MCP_Solvers (functions)


siconos.numerics.mcp_FischerBurmeister(MixedComplementarityProblem *problem, array_like (np.float64, 1D)z, array_like (np.float64, 1D)w, int *info, SolverOptions *options) → None[source]

Fischer Burmeister solver.

Parameters:
  • problem – a structure which represents the MCP
  • z – a m+n-vector, initial solution + returns the solution of the problem.
  • w – not used
  • info – termination value: 0 if success else >0.
  • options – structure used to define the solver and its parameters.

siconos.numerics.mcp_driver_init(MixedComplementarityProblem *problem, SolverOptions *options) → None[source]

Initialisation of the MCP solver (set problem, allocate working memory and so on.

This routine must be called before any attempt to run the mcp_driver.

Parameters:
  • problem – the description of the MCP
  • options – for the solver

siconos.numerics.mcp_driver_reset(MixedComplementarityProblem *problem, SolverOptions *options) → None[source]

Reset of the MCP solver.

Parameters:
  • problem – the description of the MCP
  • options – for the solver

siconos.numerics.mcp_newton_FBLSA(MixedComplementarityProblem2 *problem, array_like (np.float64, 1D)z, array_like (np.float64, 1D)Fmcp, int *info, SolverOptions *options) → None[source]

Solver based on Fischer-Burmeister reformulation and line search (VFBLSA in Facchinei–Pang 2003 p.

Parameters:
  • problem – a structure which represents the MCP
  • z – a n1+n2-vector, initial solution + returns the solution of the problem.
  • Fmcp – n1+n2-vector which contains the value of Fmcp(z) = (G(z), H(z))
  • info – termination value: 0 if success else >0.
  • options – structure used to define the solver and its parameters.

siconos.numerics.mcp_newton_minFBLSA(MixedComplementarityProblem2 *problem, array_like (np.float64, 1D)z, array_like (np.float64, 1D)Fmcp, int *info, SolverOptions *options) → None[source]

Solver based on Fischer-Burmeister reformulation and line search.

The descent direction is found using a min reformulation (minFBLSA in Facchinei –Pang 2003 p. 855)

Parameters:
  • problem – a structure which represents the MCP
  • z – a n1+n2-vector, initial solution + returns the solution of the problem.
  • Fmcp – n1+n2-vector which contains the value of Fmcp(z) = (G(z), H(z))
  • info – termination value: 0 if success else >0.
  • options – structure used to define the solver and its parameters.

siconos.numerics.mixedComplementarity_FB_setDefaultSolverOptions(MixedComplementarityProblem *problem, SolverOptions *pSolver) → int[source]

set the default solver parameters and perform memory allocation for MixedLinearComplementarity

Parameters:
  • problem – the pointer to the array of options to set.
  • pSolver – the pointer to the SolverOptions stucture.

siconos.numerics.mixedComplementarity_default_setDefaultSolverOptions(MixedComplementarityProblem *problem, SolverOptions *pOptions) → None[source]

set the default solver parameters and perform memory allocation for MixedLinearComplementarity

Parameters:
  • problem – the pointer to the array of options to set.
  • pOptions – the pointer to the SolverOptions stucture.

siconos.numerics.mixedComplementarity_setDefaultSolverOptions(MixedComplementarityProblem *problem, SolverOptions *pOptions) → int[source]

set the default solver parameters and perform memory allocation for MixedLinearComplementarity

Parameters:
  • problem – the pointer to the array of options to set.
  • pOptions – the pointer to the SolverOptions stucture.