Struct LBFGSB 

pub struct LBFGSB { /* private fields */ }

The L-BFGS-B (Limited memory, bounded Broyden-Fletcher-Goldfarb-Shanno) algorithm.

This minimization Algorithm is a quasi-Newton minimizer which approximates the inverse of the Hessian matrix using the L-BFGS update step with a modification to ensure boundary constraints are satisfied. The L-BFGS-B algorithm is described in detail in ¹.

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1. R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, “A Limited Memory Algorithm for Bound Constrained Optimization,” SIAM J. Sci. Comput., vol. 16, no. 5, pp. 1190–1208, Sep. 1995, doi: 10.1137/0916069. ↩

Trait Implementations

impl<P, U, E> Algorithm<P, GradientStatus, U, E> for LBFGSB

where P: Gradient<U, E>,

type Summary = MinimizationSummary

A type which holds a summary of the algorithm’s ending state.

type Config = LBFGSBConfig

The configuration struct for the algorithm.

fn initialize( &mut self, problem: &P, status: &mut GradientStatus, args: &U, config: &Self::Config, ) -> Result<(), E>

Any setup work done before the main steps of the algorithm should be done here.

fn step( &mut self, _current_step: usize, problem: &P, status: &mut GradientStatus, args: &U, config: &Self::Config, ) -> Result<(), E>

The main “step” of an algorithm, which is repeated until termination conditions are met or the max number of steps have been taken.

fn postprocessing( &mut self, problem: &P, status: &mut GradientStatus, args: &U, config: &Self::Config, ) -> Result<(), E>

Runs any steps needed by the Algorithm after termination or convergence. This will run regardless of whether the Algorithm converged.

fn summarize( &self, _current_step: usize, _problem: &P, status: &GradientStatus, _args: &U, config: &Self::Config, ) -> Result<Self::Summary, E>

Generates a new Algorithm::Summary from the current state of the Algorithm, which can be displayed or used elsewhere.

fn reset(&mut self)

Reset the algorithm to its initial state.

fn default_callbacks() -> Callbacks<Self, P, GradientStatus, U, E, Self::Config>

where Self: Sized,

Provides a set of reasonable default callbacks specific to this Algorithm.

fn process<C>( &mut self, problem: &P, args: &U, config: Self::Config, callbacks: C, ) -> Result<Self::Summary, E>

where C: Into<Callbacks<Self, P, S, U, E, Self::Config>>, Self: Sized,

Process the given problem using this Algorithm.

fn process_default( &mut self, problem: &P, user_data: &U, config: Self::Config, ) -> Result<Self::Summary, E>

where Self: Sized,

Process the given problem using this Algorithm.

impl Clone for LBFGSB

fn clone(&self) -> LBFGSB

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Default for LBFGSB

fn default() -> Self

Returns the “default value” for a type.

impl<P, U, E> Terminator<LBFGSB, P, GradientStatus, U, E, LBFGSBConfig> for LBFGSBFTerminator

where P: Gradient<U, E>,

fn check_for_termination( &mut self, _current_step: usize, algorithm: &mut LBFGSB, _problem: &P, status: &mut GradientStatus, _args: &U, _config: &LBFGSBConfig, ) -> ControlFlow<()>

A termination check which is called on each step of an Algorithm.

impl<P, U, E> Terminator<LBFGSB, P, GradientStatus, U, E, LBFGSBConfig> for LBFGSBGTerminator

where P: Gradient<U, E>,

fn check_for_termination( &mut self, _current_step: usize, algorithm: &mut LBFGSB, _problem: &P, status: &mut GradientStatus, _args: &U, _config: &LBFGSBConfig, ) -> ControlFlow<()>

A termination check which is called on each step of an Algorithm.

impl<P, U, E> Terminator<LBFGSB, P, GradientStatus, U, E, LBFGSBConfig> for LBFGSBInfNormGTerminator

where P: Gradient<U, E>,

fn check_for_termination( &mut self, _current_step: usize, algorithm: &mut LBFGSB, _problem: &P, status: &mut GradientStatus, _args: &U, _config: &LBFGSBConfig, ) -> ControlFlow<()>

A termination check which is called on each step of an Algorithm.