Function gradient_descent 

pub fn gradient_descent(
    obj: &impl Objective,
    x0: &[f64],
    lr: f64,
    max_iter: usize,
    tol: f64,
) -> OptimizeResult

Minimizes obj by full-gradient steps of fixed learning rate.

At each step the point moves against the gradient: x ← x − lr·∇f(x). The run stops early (reporting ConvergenceStatus::Converged) when the gradient norm drops below tol, otherwise it runs the full max_iter budget and reports ConvergenceStatus::MaxIterReached.

Arguments

• obj — the objective to minimize.
• x0 — the starting point; its length is the problem dimension.
• lr — the learning rate (step size); must be positive and small enough for the problem’s curvature or the iterates diverge.
• max_iter — the maximum number of gradient steps.
• tol — the gradient-norm convergence threshold.

Returns

An OptimizeResult with the located point, its objective value, the number of iterations performed, and the convergence status.

Examples

use stats_claw::optimizers::gradient::gradient_descent;
use stats_claw::optimizers::objectives::Quadratic;
use stats_claw::optimizers::ConvergenceStatus;

let obj = Quadratic::new(vec![3.0, -2.0]);
let r = gradient_descent(&obj, &[0.0, 0.0], 0.1, 10_000, 1e-12);
assert!(matches!(r.status, ConvergenceStatus::Converged));
assert!((r.x[0] - 3.0).abs() < 1e-6);