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
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@@ -31,7 +31,7 @@ pub trait ResultExt<T> {
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fn and_then_log<F, U>(self, f: F, msg: &str) -> Result<U>
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where
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F: FnOnce(T) -> Result<U>;
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/// Map with context
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fn map_context<F, U>(self, f: F, ctx: &str) -> Result<U>
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where
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@@ -51,14 +51,13 @@ impl<T> ResultExt<T> for Result<T> {
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}
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}
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}
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fn map_context<F, U>(self, f: F, ctx: &str) -> Result<U>
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where
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F: FnOnce(T) -> U,
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{
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self.map(f).map_err(|e| {
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OptimizrError::ComputationError(format!("{}: {}", ctx, e))
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})
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self.map(f)
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.map_err(|e| OptimizrError::ComputationError(format!("{}: {}", ctx, e)))
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}
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}
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@@ -68,14 +67,14 @@ where
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F: FnMut() -> Result<T>,
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{
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let mut last_error = None;
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for _ in 0..max_attempts {
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match f() {
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Ok(val) => return Ok(val),
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Err(e) => last_error = Some(e),
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}
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}
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Err(last_error.unwrap_or_else(|| {
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OptimizrError::ComputationError("All retry attempts failed".to_string())
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}))
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@@ -101,16 +100,16 @@ where
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cache: std::sync::Mutex::new(std::collections::HashMap::new()),
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}
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}
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pub fn call(&self, x: &[f64]) -> T {
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let key: Vec<_> = x.iter().map(|&v| ordered_float::OrderedFloat(v)).collect();
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let mut cache = self.cache.lock().unwrap();
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if let Some(cached) = cache.get(&key) {
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return cached.clone();
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}
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let result = (self.f)(x);
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cache.insert(key, result.clone());
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result
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@@ -136,7 +135,7 @@ where
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value: None,
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}
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}
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pub fn force(&mut self) -> &T {
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if self.value.is_none() {
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let f = self.f.take().unwrap();
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@@ -190,7 +189,7 @@ mod tests {
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let result = vec![1, 2, 3]
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.pipe(|v| v.into_iter().map(|x| x * 2).collect::<Vec<_>>())
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.pipe(|v: Vec<_>| v.into_iter().sum::<i32>());
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assert_eq!(result, 12);
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}
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@@ -198,7 +197,7 @@ mod tests {
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fn test_partial() {
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let add = |a: i32, b: i32| a + b;
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let add5 = partial(add, 5);
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assert_eq!(add5(3), 8);
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}
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}
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