drake::solvers::test Namespace Reference

Functions
void	CheckConstraintEvalNonsymbolic (const Constraint &constraint, const Eigen::Ref< const AutoDiffVecXd > &x_autodiff, double tol)
	Compare the result between Eval<double>() and Eval<AutoDiffXd>(). More...
void	CheckGradientSparsityPattern (const EvaluatorBase &evaluator, const AutoDiffVecXd &x_ad, bool strict=false)
	Checks the gradient sparsity pattern, by evaluating evaluator at x_ad, and compare whether the gradient matches with evaluator.gradient_sparsity_pattern(). More...

Function Documentation

CheckConstraintEvalNonsymbolic()

void drake::solvers::test::CheckConstraintEvalNonsymbolic	(	const Constraint &	constraint,
		const Eigen::Ref< const AutoDiffVecXd > &	x_autodiff,
		double	tol
	)

Compare the result between Eval<double>() and Eval<AutoDiffXd>().

Also compare the gradient in Eval<AutoDiffXd>() with a finite difference approximation.

Parameters

constraint	The constraint object to test.
x_autodiff	The point at which the Eval() methods are tested.
tol	Tolerance on the comparison of the results from Eval<double>() and Eval<AutoDiffXd>(). The tolerance on the comparison between the autodiff gradient and the finite difference approximation is sqrt(tolerance) to account for approximation error.

CheckGradientSparsityPattern()

void drake::solvers::test::CheckGradientSparsityPattern	(	const EvaluatorBase &	evaluator,
		const AutoDiffVecXd &	x_ad,
		bool	strict = false
	)

Checks the gradient sparsity pattern, by evaluating evaluator at x_ad, and compare whether the gradient matches with evaluator.gradient_sparsity_pattern().

Notice that evaluator.gradients_sparsity_pattern() contains all the (row, col) indices where the gradient might be non-zero, so evaluator.gradients_sparsity_pattern() is a superset of all the non-zero gradient indices. If strict=true, then we check if evaluator.gradients_sparsity_pattern() is exactly the set of all the non-zero gradient_indices.