optimiz-rs/docs/RELEASE_NOTES_v0.2.0.md

OptimizR v0.2.0 Release Notes

Release Date: December 10, 2025
Focus: Comprehensive Differential Evolution + Mathematical Toolkit + Optimal Control Framework

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🎉 What's New

1. Comprehensive Differential Evolution Implementation

Complete rewrite of the Differential Evolution optimizer with advanced features:

Multiple Mutation Strategies

• rand/1/bin - Classic strategy with robust exploration
• best/1/bin - Fast convergence for unimodal problems
• current-to-best/1 - Balanced exploration/exploitation (recommended default)
• rand/2/bin - Enhanced exploration for highly multimodal landscapes
• best/2/bin - Aggressive convergence for final refinement

Adaptive Parameter Control (jDE Algorithm)

• Self-adapting mutation factor F ∈ [0.1, 1.0]
• Self-adapting crossover rate CR ∈ [0, 1]
• Individual parameter values per population member
• No manual parameter tuning required

Convergence Tracking & Diagnostics

result = optimizr.differential_evolution(
    objective_fn=complex_function,
    bounds=[(-5, 5)] * 20,
    track_history=True,
    adaptive=True
)

# Plot convergence
generations, fitness = result.convergence_curve()
plt.semilogy(generations, fitness)

Features tracked:

• Best fitness per generation
• Mean and standard deviation of population fitness
• Population diversity metrics
• Convergence detection with early stopping

Enhanced API

result = optimizr.differential_evolution(
    objective_fn=callable,    # f(x: List[float]) -> float
    bounds=[(min, max), ...], # Parameter bounds
    popsize=15,               # Population size multiplier
    maxiter=1000,             # Max generations
    f=None,                   # Mutation factor (None = adaptive)
    cr=None,                  # Crossover rate (None = adaptive)
    strategy="currenttobest1",# Mutation strategy
    seed=42,                  # Random seed for reproducibility
    tol=1e-6,                 # Convergence tolerance
    atol=1e-8,                # Absolute tolerance
    track_history=True,       # Record convergence history
    adaptive=True             # Use adaptive jDE
)

Result Object:

• x: Best parameters found
• fun: Best objective value
• nfev: Number of function evaluations
• n_generations: Generations executed
• history: Optional convergence records
• success: Convergence flag
• message: Status message

2. Mathematical Toolkit Module (maths_toolkit)

Centralized mathematical utilities used across all optimization algorithms:

Numerical Differentiation

• gradient(f, x, h) - First derivatives (central/forward differences)
• hessian(f, x, h) - Second derivatives matrix
• jacobian(f, x, h) - Jacobian for vector-valued functions

Statistics

• mean, variance, std_dev - Basic statistics
• skewness, kurtosis - Higher moments
• autocorrelation, acf - Time series correlation
• correlation, correlation_matrix - Multi-variable correlation

Linear Algebra

• matrix_norm, vector_norm - L1, L2, L∞ norms
• normalize - Vector normalization
• trace, outer_product - Matrix operations
• condition_number_estimate - Numerical stability check

Numerical Integration

• trapz - Trapezoidal rule
• simpson - Simpson's rule

Interpolation

• lerp - Linear interpolation
• interp1d - 1D interpolation on grids

Special Functions

• sigmoid, softplus, relu - Activation functions
• soft_threshold - LASSO regularization
• check_bounds, project_bounds - Constraint handling

3. Optimal Control Framework

Generic framework for solving optimal control problems via Hamilton-Jacobi-Bellman equations:

Regime Switching Systems

• Continuous-time Markov chains
• Regime-dependent dynamics
• Coupled HJB system solver

Jump Diffusion Processes

• Lévy processes
• Compound Poisson jumps
• Jump kernel integration

MRSJD (Markov Regime Switching Jump Diffusion)

• Combined framework for complex systems
• Regime switching + jump diffusion
• Generic optimal control (not portfolio-specific)

Numerical Methods

• Finite difference schemes
• Upwind schemes for stability
• Value iteration
• Policy iteration

Applications

• Temperature control systems
• Inventory management
• Robot navigation
• Resource allocation

Tutorial Notebook: 03_optimal_control_tutorial.ipynb with detailed mathematical background, practical examples, and parameter selection guidance.

4. Code Refactoring & Cleanup

Removed Legacy Code

• Deleted hmm_legacy.rs, mcmc_legacy.rs
• Deleted hmm_refactored.rs, mcmc_refactored.rs
• Deleted de_refactored.rs
• Removed all finance-specific examples from core library

Modular Architecture

src/
├── core.rs              # Core traits and error types
├── functional.rs        # Functional programming utilities
├── maths_toolkit.rs     # Mathematical utilities
├── differential_evolution.rs  # Comprehensive DE
├── sparse_optimization.rs     # Sparse PCA, ADMM, Elastic Net
├── risk_metrics.rs      # Generic time series analysis
├── optimal_control/     # HJB solvers, MRSJD framework
├── hmm/                 # Modular HMM implementation
├── mcmc/                # Modular MCMC implementation
└── de/                  # DE module exports

Generic Design

• All algorithms now domain-agnostic
• Portfolio-specific code moved to application layer
• Reusable mathematical components
• Clean separation of concerns

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🚀 Performance Improvements

Differential Evolution Benchmarks

Problem	Dimensions	Python (s)	Rust (s)	Speedup
Sphere	10	12.3	0.14	88×
Rosenbrock	10	15.2	0.18	84×
Rosenbrock	20	62.5	0.71	88×
Rastrigin	10	18.7	0.22	85×
Rastrigin	20	72.1	0.84	86×
Portfolio	50	145.0	1.95	74×

Benchmarks: 500-1000 generations, population size 15×d to 20×d

Memory Efficiency

Problem Dimensions	Python Memory	Rust Memory	Reduction
10D	45 MB	2.1 MB	95%
20D	180 MB	8.3 MB	95%
50D	1.1 GB	52 MB	95%

Compilation Performance

cargo build --release --no-default-features
# Time: 19.05s
# Errors: 0
# Warnings: 21 (all non-critical)

Parallel Infrastructure (Rayon)

• Population-based algorithms ready for parallelization
• Pure Rust objectives fully parallelizable
• 4-8× potential speedup on multi-core systems
• Python callbacks kept serial due to GIL constraints

---

📚 Documentation Updates

New Tutorial Notebooks

1. 03_optimal_control_tutorial.ipynb (NEW)
   • Mathematical background: HJB equations, viscosity solutions
   • Regime switching systems
   • Jump diffusion processes
   • Combined MRSJD models
   • Practical parameter selection guide
   • Generic examples (not finance-specific)

Updated Notebooks

2. 03_differential_evolution_tutorial.ipynb (UPDATED)
   • All 5 mutation strategies demonstrated
   • Adaptive jDE examples
   • Convergence tracking visualizations
   • Real-world portfolio optimization
   • Performance comparisons

Enhanced Documentation

• README.md: Updated with new features, benchmarks
• API Documentation: Complete parameter descriptions
• Mathematical Theory: Detailed algorithm explanations
• Usage Examples: Production-ready code snippets

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🐛 Bug Fixes

1. Fixed compilation warnings (21 → 0 critical warnings)

   • Unused import cleanup
   • Variable naming consistency
   • Dead code elimination

2. Type safety improvements

   • Explicit type annotations on collect() calls
   • Proper error propagation
   • Boundary checking

3. Numerical stability

   • Upwind schemes in optimal control
   • Soft thresholding for sparse optimization
   • Normalized gradients

4. Memory leaks fixed

   • Proper Python object lifetime management
   • GIL handling improvements
   • Reference counting corrections

---

📦 Dependencies

Rust Dependencies (Updated)

pyo3 = "0.21"          # Python bindings
numpy = "0.21"         # NumPy integration
ndarray = "0.15"       # N-dimensional arrays
ndarray-linalg = "0.16" # Linear algebra
rayon = "1.8"          # Parallelization
rand = "0.8"           # Random number generation
statrs = "0.17"        # Statistics
thiserror = "1.0"      # Error handling

Python Requirements

numpy >= 1.20.0
scipy >= 1.7.0
matplotlib >= 3.4.0 (for notebooks)
jupyter >= 1.0.0 (for notebooks)

---

🔧 Breaking Changes

API Changes

1. Differential Evolution

   # OLD (v0.1.0)
   result = differential_evolution(fn, bounds, popsize, maxiter, f, cr)
   
   # NEW (v0.2.0)
   result = differential_evolution(
       fn, bounds, popsize, maxiter,
       f=None,          # Now optional (adaptive)
       cr=None,         # Now optional (adaptive)
       strategy="rand1",  # NEW: strategy selection
       adaptive=True,   # NEW: adaptive jDE
       track_history=True  # NEW: convergence tracking
   )
   

2. Result Objects

   # OLD: Simple tuple
   (x_best, f_best)
   
   # NEW: Rich result object
   result.x              # Best parameters
   result.fun            # Best value
   result.nfev           # Function evaluations
   result.n_generations  # Generations
   result.history        # Convergence history
   result.success        # Convergence flag
   result.message        # Status message
   

3. Module Imports

   # OLD: Mixed imports
   from optimizr import differential_evolution, de_refactored
   
   # NEW: Clean imports
   from optimizr import differential_evolution
   from optimizr.de import DEResult, DEStrategy
   

Removed APIs

• de_refactored.differential_evolution → Use differential_evolution
• Legacy HMM/MCMC modules → Use modular versions in hmm/, mcmc/
• Portfolio-specific constructors → Use generic interfaces

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🎯 Migration Guide

From v0.1.0 to v0.2.0

Differential Evolution

# Before
result = differential_evolution(rosenbrock, bounds, 15, 1000, 0.8, 0.7)
x_best = result.x
f_best = result.fun

# After (with new features)
result = differential_evolution(
    rosenbrock, 
    bounds, 
    popsize=15, 
    maxiter=1000,
    strategy="currenttobest1",  # Better than rand1
    adaptive=True,              # Auto-tune F and CR
    track_history=True          # Monitor convergence
)

# Check convergence
if result.success:
    print(f"Converged in {result.n_generations} generations")
    
# Plot convergence
if result.history:
    gen, fit = result.convergence_curve()
    plt.semilogy(gen, fit)

Using New Mathematical Toolkit

# Before: Implement your own gradient
def numerical_gradient(f, x, h=1e-5):
    grad = np.zeros_like(x)
    for i in range(len(x)):
        x_plus = x.copy()
        x_plus[i] += h
        x_minus = x.copy()
        x_minus[i] -= h
        grad[i] = (f(x_plus) - f(x_minus)) / (2 * h)
    return grad

# After: Use built-in toolkit
from optimizr.maths_toolkit import gradient, hessian

grad = gradient(f, x)
hess = hessian(f, x)

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🧪 Testing

Test Coverage

cargo test --release --no-default-features
# Tests: 34 passed
# Coverage: ~85%

Notebook Validation

All notebooks tested and validated:

• ✅ 01_hmm_tutorial.ipynb
• ✅ 02_mcmc_tutorial.ipynb
• ✅ 03_differential_evolution_tutorial.ipynb
• ✅ 03_optimal_control_tutorial.ipynb
• ✅ 04_real_world_applications.ipynb
• ✅ 05_performance_benchmarks.ipynb

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📈 Known Issues & Limitations

1. Parallel Python Callbacks: Currently disabled due to GIL constraints. Pure Rust objectives support full parallelization.

2. Windows Build: Requires manual OpenBLAS installation. Working on pre-built wheels.

3. Large Populations: Memory usage scales O(N_pop × dimensions). Recommended max: 50,000 individuals.

4. Notebook Compatibility: Some visualizations require matplotlib ≥ 3.4.0.

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🔮 Roadmap for v0.3.0

Planned Features

1. Additional DE Variants

   • JADE (jDE with archive)
   • SHADE (Success-History based Adaptive DE)
   • L-SHADE (with linear population reduction)

2. Multi-Objective Optimization

   • NSGA-DE (Non-dominated Sorting)
   • MODE (Multi-Objective DE)
   • Pareto front computation

3. GPU Acceleration

   • CUDA kernels for population evaluation
   • OpenCL support
   • 10-100× additional speedup

4. Additional Algorithms

   • Particle Swarm Optimization (PSO)
   • CMA-ES (Covariance Matrix Adaptation)
   • Simulated Annealing
   • Ant Colony Optimization

5. Python Callback Parallelization

   • GIL-free callback mechanism
   • Sub-interpreter support
   • Process pool integration

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🙏 Contributors

• Core Development: Melvin Alvarez
• Mathematical Algorithms: Based on research papers (see References)
• Testing & Validation: Community contributors

📚 References

Differential Evolution

• Storn & Price (1997). "Differential evolution–a simple and efficient heuristic for global optimization"
• Das & Suganthan (2011). "Differential evolution: A survey of the state-of-the-art"
• Brest et al. (2006). "Self-Adapting Control Parameters in DE: jDE Algorithm"

Optimal Control

• Fleming & Rishel. "Deterministic and Stochastic Optimal Control"
• Øksendal & Sulem. "Applied Stochastic Control of Jump Diffusions"

Sparse Optimization

• d'Aspremont (2011). "Identifying Small Mean Reverting Portfolios"
• Candès et al. (2011). "Robust Principal Component Analysis?"

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📥 Download & Install

PyPI (Coming Soon)

pip install optimizr==0.2.0

Source

git clone https://github.com/ThotDjehuty/optimiz-r.git
cd optimiz-r
git checkout v0.2.0
maturin develop --release

Docker

docker pull thotdjehuty/optimizr:0.2.0
docker run -p 8888:8888 thotdjehuty/optimizr:0.2.0

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: docs/

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Thank you for using OptimizR! 🚀