A number of minor changes to TinySolver
1. Instead of Core/LU just include Eigen/Dense
2. Rename SolverParameters to Options and params to options.
3. Rename Results to Summary.
4. Summary::error_magnitude -> Summary::final_cost.
5. Add Summary::initial_cost.
6. Change definitions of Summary::initial_cost and Summary::final_cost
to match those used by Ceres::Solver.
Change-Id: Id64b78398f47810ca25938a15423c514fc8c164d
diff --git a/include/ceres/tiny_solver.h b/include/ceres/tiny_solver.h
index 4d40ee8..8f25918 100644
--- a/include/ceres/tiny_solver.h
+++ b/include/ceres/tiny_solver.h
@@ -54,8 +54,7 @@
#include <cassert>
#include <cmath>
-#include "Eigen/Core"
-#include "Eigen/LU"
+#include "Eigen/Dense"
namespace ceres {
@@ -139,8 +138,6 @@
typedef typename Function::Scalar Scalar;
typedef typename Eigen::Matrix<Scalar, NUM_PARAMETERS, 1> Parameters;
- // TODO(keir): Some of these knobs can be derived from each other and
- // removed, instead of requiring the user to set them.
enum Status {
RUNNING,
// Resulting solution may be OK to use.
@@ -154,8 +151,12 @@
FAILED_TO_SOLVER_LINEAR_SYSTEM,
};
- struct SolverParameters {
- SolverParameters()
+ // TODO(keir): Some of these knobs can be derived from each other and
+ // removed, instead of requiring the user to set them.
+ // TODO(sameeragarwal): Make the parameters consistent with ceres
+ // TODO(sameeragarwal): Return a struct instead of just an enum.
+ struct Options {
+ Options()
: gradient_threshold(1e-16),
relative_step_threshold(1e-16),
error_threshold(1e-16),
@@ -168,9 +169,10 @@
int max_iterations; // Maximum number of solver iterations.
};
- struct Results {
- Scalar error_magnitude; // ||f(x)||
- Scalar gradient_magnitude; // ||J'f(x)||
+ struct Summary {
+ Scalar initial_cost; // 1/2 ||f(x)||^2
+ Scalar final_cost; // 1/2 ||f(x)||^2
+ Scalar gradient_magnitude; // ||J'f(x)||
int num_failed_linear_solves;
int iterations;
Status status;
@@ -185,26 +187,27 @@
// unstable. Nevertheless, it is often good enough and is fast.
jtj_ = jacobian_.transpose() * jacobian_;
g_ = jacobian_.transpose() * error_;
- if (g_.array().abs().maxCoeff() < params.gradient_threshold) {
+ if (g_.array().abs().maxCoeff() < options.gradient_threshold) {
return GRADIENT_TOO_SMALL;
- } else if (error_.norm() < params.error_threshold) {
+ } else if (error_.norm() < options.error_threshold) {
return ERROR_TOO_SMALL;
}
return RUNNING;
}
- Results Solve(const Function& function, Parameters* x_and_min) {
+ Summary& Solve(const Function& function, Parameters* x_and_min) {
Initialize<NUM_RESIDUALS, NUM_PARAMETERS>(function);
assert(x_and_min);
Parameters& x = *x_and_min;
- results.status = Update(function, x);
+ summary.status = Update(function, x);
+ summary.initial_cost = error_.squaredNorm() / Scalar(2.0);
- Scalar u = Scalar(params.initial_scale_factor * jtj_.diagonal().maxCoeff());
+ Scalar u = Scalar(options.initial_scale_factor * jtj_.diagonal().maxCoeff());
Scalar v = 2;
int i;
- for (i = 0; results.status == RUNNING && i < params.max_iterations; ++i) {
+ for (i = 0; summary.status == RUNNING && i < options.max_iterations; ++i) {
jtj_augmented_ = jtj_;
jtj_augmented_.diagonal().array() += u;
@@ -212,8 +215,8 @@
dx_ = linear_solver_.solve(g_);
bool solved = (jtj_augmented_ * dx_).isApprox(g_);
if (solved) {
- if (dx_.norm() < params.relative_step_threshold * x.norm()) {
- results.status = RELATIVE_STEP_SIZE_TOO_SMALL;
+ if (dx_.norm() < options.relative_step_threshold * x.norm()) {
+ summary.status = RELATIVE_STEP_SIZE_TOO_SMALL;
break;
}
x_new_ = x + dx_;
@@ -227,14 +230,14 @@
if (rho > 0) {
// Accept the Gauss-Newton step because the linear model fits well.
x = x_new_;
- results.status = Update(function, x);
+ summary.status = Update(function, x);
Scalar tmp = Scalar(2 * rho - 1);
u = u * std::max(1 / 3., 1 - tmp * tmp * tmp);
v = 2;
continue;
}
} else {
- results.num_failed_linear_solves++;
+ summary.num_failed_linear_solves++;
}
// Reject the update because either the normal equations failed to solve
// or the local linear model was not good (rho < 0). Instead, increase u
@@ -242,17 +245,17 @@
u *= v;
v *= 2;
}
- if (results.status == RUNNING) {
- results.status = HIT_MAX_ITERATIONS;
+ if (summary.status == RUNNING) {
+ summary.status = HIT_MAX_ITERATIONS;
}
- results.error_magnitude = error_.norm();
- results.gradient_magnitude = g_.norm();
- results.iterations = i;
- return results;
+ summary.final_cost = error_.squaredNorm() / Scalar(2.0);
+ summary.gradient_magnitude = g_.norm();
+ summary.iterations = i;
+ return summary;
}
- SolverParameters params;
- Results results;
+ Options options;
+ Summary summary;
private:
// Preallocate everything, including temporary storage needed for solving the
