[−][src]Crate lbfgs

lbfgs

The L-BFGS is an Hessian approximation algorithm commonly used by optimization algorithms in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm using a limited amount of computer memory. In this implementation extra condition are added to have convergence properties for non-convex problems, based on the C-BFGS conditions, together with basic checks on the local curvature.

Example

Create a fully featured instance of the L-BFGS algorithm with curvature and C-BFGS checks enabled.

use lbfgs::*;

fn main() {
    // Problem size and the number of stored vectors in L-BFGS cannot be zero
    let problem_size = 3;
    let lbfgs_memory_size = 5;

    // Create the L-BFGS instance with curvature and C-BFGS checks enabled
    let mut lbfgs = Lbfgs::new(problem_size, lbfgs_memory_size)
        .with_sy_epsilon(1e-8)     // L-BFGS acceptance condition on s'*y > sy_espsilon
        .with_cbfgs_alpha(1.0)     // C-BFGS condition:
        .with_cbfgs_epsilon(1e-4); // y'*s/||s||^2 > epsilon * ||grad(x)||^alpha

    // Starting value is always accepted (no s or y vectors yet)
    assert_eq!(
        lbfgs.update_hessian(&[0.0, 0.0, 0.0], &[0.0, 0.0, 0.0]),
        UpdateStatus::UpdateOk
    );

    // Rejected because of CBFGS condition
    assert_eq!(
        lbfgs.update_hessian(&[-0.838, 0.260, 0.479], &[-0.5, 0.6, -1.2]),
        UpdateStatus::Rejection
    );

    // This will fail because y'*s == 0 (curvature condition)
    assert_eq!(
        lbfgs.update_hessian(
            &[-0.5, 0.6, -1.2],
            &[0.419058177461747, 0.869843029576958, 0.260313940846084]
        ),
        UpdateStatus::Rejection
    );

    // A proper update that will be accepted
    assert_eq!(
        lbfgs.update_hessian(&[-0.5, 0.6, -1.2], &[0.1, 0.2, -0.3]),
        UpdateStatus::UpdateOk
    );

    // Apply Hessian approximation on a gradient
    let mut g = [-3.1, 1.5, 2.1];
    let correct_dir = [-1.100601247872944, -0.086568349404424, 0.948633011911515];

    lbfgs.apply_hessian(&mut g);

    assert!((g[0] - correct_dir[0]).abs() < 1e-12);
    assert!((g[1] - correct_dir[1]).abs() < 1e-12);
    assert!((g[2] - correct_dir[2]).abs() < 1e-12);
}

Errors

update_hessian will give errors if the C-BFGS or L-BFGS curvature conditions are not met.

Panics

with_sy_epsilon, with_cbfgs_alpha, and with_cbfgs_epsilon will panic if given negative values.

update_hessian and apply_hessian will panic if given slices which are not the same length as the problem_size.

[−][src]Crate lbfgs

lbfgs

Example

Errors

Panics

Modules

Structs

Enums

Constants