NMath Reference Guide

## IActive |

Class IActiveSetQPSolver is an interface for classes that solver convex
quadratic programming (QP) problems.
In particular, classes that implement this abstract class may be used
in the Active Set Sequential Quadratic Programming solver for general linear
programming problems.

Inheritance Hierarchy

Syntax

The IActiveSetQPSolver type exposes the following members.

Constructors

Name | Description | |
---|---|---|

IActiveSetQPSolver | Initializes a new instance of the IActiveSetQPSolver class |

Properties

Name | Description | |
---|---|---|

ActiveSet | Gets the set of active constraint Indices for the solution. If algorithm did not converge it returns these Indices for the final iteration. | |

Iterations | The number of iterations performed before the algorithm terminated. | |

LagrangeMultiplier | Gets the values of the Lagrange multipliers for the solution if the algorithm converged. If it did not converge it returns the values of the Lagrange multiplier for the final iteration. | |

MaxIterations | Gets and sets the maximum number of iterations to perform. | |

MaxSeconds | Gets and sets the maximum number of seconds to spend in the inequality constrained QP solver. | |

OptimalObjectiveFunctionValue | If the solver was successful, OptimalObjectiveFunctionValue returns the minimum value of the objective function. | |

OptimalX | If the solver was successful, OptimalX returns the point at which the objective function is minimized. | |

Status | Gets the status of the solver for the most recent solution attempt. |

Methods

Name | Description | |
---|---|---|

Solve(QuadraticProgrammingProblem) | Solves the given convex quadratic programming problem. | |

Solve(QuadraticProgrammingProblem, DoubleVector) | Solves the given convex quadratic programming problem. |

Remarks

QP problems are of the form:

Minimize 0.5*x'Hx + x'c

Subject to

ai'x = bi, for i in E,

ai'x >= bi, for i in I

where H is a symmetric matrix (sometimes called the Hessian) and E and I are finite sets of Indices, using an active-set method. This method is applicable only to convex problems, in which the matrix H is positive semidefinite.

Minimize 0.5*x'Hx + x'c

Subject to

ai'x = bi, for i in E,

ai'x >= bi, for i in I

where H is a symmetric matrix (sometimes called the Hessian) and E and I are finite sets of Indices, using an active-set method. This method is applicable only to convex problems, in which the matrix H is positive semidefinite.

See Also