tiny_solver/
gauss_newton_optimizer.rs

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use log::trace;
use std::{collections::HashMap, time::Instant};

use faer::sparse::linalg::solvers;
use faer_ext::IntoNalgebra;

use crate::{linear::sparse_cholesky, optimizer, OptimizerOptions};

#[derive(Debug, Clone)]
pub struct GaussNewtonOptimizer {}

impl optimizer::Optimizer for GaussNewtonOptimizer {
    fn optimize(
        &self,
        problem: &crate::problem::Problem,
        initial_values: &std::collections::HashMap<String, nalgebra::DVector<f64>>,
        optimizer_option: Option<OptimizerOptions>,
    ) -> Option<HashMap<String, nalgebra::DVector<f64>>> {
        let mut params = initial_values.clone();
        let opt_option = optimizer_option.unwrap_or_default();

        let mut last_err: f64 = 1.0;
        let mut symbolic_pattern: Option<solvers::SymbolicCholesky<usize>> = None;

        for i in 0..opt_option.max_iteration {
            let (residuals, jac) = problem.compute_residual_and_jacobian(&params);
            let current_error = residuals.norm_l2();
            trace!("iter:{} total err:{}", i, current_error);

            if current_error < opt_option.min_error_threshold {
                trace!("error too low");
                break;
            }
            if i > 0 {
                if (last_err - current_error).abs() < opt_option.min_abs_error_decrease_threshold {
                    trace!("absolute error decreas low");
                    break;
                } else if (last_err - current_error).abs() / last_err
                    < opt_option.min_rel_error_decrease_threshold
                {
                    trace!("reletive error decrease low");
                    break;
                }
            }
            last_err = current_error;

            let start = Instant::now();
            if let Some(dx) = sparse_cholesky(&residuals, &jac, &mut symbolic_pattern) {
                let duration = start.elapsed();
                trace!("Time elapsed in solve Ax=b is: {:?}", duration);

                let dx_na = dx.as_ref().into_nalgebra().column(0).clone_owned();
                self.apply_dx(
                    &dx_na,
                    &mut params,
                    &problem.variable_name_to_col_idx_dict,
                    &problem.fixed_variable_indexes,
                    &problem.variable_bounds,
                );
            } else {
                log::debug!("solve ax=b failed");
                return None;
            }
        }
        Some(params)
    }
}