Struct ProximalGradientSolver 

pub struct ProximalGradientSolver {
    pub step_size: f64,
    pub max_iter: usize,
    pub tol: f64,
}

Proximal gradient method for composite minimisation: min f(x) + g(x). f is smooth, g is proximable.

Fields

step_size: f64

Step size (1/L where L is Lipschitz constant of ∇f).

max_iter: usize

Maximum iterations.

tol: f64

Convergence tolerance on iterates.

Implementations

impl ProximalGradientSolver

pub fn new(step_size: f64, max_iter: usize, tol: f64) -> Self

Create a new solver.

pub fn solve( &self, f_smooth: &ConvexFunction, prox_g: impl Fn(&[f64], f64) -> Vec<f64>, x0: &[f64], ) -> Vec<f64>

Run proximal gradient: x_{k+1} = prox_{t*g}(x_k - t * ∇f(x_k)).

Trait Implementations

impl Clone for ProximalGradientSolver

fn clone(&self) -> ProximalGradientSolver

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ProximalGradientSolver

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.