Crate gosh_lbfgs 

Limited memory BFGS (L-BFGS) algorithm ported from liblbfgs

Example

// 0. Import the lib
use liblbfgs::lbfgs;
 
const N: usize = 100;
 
// 1. Initialize data
let mut x = [0.0 as f64; N];
for i in (0..N).step_by(2) {
    x[i] = -1.2;
    x[i + 1] = 1.0;
}
 
// 2. Defining how to evaluate function and gradient
let evaluate = |x: &[f64], gx: &mut [f64]| {
    let n = x.len();
 
    let mut fx = 0.0;
    for i in (0..n).step_by(2) {
        let t1 = 1.0 - x[i];
        let t2 = 10.0 * (x[i + 1] - x[i] * x[i]);
        gx[i + 1] = 20.0 * t2;
        gx[i] = -2.0 * (x[i] * gx[i + 1] + t1);
        fx += t1 * t1 + t2 * t2;
    }
 
    Ok(fx)
};
 
let prb = lbfgs()
    .with_max_iterations(5)
    .with_orthantwise(1.0, 0, 99) // enable OWL-QN
    .minimize(
        &mut x,                   // input variables
        evaluate,                 // define how to evaluate function
        |prgr| {                  // define progress monitor
            println!("iter: {:}", prgr.niter);
            false                 // returning true will cancel optimization
        }
    )
    .expect("lbfgs owlqn minimize");
 
println!("fx = {:}", prb.fx);

Modules

line

Find a satisfactory step length along predefined search direction

line_new

Find a satisfactory step length along predefined search direction

math

Backend for lbfgs vector operations

Structs

LbfgsParam

L-BFGS optimization parameters.

LbfgsState

LBFGS optimization state allowing iterative propagation

Output

Evaluated function value and gradient

Problem

Represents an optimization problem.

Progress

Store optimization progress data, for progress monitor

Report

Functions

default_evaluate

Default test function (rosenbrock) adopted from liblbfgs sample.c

default_progress

Default progress monitor adopted from liblbfgs sample.c

lbfgs

lbfgs_iter