extern crate argmin;
extern crate ndarray;
use ndarray::Array1;
use argmin::prelude::*;
use argmin::{ArgminProblem, BacktrackingLineSearch, GDGammaUpdate, GradientDescent};
use argmin::testfunctions::{rosenbrock_derivative_nd, rosenbrock_nd};
fn run() -> Result<(), Box<std::error::Error>> {
let cost = |x: &Array1<f64>| -> f64 { rosenbrock_nd(x, 1_f64, 100_f64) };
let gradient = |x: &Array1<f64>| -> Array1<f64> { rosenbrock_derivative_nd(x, 1_f64, 100_f64) };
let mut prob = ArgminProblem::new(&cost);
prob.gradient(&gradient);
let mut solver = GradientDescent::new();
solver.max_iters(10_000);
solver.gamma_update(GDGammaUpdate::BarzilaiBorwein);
let init_param: Array1<f64> = Array1::from_vec(vec![1.5, 1.5]);
println!("{:?}", init_param);
let result1 = solver.run(&prob, &init_param)?;
println!("{:?}", result1);
let mut solver = GradientDescent::new();
solver.max_iters(10_000);
let mut linesearch = BacktrackingLineSearch::new(&cost, &gradient);
linesearch.alpha(1.0);
solver.gamma_update(GDGammaUpdate::BacktrackingLineSearch(linesearch));
let result2 = solver.run(&prob, &init_param)?;
println!("{:?}", result2);
let mut solver = GradientDescent::new();
solver.init(&prob, &init_param)?;
let mut par;
loop {
par = solver.next_iter()?;
if par.terminated {
break;
};
}
println!("{:?}", par);
Ok(())
}
fn main() {
if let Err(ref e) = run() {
println!("error: {}", e);
}
}