objectives)

Performance:
• 74-88× speedup for DE vs SciPy
• 50-100× speedup overall vs pure Python

Refactoring & Cleanup:
• Removed 5 legacy files (de_refactored.rs, hmm_legacy.rs, hmm_refactored.rs, mcmc_legacy.rs, mcmc_refactored.rs)
• Modular architecture with trait-based design
• Generic implementations (no domain-specific code)
• Updated Python bindings for new DE API
• Fixed ALL compilation warnings (0 errors, 0 warnings)

Documentation:
• Updated README with v0.2.0 features and benchmarks
• Created RELEASE_NOTES_v0.2.0.md (comprehensive changelog)
• New optimal control tutorial notebook (03_optimal_control_tutorial.ipynb)
• Updated API examples in README
• Created test_release.py for release validation

Version Bumps:
• Cargo.toml: 0.1.0 → 0.2.0
• pyproject.toml: 0.1.0 → 0.2.0
• python/__init__.py: 0.1.0 → 0.2.0

Breaking Changes:
• DE API: mutation_factor/crossover_rate → f/cr
• DE API: use_adaptive_jde → adaptive
• DE API: strategy names simplified (e.g., 'rand/1/bin' → 'rand1')
• DE returns: (x, fun) tuple instead of dict-like object

Known Items (Post-Release):
• Mathematical toolkit functions available in Rust but not yet exposed to Python
• MCMC Python wrapper needs API update to match new Rust implementation
• Tutorial notebooks need DE API updates

Tests: 34 Rust tests passing, core Python functionality validated with test_release.py
2025-12-10 18:54:32 +01:00

204 lines
4.3 KiB
Rust

//! Trait-based functional utilities for OptimizR
//!
//! This module provides functional programming utilities like composition,
//! monadic operations, and higher-order functions.
use crate::core::{OptimizrError, Result};
/// Function composition trait
pub trait Compose<A, B, C>: Sized {
fn compose<G>(self, g: G) -> impl Fn(A) -> C
where
G: Fn(B) -> C,
Self: Fn(A) -> B;
}
impl<F, A, B, C> Compose<A, B, C> for F
where
F: Fn(A) -> B,
{
fn compose<G>(self, g: G) -> impl Fn(A) -> C
where
G: Fn(B) -> C,
{
move |x| g(self(x))
}
}
/// Monadic operations for Result
pub trait ResultExt<T> {
/// Apply a function if Ok, short-circuit on Err
fn and_then_log<F, U>(self, f: F, msg: &str) -> Result<U>
where
F: FnOnce(T) -> Result<U>;
/// Map with context
fn map_context<F, U>(self, f: F, ctx: &str) -> Result<U>
where
F: FnOnce(T) -> U;
}
impl<T> ResultExt<T> for Result<T> {
fn and_then_log<F, U>(self, f: F, msg: &str) -> Result<U>
where
F: FnOnce(T) -> Result<U>,
{
match self {
Ok(val) => f(val),
Err(e) => {
eprintln!("Error at {}: {:?}", msg, e);
Err(e)
}
}
}
fn map_context<F, U>(self, f: F, ctx: &str) -> Result<U>
where
F: FnOnce(T) -> U,
{
self.map(f)
.map_err(|e| OptimizrError::ComputationError(format!("{}: {}", ctx, e)))
}
}
/// Retry logic for operations
pub fn retry<F, T>(mut f: F, max_attempts: usize) -> Result<T>
where
F: FnMut() -> Result<T>,
{
let mut last_error = None;
for _ in 0..max_attempts {
match f() {
Ok(val) => return Ok(val),
Err(e) => last_error = Some(e),
}
}
Err(last_error.unwrap_or_else(|| {
OptimizrError::ComputationError("All retry attempts failed".to_string())
}))
}
/// Memoization for expensive computations
pub struct Memoized<F, T>
where
F: Fn(&[f64]) -> T,
{
f: F,
cache: std::sync::Mutex<std::collections::HashMap<Vec<ordered_float::OrderedFloat<f64>>, T>>,
}
impl<F, T> Memoized<F, T>
where
F: Fn(&[f64]) -> T,
T: Clone,
{
pub fn new(f: F) -> Self {
Self {
f,
cache: std::sync::Mutex::new(std::collections::HashMap::new()),
}
}
pub fn call(&self, x: &[f64]) -> T {
let key: Vec<_> = x.iter().map(|&v| ordered_float::OrderedFloat(v)).collect();
let mut cache = self.cache.lock().unwrap();
if let Some(cached) = cache.get(&key) {
return cached.clone();
}
let result = (self.f)(x);
cache.insert(key, result.clone());
result
}
}
/// Lazy evaluation wrapper
pub struct Lazy<T, F>
where
F: FnOnce() -> T,
{
f: Option<F>,
value: Option<T>,
}
impl<T, F> Lazy<T, F>
where
F: FnOnce() -> T,
{
pub fn new(f: F) -> Self {
Self {
f: Some(f),
value: None,
}
}
pub fn force(&mut self) -> &T {
if self.value.is_none() {
let f = self.f.take().unwrap();
self.value = Some(f());
}
self.value.as_ref().unwrap()
}
}
/// Piping operator - allows chaining operations
pub trait Pipe: Sized {
fn pipe<F, R>(self, f: F) -> R
where
F: FnOnce(Self) -> R,
{
f(self)
}
}
impl<T> Pipe for T {}
/// Currying utilities
/// Note: Simplified version due to Rust's ownership constraints
/// For full currying, use the partial function instead
pub fn curry2<A, B, R, F>(f: F) -> impl Fn((A, B)) -> R
where
F: Fn(A, B) -> R + 'static,
A: 'static,
B: 'static,
R: 'static,
{
move |(a, b)| f(a, b)
}
/// Partial application
pub fn partial<A: Clone + 'static, B, R, F>(f: F, a: A) -> impl Fn(B) -> R
where
F: Fn(A, B) -> R + 'static,
B: 'static,
R: 'static,
{
move |b| f(a.clone(), b)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_pipe() {
let result = vec![1, 2, 3]
.pipe(|v| v.into_iter().map(|x| x * 2).collect::<Vec<_>>())
.pipe(|v: Vec<_>| v.into_iter().sum::<i32>());
assert_eq!(result, 12);
}
#[test]
fn test_partial() {
let add = |a: i32, b: i32| a + b;
let add5 = partial(add, 5);
assert_eq!(add5(3), 8);
}
}