From 1adf00b04505cfe73fe949820194b833362e8d47 Mon Sep 17 00:00:00 2001
From: Powei Lin <poweilin1994@gmail.com>
Date: Sun, 23 Jul 2023 18:42:42 -0400
Subject: [PATCH] finish second version using auto grad

---
 src/tiny_solver.rs | 23 ++++++++---------------
 1 file changed, 8 insertions(+), 15 deletions(-)

diff --git a/src/tiny_solver.rs b/src/tiny_solver.rs
index 33701e5..d3edf4d 100644
--- a/src/tiny_solver.rs
+++ b/src/tiny_solver.rs
@@ -1,8 +1,6 @@
 extern crate nalgebra as na;
-use std::cmp;
-use std::ops::Mul;
-
 use num_dual;
+use std::ops::Mul;
 
 struct SolverParameters {
     gradient_threshold: f64,
@@ -88,10 +86,12 @@ pub trait TinySolver<const NUM_PARAMETERS: usize, const NUM_RESIDUALS: usize> {
                 println!("gradient too small. {}", max_gradient);
                 result.status = SolverStatus::GradientTooSmall;
                 break;
-            } else if (residual.norm() < solver_params.error_threshold) {
+            } else if residual.norm() < solver_params.error_threshold {
                 result.status = SolverStatus::ErrorTooSmall;
                 break;
             }
+
+            // initialize u and v
             if step == 0 {
                 u = solver_params.initial_scale_factor * jtj.diagonal().max();
                 v = 2;
@@ -101,19 +101,14 @@ pub trait TinySolver<const NUM_PARAMETERS: usize, const NUM_RESIDUALS: usize> {
             jtj_augmented.copy_from(&jtj);
             jtj_augmented.set_diagonal(&jtj_augmented.diagonal().add_scalar(u));
 
-            println!("jtj {}", jtj_augmented);
+            // println!("jtj {}", jtj_augmented);
             let dx = na::linalg::LU::new(jtj_augmented.clone())
                 .solve(&gradient)
                 .unwrap();
             let solution: na::SMatrix<f64, NUM_PARAMETERS, 1> = jtj_augmented.fixed_view(0, 0) * dx;
             let solved = (solution - gradient).abs().min() < solver_params.error_threshold;
             if solved {
-                // if (dx.norm() < params.relative_step_threshold * x.norm()) {
-                //     results.status = RELATIVE_STEP_SIZE_TOO_SMALL;
-                //     break;
-                // }
-                // println!("success!");
-                if (dx.norm() < solver_params.relative_step_threshold * params.norm()) {
+                if dx.norm() < solver_params.relative_step_threshold * params.norm() {
                     result.status = SolverStatus::RelativeStepSizeTooSmall;
                     break;
                 }
@@ -124,12 +119,11 @@ pub trait TinySolver<const NUM_PARAMETERS: usize, const NUM_RESIDUALS: usize> {
                 // TODO: Error handling on user eval.
                 let residual_new =
                     Self::cost_function(param_new.map(num_dual::DualSVec64::from_re)).map(|x| x.re);
-                let rho: f64 = ((residual.norm_squared() - residual_new.norm_squared())
-                    / dx.dot(&(u * dx + gradient)));
+                let rho: f64 = (residual.norm_squared() - residual_new.norm_squared())
+                    / dx.dot(&(u * dx + gradient));
                 if rho > 0.0 {
                     // Accept the Gauss-Newton step because the linear model fits well.
                     *params = param_new;
-                    // result.status = Update(function, x);
                     let tmp: f64 = 2.0 * rho - 1.0;
                     u = u * (1.0_f64 / 3.0).max(1.0 - tmp.powi(3));
                     v = 2;
@@ -150,7 +144,6 @@ pub trait TinySolver<const NUM_PARAMETERS: usize, const NUM_RESIDUALS: usize> {
         }
         result.error_magnitude = residual.norm();
         result.gradient_magnitude = gradient.norm();
-        // println!("x0 {}", params);
 
         result
     }