Crate liblbfgs

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
math	Backend for lbfgs vector operations

Structs

LbfgsParam	L-BFGS optimization parameters.
LbfgsState	LBFGS optimization state allowing iterative propagation
Orthantwise	Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) algorithm
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