Extend more_garbow_hillstrom to include unconstrained solves.
Change-Id: If855b0fff26898a61b701a1b7fcd02337614d108
diff --git a/examples/more_garbow_hillstrom.cc b/examples/more_garbow_hillstrom.cc
index 505d9b3..42d7450 100644
--- a/examples/more_garbow_hillstrom.cc
+++ b/examples/more_garbow_hillstrom.cc
@@ -58,13 +58,14 @@
namespace ceres {
namespace examples {
-#define BEGIN_BOUNDS_TEST(name, num_parameters, num_residuals) \
+#define BEGIN_MGH_PROBLEM(name, num_parameters, num_residuals) \
struct name { \
static const int kNumParameters = num_parameters; \
static const double initial_x[kNumParameters]; \
static const double lower_bounds[kNumParameters]; \
static const double upper_bounds[kNumParameters]; \
- static const double optimal_cost; \
+ static const double constrained_optimal_cost; \
+ static const double unconstrained_optimal_cost; \
static CostFunction* Create() { \
return new AutoDiffCostFunction<name, \
num_residuals, \
@@ -73,47 +74,50 @@
template <typename T> \
bool operator()(const T* const x, T* residual) const {
-#define END_BOUNDS_TEST return true; } };
+#define END_MGH_PROBLEM return true; } };
-BEGIN_BOUNDS_TEST(TestProblem3, 2, 2)
+BEGIN_MGH_PROBLEM(TestProblem3, 2, 2)
const T x1 = x[0];
const T x2 = x[1];
residual[0] = T(10000.0) * x1 * x2 - T(1.0);
residual[1] = exp(-x1) + exp(-x2) - T(1.0001);
-END_BOUNDS_TEST;
+END_MGH_PROBLEM;
const double TestProblem3::initial_x[] = {0.0, 1.0};
const double TestProblem3::lower_bounds[] = {0.0, 1.0};
const double TestProblem3::upper_bounds[] = {1.0, 9.0};
-const double TestProblem3::optimal_cost = 0.15125900e-9;
+const double TestProblem3::constrained_optimal_cost = 0.15125900e-9;
+const double TestProblem3::unconstrained_optimal_cost = 0.0;
-BEGIN_BOUNDS_TEST(TestProblem4, 2, 3)
+BEGIN_MGH_PROBLEM(TestProblem4, 2, 3)
const T x1 = x[0];
const T x2 = x[1];
residual[0] = x1 - T(1000000.0);
residual[1] = x2 - T(0.000002);
residual[2] = x1 * x2 - T(2.0);
-END_BOUNDS_TEST;
+END_MGH_PROBLEM;
const double TestProblem4::initial_x[] = {1.0, 1.0};
const double TestProblem4::lower_bounds[] = {0.0, 0.00003};
const double TestProblem4::upper_bounds[] = {1000000.0, 100.0};
-const double TestProblem4::optimal_cost = 0.78400000e3;
+const double TestProblem4::constrained_optimal_cost = 0.78400000e3;
+const double TestProblem4::unconstrained_optimal_cost = 0.0;
-BEGIN_BOUNDS_TEST(TestProblem5, 2, 3)
+BEGIN_MGH_PROBLEM(TestProblem5, 2, 3)
const T x1 = x[0];
const T x2 = x[1];
residual[0] = T(1.5) - x1 * (T(1.0) - x2);
residual[1] = T(2.25) - x1 * (T(1.0) - x2 * x2);
residual[2] = T(2.625) - x1 * (T(1.0) - x2 * x2 * x2);
-END_BOUNDS_TEST;
+END_MGH_PROBLEM;
const double TestProblem5::initial_x[] = {1.0, 1.0};
const double TestProblem5::lower_bounds[] = {0.6, 0.5};
const double TestProblem5::upper_bounds[] = {10.0, 100.0};
-const double TestProblem5::optimal_cost = 0.0;
+const double TestProblem5::constrained_optimal_cost = 0.0;
+const double TestProblem5::unconstrained_optimal_cost = 0.0;
-BEGIN_BOUNDS_TEST(TestProblem7, 3, 3)
+BEGIN_MGH_PROBLEM(TestProblem7, 3, 3)
const T x1 = x[0];
const T x2 = x[1];
const T x3 = x[2];
@@ -122,14 +126,15 @@
residual[0] = T(10.0) * (x3 - T(10.0) * theta);
residual[1] = T(10.0) * (sqrt(x1 * x1 + x2 * x2) - T(1.0));
residual[2] = x3;
-END_BOUNDS_TEST;
+END_MGH_PROBLEM;
const double TestProblem7::initial_x[] = {-1.0, 0.0, 0.0};
const double TestProblem7::lower_bounds[] = {-100.0, -1.0, -1.0};
const double TestProblem7::upper_bounds[] = {0.8, 1.0, 1.0};
-const double TestProblem7::optimal_cost = 0.99042212;
+const double TestProblem7::constrained_optimal_cost = 0.99042212;
+const double TestProblem7::unconstrained_optimal_cost = 0.0;
-BEGIN_BOUNDS_TEST(TestProblem9, 3, 15)
+BEGIN_MGH_PROBLEM(TestProblem9, 3, 15)
const T x1 = x[0];
const T x2 = x[1];
const T x3 = x[2];
@@ -142,17 +147,18 @@
const T y_i = T(y[i]);
residual[i] = x1 * exp( -x2 * (t_i - x3) * (t_i - x3) / T(2.0)) - y_i;
}
-END_BOUNDS_TEST;
+END_MGH_PROBLEM;
const double TestProblem9::initial_x[] = {0.4, 1.0, 0.0};
const double TestProblem9::lower_bounds[] = {0.398, 1.0 ,-0.5};
const double TestProblem9::upper_bounds[] = {4.2, 2.0, 0.1};
-const double TestProblem9::optimal_cost = 0.11279300e-7;
+const double TestProblem9::constrained_optimal_cost = 0.11279300e-7;
+const double TestProblem9::unconstrained_optimal_cost = 0.112793e-7;
-#undef BEGIN_BOUNDS_TEST
-#undef END_BOUNDS_TEST
+#undef BEGIN_MGH_PROBLEM
+#undef END_MGH_PROBLEM
-template<typename TestProblem> string Solve() {
+template<typename TestProblem> string ConstrainedSolve() {
double x[TestProblem::kNumParameters];
std::copy(TestProblem::initial_x,
TestProblem::initial_x + TestProblem::kNumParameters,
@@ -176,8 +182,40 @@
const double kMinLogRelativeError = 5.0;
const double log_relative_error = -std::log10(
- std::abs(2.0 * summary.final_cost - TestProblem::optimal_cost) /
- (TestProblem::optimal_cost > 0.0 ? TestProblem::optimal_cost : 1.0));
+ std::abs(2.0 * summary.final_cost - TestProblem::constrained_optimal_cost) /
+ (TestProblem::constrained_optimal_cost > 0.0
+ ? TestProblem::constrained_optimal_cost
+ : 1.0));
+
+ return (log_relative_error >= kMinLogRelativeError
+ ? "Success\n"
+ : "Failure\n");
+}
+
+template<typename TestProblem> string UnconstrainedSolve() {
+ double x[TestProblem::kNumParameters];
+ std::copy(TestProblem::initial_x,
+ TestProblem::initial_x + TestProblem::kNumParameters,
+ x);
+
+ Problem problem;
+ problem.AddResidualBlock(TestProblem::Create(), NULL, x);
+
+ Solver::Options options;
+ options.parameter_tolerance = 1e-18;
+ options.function_tolerance = 1e-18;
+ options.gradient_tolerance = 1e-18;
+ options.max_num_iterations = 1000;
+ options.linear_solver_type = DENSE_QR;
+ Solver::Summary summary;
+ Solve(options, &problem, &summary);
+
+ const double kMinLogRelativeError = 5.0;
+ const double log_relative_error = -std::log10(
+ std::abs(2.0 * summary.final_cost - TestProblem::unconstrained_optimal_cost) /
+ (TestProblem::unconstrained_optimal_cost > 0.0
+ ? TestProblem::unconstrained_optimal_cost
+ : 1.0));
return (log_relative_error >= kMinLogRelativeError
? "Success\n"
@@ -191,13 +229,32 @@
google::ParseCommandLineFlags(&argc, &argv, true);
google::InitGoogleLogging(argv[0]);
- using ceres::examples::Solve;
+ using ceres::examples::ConstrainedSolve;
+ using ceres::examples::UnconstrainedSolve;
- std::cout << "Test problem 3 : " << Solve<ceres::examples::TestProblem3>();
- std::cout << "Test problem 4 : " << Solve<ceres::examples::TestProblem4>();
- std::cout << "Test problem 5 : " << Solve<ceres::examples::TestProblem5>();
- std::cout << "Test problem 7 : " << Solve<ceres::examples::TestProblem7>();
- std::cout << "Test problem 9 : " << Solve<ceres::examples::TestProblem9>();
+ std::cout << "Unconstrained Problems\n";
+ std::cout << "Test problem 3 : "
+ << UnconstrainedSolve<ceres::examples::TestProblem3>();
+ std::cout << "Test problem 4 : "
+ << UnconstrainedSolve<ceres::examples::TestProblem4>();
+ std::cout << "Test problem 5 : "
+ << UnconstrainedSolve<ceres::examples::TestProblem5>();
+ std::cout << "Test problem 7 : "
+ << UnconstrainedSolve<ceres::examples::TestProblem7>();
+ std::cout << "Test problem 9 : "
+ << UnconstrainedSolve<ceres::examples::TestProblem9>();
+
+ std::cout << "Constrained Problems\n";
+ std::cout << "Test problem 3 : "
+ << ConstrainedSolve<ceres::examples::TestProblem3>();
+ std::cout << "Test problem 4 : "
+ << ConstrainedSolve<ceres::examples::TestProblem4>();
+ std::cout << "Test problem 5 : "
+ << ConstrainedSolve<ceres::examples::TestProblem5>();
+ std::cout << "Test problem 7 : "
+ << ConstrainedSolve<ceres::examples::TestProblem7>();
+ std::cout << "Test problem 9 : "
+ << ConstrainedSolve<ceres::examples::TestProblem9>();
return 0;
}
