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Least squares optimization

This module provides methods for solving nonlinear least squares problems, including robust methods that are less sensitive to outliers.

Example

use scirs2_core::ndarray::{array, Array1, Array2};
use scirs2_optimize::least_squares::{least_squares, Method};

// Define a function that returns the residuals
fn residual(x: &[f64], _data: &[f64]) -> Array1<f64> {
    let y = array![
        x[0] + 2.0 * x[1] - 2.0,
        x[0] + x[1] - 1.0
    ];
    y
}

// Define the Jacobian (optional)
fn jacobian(x: &[f64], _data: &[f64]) -> Array2<f64> {
    array![[1.0, 2.0], [1.0, 1.0]]
}

// Initial guess
let x0 = array![0.0, 0.0];
let data = array![];  // No data needed for this example

// Solve the least squares problem
let result = least_squares(residual, &x0, Method::LevenbergMarquardt, Some(jacobian), &data, None)?;

// The solution should be close to [0.0, 1.0]
assert!(result.success);

Structs

Options

Options for the least squares optimizer.

Enums

Method

Optimization methods for least squares problems.

Functions

least_squares

Solve a nonlinear least-squares problem.