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Optimize the computation of the LM diagonal in TinySolver

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This eliminates an entire vector and computation of a square root
followed by a squaring.

Thanks to @rlabbe for pointing this out.

Change-Id: I0de117b31b9332c61e687f18466d7cb2e2ac611e
This commit is contained in:
Sameer Agarwal
2023-10-10 10:38:10 -07:00
parent 611b139b1b
commit 2120eae674
1 changed files with 4 additions and 6 deletions
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include/ceres/tiny_solver.h
+4 -6
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@@ -248,10 +248,9 @@ class TinySolver {
jtj_regularized_ = jtj_;
const Scalar min_diagonal = 1e-6;
const Scalar max_diagonal = 1e32;
for (int i = 0; i < lm_diagonal_.rows(); ++i) {
lm_diagonal_[i] = std::sqrt(
u * (std::min)((std::max)(jtj_(i, i), min_diagonal), max_diagonal));
jtj_regularized_(i, i) += lm_diagonal_[i] * lm_diagonal_[i];
for (int i = 0; i < dx_.rows(); ++i) {
jtj_regularized_(i, i) +=
u * (std::min)((std::max)(jtj_(i, i), min_diagonal), max_diagonal);
}
// TODO(sameeragarwal): Check for failure and deal with it.
@@ -338,7 +337,7 @@ class TinySolver {
// linear system. This allows reusing the intermediate storage across solves.
LinearSolver linear_solver_;
Scalar cost_;
Parameters dx_, x_new_, g_, jacobi_scaling_, lm_diagonal_, lm_step_;
Parameters dx_, x_new_, g_, jacobi_scaling_, lm_step_;
Eigen::Matrix<Scalar, NUM_RESIDUALS, 1> residuals_, f_x_new_;
Eigen::Matrix<Scalar, NUM_RESIDUALS, NUM_PARAMETERS> jacobian_;
Eigen::Matrix<Scalar, NUM_PARAMETERS, NUM_PARAMETERS> jtj_, jtj_regularized_;
@@ -385,7 +384,6 @@ class TinySolver {
x_new_.resize(num_parameters);
g_.resize(num_parameters);
jacobi_scaling_.resize(num_parameters);
lm_diagonal_.resize(num_parameters);
lm_step_.resize(num_parameters);
residuals_.resize(num_residuals);
f_x_new_.resize(num_residuals);