# Solvers definition (numerics)¶

To define a non-smooth problem in Numerics, the structure SolverOptions is used. It handles the name of the solver and its input-output parameters.

SolverOptions main components are:
• a name
• two lists of input-output parameters (int: iparam, double: dparam) and their sizes

Check each type of formulation of the problem to find which solvers are available and what are the required parameters, see for example LCP solvers or Friction-Contact solvers.

As an example, consider a Linear Complementarity Problem : M is a NumericsMatrix and can be saved as a double* or as a SparseBlockStructuredMatrix. One needs to define a SolverOptions, say “options”, by choosing one solver among those given in LCP solvers and set:

int nbSolvers = 1;
SolverOptions options;
strcpy(options.solverName,"PGS");
int iparam[2] ={maxIter, 0};
double dparam[2] = {tolerance,0.0};
options.iSize = 2;
options.dSize = 2;
options.iparam = iparam;
options.dparam = dparam;
options.isSet = 1;


And then call the driver:

int info = lcp_driver(myProblem, z,w, &options, nbSolvers, &global_options);


which will result in the resolution of the LCP defined in myProblem thanks to a PGS solver.

On the other side if M is saved as a SparseBlockStructuredMatrix, with N rows of blocks, one needs to used a “block-solver” with possibly one or more specific local solver dedicated to each local problem. In that case options must be a vector of SolverOptions, with:

• options[0] the definition for the global “block” solver
• options[i], i>0, the solvers used for each local problem.

Example with a LCP:

// First define a vector of options
int nbSolvers = 3;
SolverOptions options[nbSolvers];

// The global solver:
strcpy(options[0].solverName,"GaussSeidel_SBM");
int iparam[2] ={maxIter, 0};
double dparam[2] = {tolerance,0.0};
options[0].iSize = 2;
options[0].dSize = 2;
options[0].iparam = iparam;
options[0].dparam = dparam;
options[0].isSet = 1;

// The local solvers:
strcpy(options[1].solverName,"PGS");
int iparam[2] ={maxIter, 0};
double dparam[2] = {tolerance,0.0};
options[1].iSize = 2;
options[1].dSize = 2;
options[1].iparam = iparam;
options[1].dparam = dparam;
options[1].isSet = 1;
strcpy(options[2].solverName,"Lemke");
int iparam[2] ={maxIter,0};
double dparam[2] = {tolerance,0.0};
options[2].iSize = 2;
options[2].dSize = 2;
options[2].iparam = iparam;
options[2].dparam = dparam;
options[2].isSet = 1;


The call of the driver remains the same:

int info = lcp_driver(myProblem, z,w, options,nbSolvers, &global_options);


In this case, if the matrix M has N rows of blocks, the global problem will be solved thanks to the Gauss-Seidel block solver, with the first local problem (first row) solved thanks to a PGS and the others with a Lemke. Note that options[i+1] is used for row i of M, while i<nbSolvers-1 and options[nbSolvers-1] for row i when i>=nbSolvers.