Expand description
Unconstrained optimization algorithms
This module provides various algorithms for unconstrained minimization problems.
Re-exports§
pub use result::OptimizeResult;pub use adaptive_convergence::check_convergence_adaptive;pub use adaptive_convergence::create_adaptive_options_for_problem;pub use adaptive_convergence::AdaptationStats;pub use adaptive_convergence::AdaptiveToleranceOptions;pub use adaptive_convergence::AdaptiveToleranceState;pub use adaptive_convergence::ConvergenceStatus;pub use advanced_line_search::advanced_line_search;pub use advanced_line_search::create_non_monotone_state;pub use advanced_line_search::AdvancedLineSearchOptions;pub use advanced_line_search::InterpolationStrategy;pub use advanced_line_search::LineSearchMethod;pub use advanced_line_search::LineSearchResult;pub use advanced_line_search::LineSearchStats;pub use bfgs::minimize_bfgs;pub use callback_diagnostics::minimize_with_diagnostics;pub use callback_diagnostics::optimize_with_diagnostics;pub use callback_diagnostics::CallbackInfo;pub use callback_diagnostics::CallbackResult;pub use callback_diagnostics::DiagnosticOptimizer;pub use callback_diagnostics::OptimizationCallback;pub use conjugate_gradient::minimize_conjugate_gradient;pub use convergence_diagnostics::ConvergenceDiagnostics;pub use convergence_diagnostics::DiagnosticCollector;pub use convergence_diagnostics::DiagnosticOptions;pub use convergence_diagnostics::DiagnosticWarning;pub use convergence_diagnostics::ExportFormat;pub use convergence_diagnostics::IterationDiagnostic;pub use convergence_diagnostics::LineSearchDiagnostic;pub use convergence_diagnostics::PerformanceMetrics;pub use convergence_diagnostics::ProblemAnalysis;pub use convergence_diagnostics::ProblemDifficulty;pub use convergence_diagnostics::WarningSeverity;pub use efficient_sparse::minimize_efficient_sparse_newton;pub use efficient_sparse::EfficientSparseOptions;pub use efficient_sparse::SparsityInfo;pub use lbfgs::minimize_lbfgs;pub use lbfgs::minimize_lbfgsb;pub use memory_efficient::create_memory_efficient_optimizer;pub use memory_efficient::minimize_memory_efficient_lbfgs;pub use memory_efficient::MemoryOptions;pub use memory_efficient_sparse::create_advanced_scale_optimizer;pub use memory_efficient_sparse::minimize_advanced_scale;pub use memory_efficient_sparse::AdvancedScaleOptions;pub use nelder_mead::minimize_nelder_mead;pub use newton::minimize_newton_cg;pub use powell::minimize_powell;pub use quasi_newton::minimize_dfp;pub use quasi_newton::minimize_quasi_newton;pub use quasi_newton::minimize_sr1;pub use quasi_newton::UpdateFormula;pub use robust_convergence::create_robust_options_for_problem;pub use robust_convergence::RobustConvergenceOptions;pub use robust_convergence::RobustConvergenceResult;pub use robust_convergence::RobustConvergenceState;pub use simd_bfgs::minimize_simd_bfgs;pub use simd_bfgs::minimize_simd_bfgs_default;pub use simd_bfgs::SimdBfgsOptions;pub use sparse_optimization::auto_detect_sparsity;pub use sparse_optimization::minimize_sparse_bfgs;pub use sparse_optimization::SparseOptimizationOptions;pub use strong_wolfe::create_strong_wolfe_options_for_method;pub use strong_wolfe::strong_wolfe_line_search;pub use strong_wolfe::StrongWolfeOptions;pub use strong_wolfe::StrongWolfeResult;pub use subspace_methods::minimize_adaptive_subspace;pub use subspace_methods::minimize_block_coordinate_descent;pub use subspace_methods::minimize_cyclical_coordinate_descent;pub use subspace_methods::minimize_random_coordinate_descent;pub use subspace_methods::minimize_random_subspace;pub use subspace_methods::minimize_subspace;pub use subspace_methods::SubspaceMethod;pub use subspace_methods::SubspaceOptions;pub use truncated_newton::minimize_truncated_newton;pub use truncated_newton::minimize_trust_region_newton;pub use truncated_newton::Preconditioner;pub use truncated_newton::TruncatedNewtonOptions;pub use trust_region::minimize_trust_exact;pub use trust_region::minimize_trust_krylov;pub use trust_region::minimize_trust_ncg;
Modules§
- adaptive_
convergence - Adaptive tolerance selection and convergence criteria
- advanced_
line_ search - Advanced line search algorithms for optimization
- bfgs
- BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm for unconstrained optimization
- callback_
diagnostics - Integration of convergence diagnostics with callback system
- conjugate_
gradient - Conjugate Gradient method for unconstrained optimization
- convergence_
diagnostics - Enhanced convergence diagnostics for optimization algorithms
- efficient_
sparse - Efficient sparse Jacobian and Hessian handling for optimization
- lbfgs
- Limited-memory BFGS algorithms for large-scale optimization
- line_
search - Line search algorithms for optimization
- memory_
efficient - Memory-efficient algorithms for large-scale optimization problems
- memory_
efficient_ sparse - Memory-efficient sparse optimization for very large-scale problems
- nelder_
mead - Nelder-Mead simplex algorithm for unconstrained optimization
- newton
- Newton methods for unconstrained optimization
- powell
- Powell’s method for unconstrained optimization
- quasi_
newton - Quasi-Newton algorithms with different update formulas (SR1, DFP, BFGS)
- result
- Result structure for unconstrained optimization
- robust_
convergence - Robust convergence criteria with multiple stopping conditions
- simd_
bfgs - SIMD-accelerated BFGS algorithm
- sparse_
optimization - Sparse optimization algorithms for high-dimensional problems
- strong_
wolfe - Enhanced Strong Wolfe conditions line search implementation
- subspace_
methods - Subspace methods for very high-dimensional optimization
- truncated_
newton - Truncated Newton methods for large-scale optimization
- trust_
region - Trust region methods for unconstrained optimization
- utils
- Common utilities for unconstrained optimization algorithms
Structs§
Enums§
- Method
- Optimization methods for unconstrained minimization.
Functions§
- minimize
- Main minimize function for unconstrained optimization