Use Ridders' method in GradientChecker.
Using Ridders' method gives orders of magnitude more accuracy compared
to central differences. This will make things slower, but this is
primarily a testing/debugging feature and the speed hit is not a
concern. This should also reduce the false positive rates when users
enable check_gradients. This is reflected the increased sensitivity of
the tests for GradientChecker.
https://github.com/ceres-solver/ceres-solver/issues/554
Change-Id: I6b871c72df55be1c31175ba062cf3c1e94e4b662
diff --git a/internal/ceres/gradient_checker.cc b/internal/ceres/gradient_checker.cc
index 411a67f..ef56666 100644
--- a/internal/ceres/gradient_checker.cc
+++ b/internal/ceres/gradient_checker.cc
@@ -130,9 +130,9 @@
local_parameterizations_.resize(function->parameter_block_sizes().size(),
NULL);
}
- DynamicNumericDiffCostFunction<CostFunction, CENTRAL>*
+ DynamicNumericDiffCostFunction<CostFunction, RIDDERS>*
finite_diff_cost_function =
- new DynamicNumericDiffCostFunction<CostFunction, CENTRAL>(
+ new DynamicNumericDiffCostFunction<CostFunction, RIDDERS>(
function, DO_NOT_TAKE_OWNERSHIP, options);
finite_diff_cost_function_.reset(finite_diff_cost_function);
diff --git a/internal/ceres/gradient_checker_test.cc b/internal/ceres/gradient_checker_test.cc
index cd194a7..b2dd35e 100644
--- a/internal/ceres/gradient_checker_test.cc
+++ b/internal/ceres/gradient_checker_test.cc
@@ -48,6 +48,7 @@
namespace internal {
using std::vector;
+const double kTolerance = 1e-12;
// We pick a (non-quadratic) function whose derivative are easy:
//
@@ -154,7 +155,7 @@
if (jacobians[j]) {
for (int u = 0; u < parameter_block_sizes()[j]; ++u) {
// See comments before class.
- jacobians[j][u] = -f * a_[j][u] + 0.001;
+ jacobians[j][u] = -f * a_[j][u] + kTolerance;
}
}
}
@@ -168,7 +169,7 @@
vector<vector<double>> a_; // our vectors.
};
-const double kTolerance = 1e-6;
+
static void CheckDimensions(const GradientChecker::ProbeResults& results,
const std::vector<int>& parameter_sizes,
