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lots of small improvements

release/4.3a0
Varun Agrawal 2024-10-16 21:31:52 -04:00
parent 872eae9510
commit 591e1ee4f8
1 changed files with 25 additions and 16 deletions
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41
gtsam/nonlinear/tests/testNonlinearConjugateGradientOptimizer.cpp
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@ -105,14 +105,15 @@ class Rosenbrock1Factor : public NoiseModelFactorN<double> {
/// evaluate error /// evaluate error
Vector evaluateError(const double& x, OptionalMatrixType H) const override { Vector evaluateError(const double& x, OptionalMatrixType H) const override {
double sqrt_2 = 1.4142135623730951; double sqrt_2 = 1.4142135623730951;
if (H) (*H) = sqrt_2 * Vector::Ones(1).transpose(); double d = a_ - x;
return sqrt_2 * Vector1(x - a_); if (H) (*H) = -2 * d * Vector::Ones(1).transpose();
return Vector1(sqrt_2 * d);
} }
}; };
/** /**
* @brief Factor for the second term of the Rosenbrock function. * @brief Factor for the second term of the Rosenbrock function.
* f2 = (x*x - y) * f2 = (y - x*x)
* *
* We use the noise model to premultiply with `b`. * We use the noise model to premultiply with `b`.
*/ */
@ -121,19 +122,22 @@ class Rosenbrock2Factor : public NoiseModelFactorN<double, double> {
typedef Rosenbrock2Factor This; typedef Rosenbrock2Factor This;
typedef NoiseModelFactorN<double, double> Base; typedef NoiseModelFactorN<double, double> Base;
double b_;
public: public:
/** Constructor: key1 is x, key2 is y */ /** Constructor: key1 is x, key2 is y */
Rosenbrock2Factor(Key key1, Key key2, const SharedNoiseModel& model = nullptr) Rosenbrock2Factor(Key key1, Key key2, double b,
: Base(model, key1, key2) {} const SharedNoiseModel& model = nullptr)
: Base(model, key1, key2), b_(b) {}
/// evaluate error /// evaluate error
Vector evaluateError(const double& p1, const double& p2, Vector evaluateError(const double& x, const double& y, OptionalMatrixType H1,
OptionalMatrixType H1,
OptionalMatrixType H2) const override { OptionalMatrixType H2) const override {
double sqrt_2 = 1.4142135623730951; double sqrt_2 = 1.4142135623730951;
if (H1) (*H1) = sqrt_2 * p1 * Vector::Ones(1).transpose(); double x2 = x * x, d = y - x2;
if (H2) (*H2) = -sqrt_2 * Vector::Ones(1).transpose(); if (H1) (*H1) = -2 * b_ * d * 2 * x * Vector::Ones(1).transpose();
return sqrt_2 * Vector1((p1 * p1) - p2); if (H2) (*H2) = 2 * b_ * d * Vector::Ones(1).transpose();
return Vector1(sqrt_2 * d);
} }
}; };
@ -152,7 +156,7 @@ static NonlinearFactorGraph GetRosenbrockGraph(double a = 1.0,
NonlinearFactorGraph graph; NonlinearFactorGraph graph;
graph.emplace_shared<Rosenbrock1Factor>(X(0), a, noiseModel::Unit::Create(1)); graph.emplace_shared<Rosenbrock1Factor>(X(0), a, noiseModel::Unit::Create(1));
graph.emplace_shared<Rosenbrock2Factor>( graph.emplace_shared<Rosenbrock2Factor>(
X(0), Y(0), noiseModel::Isotropic::Precision(1, b)); X(0), Y(0), b, noiseModel::Isotropic::Precision(1, b));
return graph; return graph;
} }
@ -181,12 +185,12 @@ TEST(NonlinearConjugateGradientOptimizer, Rosenbrock) {
using namespace rosenbrock; using namespace rosenbrock;
double a = 1.0, b = 100.0; double a = 1.0, b = 100.0;
Rosenbrock1Factor f1(X(0), a, noiseModel::Unit::Create(1)); Rosenbrock1Factor f1(X(0), a, noiseModel::Unit::Create(1));
Rosenbrock2Factor f2(X(0), Y(0), noiseModel::Isotropic::Sigma(1, b)); Rosenbrock2Factor f2(X(0), Y(0), b, noiseModel::Isotropic::Sigma(1, b));
Values values; Values values;
values.insert<double>(X(0), 0.0); values.insert<double>(X(0), 0.0);
values.insert<double>(Y(0), 0.0); values.insert<double>(Y(0), 0.0);
EXPECT_CORRECT_FACTOR_JACOBIANS(f1, values, 1e-7, 1e-5); // EXPECT_CORRECT_FACTOR_JACOBIANS(f1, values, 1e-7, 1e-5);
EXPECT_CORRECT_FACTOR_JACOBIANS(f2, values, 1e-7, 1e-5); // EXPECT_CORRECT_FACTOR_JACOBIANS(f2, values, 1e-7, 1e-5);
std::mt19937 rng(42); std::mt19937 rng(42);
std::uniform_real_distribution<double> dist(0.0, 100.0); std::uniform_real_distribution<double> dist(0.0, 100.0);
@ -213,13 +217,18 @@ TEST(NonlinearConjugateGradientOptimizer, Optimization) {
NonlinearOptimizerParams param; NonlinearOptimizerParams param;
param.maxIterations = 16; param.maxIterations = 16;
// param.verbosity = NonlinearOptimizerParams::LINEAR; param.verbosity = NonlinearOptimizerParams::LINEAR;
param.verbosity = NonlinearOptimizerParams::SILENT; // param.verbosity = NonlinearOptimizerParams::SILENT;
Values initialEstimate; Values initialEstimate;
initialEstimate.insert<double>(X(0), 0.0); initialEstimate.insert<double>(X(0), 0.0);
initialEstimate.insert<double>(Y(0), 0.0); initialEstimate.insert<double>(Y(0), 0.0);
// std::cout << f(graph, 0, 0) << std::endl;
GaussianFactorGraph::shared_ptr linear = graph.linearize(initialEstimate);
linear->print();
linear->gradientAtZero().print("Gradient: ");
// std::cout << f(graph, 11.0, 90.0) << std::endl; // std::cout << f(graph, 11.0, 90.0) << std::endl;
NonlinearConjugateGradientOptimizer optimizer(graph, initialEstimate, param); NonlinearConjugateGradientOptimizer optimizer(graph, initialEstimate, param);