#include <GradientDescentSolver.hpp>
◆ Properties()
◆ mDefaultConstraintWeight
| double dart::optimizer::GradientDescentSolver::UniqueProperties::mDefaultConstraintWeight |
|
inherited |
This is the weight that will be applied to any constraints that do not have a corresponding weight specified by mEqConstraintWeights or by mIneqConstraintWeights.
◆ mEqConstraintWeights
| Eigen::VectorXd dart::optimizer::GradientDescentSolver::UniqueProperties::mEqConstraintWeights |
|
inherited |
Vector of weights that should be applied to the equality constraints.
If there are fewer components in this vector than there are equality constraints in the Problem, then the remaining equality constraints will be assigned a weight of mDefaultConstraintWeight.
◆ mIneqConstraintWeights
| Eigen::VectorXd dart::optimizer::GradientDescentSolver::UniqueProperties::mIneqConstraintWeights |
|
inherited |
Vector of weights that should be applied to the inequality constraints.
If there are fewer components in this vector than there are inequality constraints in the Problem, then the remaining inequality constraints will be assigned a weight of mDefaultConstraintWeight.
◆ mIterationsPerPrint
| std::size_t dart::optimizer::Solver::Properties::mIterationsPerPrint |
|
inherited |
How many iterations between printing the Solver's progress to the terminal.
Use 0 for no printing.
◆ mMaxAttempts
| std::size_t dart::optimizer::GradientDescentSolver::UniqueProperties::mMaxAttempts |
|
inherited |
Number of attempts to make before quitting.
Each attempt will start from the next seed provided by the problem. Once there are no more seeds, random starting configurations will be used.
Set this to 0 to keep trying until a solution is found (the program will need to be interrupted in order to stop if no solution is being found).
◆ mMaxPerturbationFactor
| double dart::optimizer::GradientDescentSolver::UniqueProperties::mMaxPerturbationFactor |
|
inherited |
The random perturbation works as follows: A random point in the domain of the Problem is selected, and then a random step size between 0 and mMaxPerturbationFactor is selected.
The configuration will take a step of that random step size towards the random point.
A maximum value of 1.0 is recommended for mMaxPerturbationFactor. A smaller value will result in smaller randomized perturbations. A value significantly larger than 1.0 could bias the configuration towards the boundary of the Problem domain.
◆ mMaxRandomizationStep
| double dart::optimizer::GradientDescentSolver::UniqueProperties::mMaxRandomizationStep |
|
inherited |
The largest permittable change in value when randomizing a configuration.
◆ mNumMaxIterations
| std::size_t dart::optimizer::Solver::Properties::mNumMaxIterations |
|
inherited |
The maximum number of iterations that the solver should use.
Use 0 for infinite iterations.
◆ mOutStream
| std::ostream* dart::optimizer::Solver::Properties::mOutStream |
|
inherited |
Stream for printing the Solver's progress. Default is std::cout.
◆ mPerturbationStep
| std::size_t dart::optimizer::GradientDescentSolver::UniqueProperties::mPerturbationStep |
|
inherited |
The number of steps between random perturbations being applied to the configuration.
Set this to 0 to never apply randomized perturbations.
◆ mPrintFinalResult
| bool dart::optimizer::Solver::Properties::mPrintFinalResult |
|
inherited |
Set to true if the final result should be printed to the terminal.
◆ mProblem
| std::shared_ptr<Problem> dart::optimizer::Solver::Properties::mProblem |
|
inherited |
Nonlinear optimization Problem to be solved.
◆ mResultFile
| std::string dart::optimizer::Solver::Properties::mResultFile |
|
inherited |
Publish the results of the optimization to a file.
Leave this string empty to avoid publishing anything.
◆ mStepSize
| double dart::optimizer::GradientDescentSolver::UniqueProperties::mStepSize |
|
inherited |
Value of the fixed step size.
◆ mTolerance
| double dart::optimizer::Solver::Properties::mTolerance |
|
inherited |
The relative tolerance on the optimization parameters.
For example, the distance between the last parameters and the current parameters to be considered converged.
