tiny_solver/optimizer/
gauss_newton_optimizer.rs

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

use faer_ext::IntoNalgebra;

use crate::common::OptimizerOptions;
use crate::linear;
use crate::optimizer;
use crate::parameter_block::ParameterBlock;
use crate::sparse::LinearSolverType;
use crate::sparse::SparseLinearSolver;

#[derive(Debug)]
pub struct GaussNewtonOptimizer {}
impl GaussNewtonOptimizer {
    pub fn new() -> Self {
        Self {}
    }
}
impl Default for GaussNewtonOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

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 parameter_blocks: HashMap<String, ParameterBlock> =
            problem.initialize_parameter_blocks(initial_values);

        let variable_name_to_col_idx_dict =
            problem.get_variable_name_to_col_idx_dict(&parameter_blocks);
        let total_variable_dimension = parameter_blocks.values().map(|p| p.tangent_size()).sum();

        let opt_option = optimizer_option.unwrap_or_default();
        let mut linear_solver: Box<dyn SparseLinearSolver> = match opt_option.linear_solver_type {
            LinearSolverType::SparseCholesky => Box::new(linear::SparseCholeskySolver::new()),
            LinearSolverType::SparseQR => Box::new(linear::SparseQRSolver::new()),
        };

        let mut last_err: f64 = 1.0;

        for i in 0..opt_option.max_iteration {
            let (residuals, jac) = problem.compute_residual_and_jacobian(
                &parameter_blocks,
                &variable_name_to_col_idx_dict,
                total_variable_dimension,
            );
            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;
            } else if current_error.is_nan() {
                log::debug!("solve ax=b failed, current error is nan");
                return None;
            }
            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) = linear_solver.solve(&residuals, &jac) {
                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_dx2(
                    &dx_na,
                    &mut parameter_blocks,
                    &variable_name_to_col_idx_dict,
                );
            } else {
                log::debug!("solve ax=b failed");
                return None;
            }
        }
        let params = parameter_blocks
            .iter()
            .map(|(k, v)| (k.to_owned(), v.params.clone()))
            .collect();
        Some(params)
    }
}