From b10263b4f28fd91a10005e2d073bba66ce825e2d Mon Sep 17 00:00:00 2001 From: ThotDjehuty Date: Tue, 17 Feb 2026 10:09:15 +0100 Subject: [PATCH] docs: Rebrand OptimizR to Optimiz-rs throughout documentation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Updated Branding in ReadTheDocs: - ✅ All 'OptimizR' → 'Optimiz-rs' (17 files) - ✅ Project name in conf.py - ✅ HTML title and short title - ✅ All algorithm documentation - ✅ Getting started guide - ✅ Installation guide - ✅ Theory/mathematical foundations - ✅ Archive documentation Documentation now consistently uses the new 'optimiz-rs' branding that matches both PyPI and crates.io package names. Note: Python module name 'optimizr' in import statements intentionally unchanged (that's the actual module name). --- docs/archive/COMPLETION_SUMMARY.md | 4 +-- docs/archive/DEVELOPMENT.md | 2 +- docs/archive/ENHANCEMENT_STRATEGY.md | 28 +++++++++---------- docs/archive/ENHANCEMENT_SUITE_COMPLETE.md | 18 ++++++------ docs/archive/NOTEBOOK_AUDIT_REPORT.md | 10 +++---- docs/archive/PROJECT_SUMMARY.md | 12 ++++---- docs/archive/REFACTORING.md | 6 ++-- docs/archive/SETUP_COMPLETE.md | 6 ++-- .../TIMESERIES_HELPERS_IMPLEMENTATION.md | 6 ++-- .../algorithms/differential_evolution.md | 2 +- docs/source/algorithms/mcmc.md | 2 +- docs/source/conf.py | 6 ++-- docs/source/examples.md | 2 +- docs/source/getting-started.md | 4 +-- docs/source/index.rst | 6 ++-- docs/source/installation.md | 2 +- .../source/theory/mathematical_foundations.md | 10 +++---- 17 files changed, 63 insertions(+), 63 deletions(-) diff --git a/docs/archive/COMPLETION_SUMMARY.md b/docs/archive/COMPLETION_SUMMARY.md index fffed7e..a9f6aa0 100644 --- a/docs/archive/COMPLETION_SUMMARY.md +++ b/docs/archive/COMPLETION_SUMMARY.md @@ -1,4 +1,4 @@ -# OptimizR Refactoring - Completion Report +# Optimiz-rs Refactoring - Completion Report ## ✅ All Tasks Completed @@ -263,7 +263,7 @@ pyproject.toml # ✅ Unchanged ✅ **All todo items completed successfully!** -The OptimizR codebase has been completely refactored with: +The Optimiz-rs codebase has been completely refactored with: - ✅ Modular trait-based architecture - ✅ Functional programming patterns - ✅ Advanced design patterns (Strategy, Builder, Traits) diff --git a/docs/archive/DEVELOPMENT.md b/docs/archive/DEVELOPMENT.md index 7cf937d..92a02c3 100644 --- a/docs/archive/DEVELOPMENT.md +++ b/docs/archive/DEVELOPMENT.md @@ -1,4 +1,4 @@ -# OptimizR Development Guide +# Optimiz-rs Development Guide ## Quick Start diff --git a/docs/archive/ENHANCEMENT_STRATEGY.md b/docs/archive/ENHANCEMENT_STRATEGY.md index f6d0d2b..40bb061 100644 --- a/docs/archive/ENHANCEMENT_STRATEGY.md +++ b/docs/archive/ENHANCEMENT_STRATEGY.md @@ -1,4 +1,4 @@ -# OptimizR Enhancement Strategy +# Optimiz-rs Enhancement Strategy **Date**: January 2, 2025 **Context**: Post-Polarway Phase 4, exploring integration and improvements @@ -58,7 +58,7 @@ - Simulated Annealing - Ant Colony Optimization -## Synergy Opportunities: Polarway + OptimizR +## Synergy Opportunities: Polarway + Optimiz-rs ### 1. Time-Series Feature Engineering for HMM **Description**: Use Polarway's time-series operations to create features for regime detection @@ -70,7 +70,7 @@ df = client.lag(['price'], periods=1) # Lagged prices df = client.pct_change(['price'], periods=1) # Returns df = client.diff(['price'], periods=1) # Price changes -# OptimizR: Regime detection on features +# Optimiz-rs: Regime detection on features returns = df['price_pct_change'].to_numpy() hmm = HMM(n_states=3) # Bull, Bear, Sideways hmm.fit(returns, n_iterations=100) @@ -79,7 +79,7 @@ states = hmm.predict(returns) **Value**: - Polarway provides fast feature engineering (50-200× faster for large datasets) -- OptimizR provides statistical inference (HMM regime detection) +- Optimiz-rs provides statistical inference (HMM regime detection) - Combined: Real-time regime switching for trading strategies ### 2. Risk Metrics on Time-Series Data @@ -91,14 +91,14 @@ states = hmm.predict(returns) df = client.pct_change(['price'], periods=1) returns = df['price_pct_change'].to_numpy() -# OptimizR: Risk analysis +# Optimiz-rs: Risk analysis hurst = compute_hurst_exponent(returns) # Mean-reversion detection half_life = estimate_half_life(returns) # Reversion time risk_metrics = compute_risk_metrics(returns) # Comprehensive suite ``` **Value**: -- Fast preprocessing (Polarway) + sophisticated analysis (OptimizR) +- Fast preprocessing (Polarway) + sophisticated analysis (Optimiz-rs) - Useful for pairs trading, mean-reversion strategies - Real-time risk monitoring @@ -111,7 +111,7 @@ risk_metrics = compute_risk_metrics(returns) # Comprehensive suite df = client.lag(['spy_price', 'vix'], periods=[1, 5, 20]) df = client.pct_change(['spy_price'], periods=1) -# OptimizR: Solve optimal control problem +# Optimiz-rs: Solve optimal control problem # State: [price, volatility regime] # Control: portfolio weights value_fn = solve_hjb_regime_switching(...) @@ -133,7 +133,7 @@ def backtest_strategy(params): # ... strategy logic ... return -sharpe_ratio # Minimize negative Sharpe -# OptimizR: Find optimal parameters +# Optimiz-rs: Find optimal parameters result = differential_evolution( objective_fn=backtest_strategy, bounds=[(1, 50), (0.01, 0.5)], # [lag_period, threshold] @@ -144,7 +144,7 @@ result = differential_evolution( **Value**: - Polarway handles heavy data processing -- OptimizR finds optimal parameters +- Optimiz-rs finds optimal parameters - 74-88× faster than SciPy DE ## High-Priority Enhancements @@ -258,12 +258,12 @@ impl SHADEMemory { ### Priority 3: Time-Series Integration Helpers -**Problem**: Using Polarway + OptimizR requires manual glue code +**Problem**: Using Polarway + Optimiz-rs requires manual glue code **Solution**: Create helper functions for common time-series + optimization patterns **Implementation Strategy**: -1. Add `timeseries_utils` module to OptimizR +1. Add `timeseries_utils` module to Optimiz-rs 2. Functions for common workflows 3. Optional Polarway integration (via feature flag) @@ -350,7 +350,7 @@ result = tsu.optimize_strategy_params( 1. **Session 1 (Current)**: Time-Series Integration Helpers (1-2 hours) - Low effort, immediate value - - Makes Polarway + OptimizR integration obvious + - Makes Polarway + Optimiz-rs integration obvious - Creates examples for documentation 2. **Session 2**: Enable Rust-Native Parallelization (1-2 hours) @@ -372,7 +372,7 @@ result = tsu.optimize_strategy_params( For each enhancement: 1. **Unit tests**: Algorithm correctness (sphere function, Rosenbrock) 2. **Benchmarks**: Performance comparison (before/after) -3. **Integration tests**: Polarway + OptimizR workflows +3. **Integration tests**: Polarway + Optimiz-rs workflows 4. **Documentation**: Usage examples, API docs ## Git Commit Strategy (per MANDATORY rules) @@ -404,4 +404,4 @@ Each enhancement gets: --- -**Next Action**: Implement Priority 3 (Time-Series Integration Helpers) as it's lowest effort with immediate value for demonstrating Polarway + OptimizR synergy. +**Next Action**: Implement Priority 3 (Time-Series Integration Helpers) as it's lowest effort with immediate value for demonstrating Polarway + Optimiz-rs synergy. diff --git a/docs/archive/ENHANCEMENT_SUITE_COMPLETE.md b/docs/archive/ENHANCEMENT_SUITE_COMPLETE.md index fdcc7c0..2b638b5 100644 --- a/docs/archive/ENHANCEMENT_SUITE_COMPLETE.md +++ b/docs/archive/ENHANCEMENT_SUITE_COMPLETE.md @@ -1,4 +1,4 @@ -# OptimizR Enhancement Suite - Implementation Complete +# Optimiz-rs Enhancement Suite - Implementation Complete **Date**: January 2, 2026 **Session Duration**: ~3 hours @@ -8,13 +8,13 @@ ## Overview -Completed comprehensive enhancement suite for OptimizR v0.2.0, implementing all 3 priorities from the Enhancement Strategy: +Completed comprehensive enhancement suite for Optimiz-rs v0.2.0, implementing all 3 priorities from the Enhancement Strategy: 1. ✅ **Time-Series Integration Helpers** (Priority 3) 2. ✅ **Rust Parallelization** (Priority 2) 3. ✅ **SHADE Algorithm** (Priority 1) -Additionally created integration examples combining Polarway + OptimizR workflows. +Additionally created integration examples combining Polarway + Optimiz-rs workflows. --- @@ -22,7 +22,7 @@ Additionally created integration examples combining Polarway + OptimizR workflow ### 1. Time-Series Integration Helpers (Commit: 9a8032e, 7f77f29) -**Purpose**: Bridge OptimizR's optimization with time-series analysis for financial workflows. +**Purpose**: Bridge Optimiz-rs's optimization with time-series analysis for financial workflows. **Implementation**: - Created `src/timeseries_utils.rs` (400+ lines) @@ -41,7 +41,7 @@ Additionally created integration examples combining Polarway + OptimizR workflow - All functions tested and working **Impact**: -- Enables Polarway → OptimizR workflows +- Enables Polarway → Optimiz-rs workflows - Simplifies regime detection with HMM - Streamlines pairs trading analysis @@ -130,7 +130,7 @@ Additionally created integration examples combining Polarway + OptimizR workflow ### 4. Integration Examples (Included with parallelization) -**Purpose**: Demonstrate Polarway + OptimizR workflows. +**Purpose**: Demonstrate Polarway + Optimiz-rs workflows. **Implementation**: - `examples/polarway_optimizr_integration.py` (500+ lines) @@ -148,7 +148,7 @@ Additionally created integration examples combining Polarway + OptimizR workflow **Impact**: - End-to-end examples for financial analysis -- Demonstrates Polarway + OptimizR synergy +- Demonstrates Polarway + Optimiz-rs synergy - Ready for production adaptation **Files**: @@ -221,7 +221,7 @@ All commits pushed to origin/main ✅ ## 🎯 Alignment with Roadmap -All enhancements align with OptimizR v0.3.0 roadmap: +All enhancements align with Optimiz-rs v0.3.0 roadmap: - ✅ **Time-series integration**: Enable Polarway workflows - ✅ **Parallelization**: Unlock Rayon infrastructure @@ -325,4 +325,4 @@ Future (v0.3.0+): **Status**: ✅ **ALL OBJECTIVES COMPLETE** **Next**: Integrate SHADE into DE, performance testing -**Version**: OptimizR v0.2.0 → v0.3.0 prep +**Version**: Optimiz-rs v0.2.0 → v0.3.0 prep diff --git a/docs/archive/NOTEBOOK_AUDIT_REPORT.md b/docs/archive/NOTEBOOK_AUDIT_REPORT.md index 388769e..70dae4f 100644 --- a/docs/archive/NOTEBOOK_AUDIT_REPORT.md +++ b/docs/archive/NOTEBOOK_AUDIT_REPORT.md @@ -9,7 +9,7 @@ Example notebooks in `examples/notebooks/` **ARE WORKING CORRECTLY**! They use P - Automatic Rust backend when available - Graceful fallback to pure Python -## Actual OptimizR Python API (from lib.rs) +## Actual Optimiz-rs Python API (from lib.rs) ### ✅ Available Functions/Classes: @@ -155,7 +155,7 @@ from optimizr import ( ``` **Features Demonstrated:** -- Direct comparison: OptimizR (Rust) vs Python libraries +- Direct comparison: Optimiz-rs (Rust) vs Python libraries - Benchmarks against: hmmlearn, scipy, sklearn - Performance metrics and speedup calculations @@ -178,7 +178,7 @@ from optimizr import ( ### Python Wrapper Design (Brilliant!) -OptimizR uses a **two-layer architecture**: +Optimiz-rs uses a **two-layer architecture**: 1. **Rust Core** (`src/` with PyO3): - `HMMParams` class @@ -237,7 +237,7 @@ This design is **excellent** because: ## Testing Summary -| Notebook | Status | OptimizR Features | Test Result | +| Notebook | Status | Optimiz-rs Features | Test Result | |----------|--------|-------------------|-------------| | 01_hmm_tutorial.ipynb | ✅ PASS | HMM (Rust) | All cells run | | 02_mcmc_tutorial.ipynb | ✅ PASS | mcmc_sample | Imports OK | @@ -277,7 +277,7 @@ This design is **excellent** because: **Actual Status:** Notebooks use Python wrappers correctly **What I Learned:** -1. OptimizR has excellent two-layer design +1. Optimiz-rs has excellent two-layer design 2. Python wrappers provide familiar OOP interface 3. Rust acceleration is transparent to users 4. Only 1 minor fix needed (random_state parameter) diff --git a/docs/archive/PROJECT_SUMMARY.md b/docs/archive/PROJECT_SUMMARY.md index 26d1b64..aa39279 100644 --- a/docs/archive/PROJECT_SUMMARY.md +++ b/docs/archive/PROJECT_SUMMARY.md @@ -1,8 +1,8 @@ -# OptimizR Project Summary +# Optimiz-rs Project Summary -## What is OptimizR? +## What is Optimiz-rs? -OptimizR is a **general-purpose optimization library** that provides high-performance implementations of advanced algorithms in Rust with easy-to-use Python bindings. It's designed to be fast, reliable, and production-ready for open-source distribution. +Optimiz-rs is a **general-purpose optimization library** that provides high-performance implementations of advanced algorithms in Rust with easy-to-use Python bindings. It's designed to be fast, reliable, and production-ready for open-source distribution. ## Key Features @@ -140,7 +140,7 @@ x_opt, f_min = differential_evolution( ## Differences from rust-hft-arbitrage-lab -| Aspect | rust-hft-arbitrage-lab | OptimizR | +| Aspect | rust-hft-arbitrage-lab | Optimiz-rs | |--------|----------------------|----------| | **Purpose** | HFT trading strategies | General optimization library | | **Scope** | Trading-specific | Domain-agnostic | @@ -165,7 +165,7 @@ x_opt, f_min = differential_evolution( ```bash git init git add . - git commit -m "Initial commit: OptimizR v0.1.0" + git commit -m "Initial commit: Optimiz-rs v0.1.0" git remote add origin https://github.com/ThotDjehuty/optimiz-r.git git push -u origin main ``` @@ -217,7 +217,7 @@ Based on benchmarks from rust-hft-arbitrage-lab: ## Marketing/Outreach 1. **Reddit**: r/rust, r/python, r/MachineLearning -2. **Hacker News**: "Show HN: OptimizR - Fast optimization algorithms in Rust" +2. **Hacker News**: "Show HN: Optimiz-rs - Fast optimization algorithms in Rust" 3. **Twitter/X**: Tweet with #rustlang #python 4. **PyPI**: Ensure good package description 5. **GitHub Topics**: optimization, rust, python, scientific-computing diff --git a/docs/archive/REFACTORING.md b/docs/archive/REFACTORING.md index 93d2fea..c80bc72 100644 --- a/docs/archive/REFACTORING.md +++ b/docs/archive/REFACTORING.md @@ -1,8 +1,8 @@ -# OptimizR Refactoring Summary +# Optimiz-rs Refactoring Summary ## Overview -This document summarizes the major refactoring applied to OptimizR to improve modularity, introduce functional programming patterns, implement design patterns, and add concurrency support. +This document summarizes the major refactoring applied to Optimiz-rs to improve modularity, introduce functional programming patterns, implement design patterns, and add concurrency support. ## Architecture Changes @@ -369,7 +369,7 @@ impl ProposalStrategy for MyProposal { ## Conclusion -This refactoring significantly improves OptimizR's: +This refactoring significantly improves Optimiz-rs's: - **Modularity**: Clear trait boundaries, easy to extend - **Maintainability**: Builder patterns, functional utilities reduce boilerplate - **Performance**: Parallel execution, memoization, lazy evaluation diff --git a/docs/archive/SETUP_COMPLETE.md b/docs/archive/SETUP_COMPLETE.md index d201bd3..2a2f0ad 100644 --- a/docs/archive/SETUP_COMPLETE.md +++ b/docs/archive/SETUP_COMPLETE.md @@ -1,4 +1,4 @@ -# OptimizR Setup Complete! ✅ +# Optimiz-rs Setup Complete! ✅ ## What Was Done @@ -136,7 +136,7 @@ pytest tests/ -v -k HMM # Run HMM tests only ## Performance -OptimizR provides **50-100x speedup** over pure Python for: +Optimiz-rs provides **50-100x speedup** over pure Python for: - HMM fitting (71x faster) - MCMC sampling (71x faster) - Differential Evolution (53x faster) @@ -144,7 +144,7 @@ OptimizR provides **50-100x speedup** over pure Python for: ## Summary -The OptimizR project is now **fully functional** with: +The Optimiz-rs project is now **fully functional** with: - ✅ Zero compilation errors - ✅ All tests passing - ✅ Docker support diff --git a/docs/archive/TIMESERIES_HELPERS_IMPLEMENTATION.md b/docs/archive/TIMESERIES_HELPERS_IMPLEMENTATION.md index bf82309..5082d8e 100644 --- a/docs/archive/TIMESERIES_HELPERS_IMPLEMENTATION.md +++ b/docs/archive/TIMESERIES_HELPERS_IMPLEMENTATION.md @@ -1,7 +1,7 @@ # Time-Series Integration Helpers Implementation Summary ## Overview -Completed Priority 3 from Enhancement Strategy: Time-series integration helpers for OptimizR v0.3.0. These 6 helper functions bridge OptimizR's optimization capabilities with time-series analysis, particularly useful for regime-switching models and pairs trading strategies. +Completed Priority 3 from Enhancement Strategy: Time-series integration helpers for Optimiz-rs v0.3.0. These 6 helper functions bridge Optimiz-rs's optimization capabilities with time-series analysis, particularly useful for regime-switching models and pairs trading strategies. ## Implementation Details @@ -15,7 +15,7 @@ Completed Priority 3 from Enhancement Strategy: Time-series integration helpers - Volatility proxy: squared returns - Lagged returns for each lag period - Returns: Feature matrix (N-max_lag rows × (3 + num_lags) columns) - - Use case: Prepare price data for OptimizR's HMM regime detection + - Use case: Prepare price data for Optimiz-rs's HMM regime detection 2. **`rolling_hurst_exponent(returns: &[f64], window_size: usize) -> Vec`** - Purpose: Detect mean-reversion vs trending behavior @@ -146,7 +146,7 @@ import optimizr prices = [100.0, 101.5, 99.8, 102.3, 103.7] features = optimizr.prepare_for_hmm_py(prices, [1, 2]) -# Use with OptimizR's HMM for regime detection +# Use with Optimiz-rs's HMM for regime detection ``` ### Mean-Reversion Check diff --git a/docs/source/algorithms/differential_evolution.md b/docs/source/algorithms/differential_evolution.md index 39f7dac..5b8675e 100644 --- a/docs/source/algorithms/differential_evolution.md +++ b/docs/source/algorithms/differential_evolution.md @@ -303,7 +303,7 @@ print(f"Function evaluations: {result.nfev}") ## Adaptive Control (jDE) -OptimizR implements **jDE** (self-adaptive DE), where the parameters $F$ and $CR$ +Optimiz-rs implements **jDE** (self-adaptive DE), where the parameters $F$ and $CR$ evolve with the population: $$ diff --git a/docs/source/algorithms/mcmc.md b/docs/source/algorithms/mcmc.md index c4e53ba..59becdc 100644 --- a/docs/source/algorithms/mcmc.md +++ b/docs/source/algorithms/mcmc.md @@ -161,7 +161,7 @@ $$ This is symmetric, so Metropolis acceptance applies. -**This is what OptimizR implements.** +**This is what Optimiz-rs implements.** --- diff --git a/docs/source/conf.py b/docs/source/conf.py index 4a01a3d..70fa4e1 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -6,7 +6,7 @@ import sys sys.path.insert(0, os.path.abspath('../../python')) # -- Project information ----------------------------------------------------- -project = 'OptimizR' +project = 'Optimiz-rs' copyright = '2026, HFThot Research Lab' author = 'HFThot Research Lab' release = '0.3.0' @@ -32,8 +32,8 @@ exclude_patterns = [] # -- Options for HTML output ------------------------------------------------- html_theme = 'furo' # Modern, clean theme html_static_path = ['_static'] -html_title = 'OptimizR Documentation' -html_short_title = 'OptimizR' +html_title = 'Optimiz-rs Documentation' +html_short_title = 'Optimiz-rs' html_logo = 'logo_optimizrs.png' html_favicon = 'logo_optimizrs.png' diff --git a/docs/source/examples.md b/docs/source/examples.md index 601b14d..cc2b67c 100644 --- a/docs/source/examples.md +++ b/docs/source/examples.md @@ -1,6 +1,6 @@ # Examples -Practical snippets for every OptimizR component. +Practical snippets for every Optimiz-rs component. ## Differential Evolution (global optimization) diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index 0bb5787..c4c19f2 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -12,7 +12,7 @@ pip install -r docs/requirements.txt pip install maturin numpy ``` -## 2. Build and install OptimizR locally +## 2. Build and install Optimiz-rs locally ```bash pip install . @@ -26,7 +26,7 @@ maturin develop --release python - <<'PY' import optimizr from optimizr import differential_evolution, HMM -print("OptimizR version:", optimizr.__version__) +print("Optimiz-rs version:", optimizr.__version__) # Simple objective f = lambda x: sum(v * v for v in x) diff --git a/docs/source/index.rst b/docs/source/index.rst index 6a3595c..6230e1d 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,6 +1,6 @@ -.. OptimizR documentation master file +.. Optimiz-rs documentation master file -OptimizR Documentation +Optimiz-rs Documentation ====================== **High-performance optimization algorithms in Rust with Python bindings** @@ -13,7 +13,7 @@ OptimizR Documentation :target: https://github.com/ThotDjehuty/optimiz-r/blob/main/LICENSE :alt: License -OptimizR provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python through PyO3, it delivers **50-100× speedup** over pure Python implementations. +Optimiz-rs provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python through PyO3, it delivers **50-100× speedup** over pure Python implementations. .. toctree:: :maxdepth: 2 diff --git a/docs/source/installation.md b/docs/source/installation.md index e78d3d1..6b0671a 100644 --- a/docs/source/installation.md +++ b/docs/source/installation.md @@ -8,7 +8,7 @@ ## Install from PyPI -**Coming soon**: OptimizR will be available on PyPI. +**Coming soon**: Optimiz-rs will be available on PyPI. ```bash pip install optimizr diff --git a/docs/source/theory/mathematical_foundations.md b/docs/source/theory/mathematical_foundations.md index 611b8e8..0db5e5f 100644 --- a/docs/source/theory/mathematical_foundations.md +++ b/docs/source/theory/mathematical_foundations.md @@ -1,6 +1,6 @@ # Mathematical Foundations -This page collects the core equations driving OptimizR’s Rust kernels, plus short intuition blurbs and micro-checks you can run in a notebook. For visuals and full walkthroughs, see the example notebooks in `examples/notebooks/`. +This page collects the core equations driving Optimiz-rs’s Rust kernels, plus short intuition blurbs and micro-checks you can run in a notebook. For visuals and full walkthroughs, see the example notebooks in `examples/notebooks/`. ## Differential Evolution (DE) @@ -29,7 +29,7 @@ $$ \end{cases} $$ -**Self-adaptive jDE (used by OptimizR):** +**Self-adaptive jDE (used by Optimiz-rs):** $$ F_i^{g+1} = \begin{cases} F_{\min} + r_1 \cdot F_{\max} & r_2 < \tau_1,\\ @@ -52,7 +52,7 @@ $$ -\partial_t V(t,x) = \inf_{u\in\mathcal{U}} \Big[ \ell(x,u) + \nabla_x V(t,x)^{\top} b(x,u) + \tfrac12 \operatorname{Tr}\big(\sigma\sigma^{\top}(x,u) \, \nabla_x^2 V(t,x)\big) \Big],\quad V(T,x) = g(x). $$ -OptimizR uses finite differences with backward time-stepping and optional policy iteration. On a uniform grid $(t_n, x_j)$: +Optimiz-rs uses finite differences with backward time-stepping and optional policy iteration. On a uniform grid $(t_n, x_j)$: $$ V^{n} = \min_{u}\Big\{ \ell(x_j,u)\,\Delta t + V^{n+1} + \nabla_x V^{n+1}\cdot b\,\Delta t + \tfrac12 \operatorname{Tr}(\sigma\sigma^{\top}\nabla_x^2 V^{n+1})\,\Delta t \Big\}. $$ @@ -62,7 +62,7 @@ The control that attains the minimum yields the feedback policy $u^{\star}(x_j, ## Mean Field Games (1D solver) -OptimizR’s MFG module solves the coupled system for value $u$ and density $m$: +Optimiz-rs’s MFG module solves the coupled system for value $u$ and density $m$: $$ \begin{aligned} -\partial_t u(t,x) - \nu\,\partial_{xx} u(t,x) + H\big(x,\partial_x u(t,x), m(t,x)\big) &= 0,\\ @@ -100,7 +100,7 @@ For target density $\pi(x)$ and proposal $q(x'\mid x)$: $$ \alpha(x \to x') = \min\Big(1, \frac{\pi(x')\, q(x \mid x')}{\pi(x)\, q(x' \mid x)}\Big). $$ -OptimizR uses symmetric Gaussian proposals (so $q$ cancels) by default, with optional bounds projection and burn-in. +Optimiz-rs uses symmetric Gaussian proposals (so $q$ cancels) by default, with optional bounds projection and burn-in. **Heuristic:** Tune proposal std so acceptance is ~0.25–0.35 for moderate dimensions; see `examples/notebooks/02_mcmc.ipynb` for trace plots.