def get_solver_name(mip=False, qp=False):
    """Select a solver for a given optimization problem.

    Parameters
    ----------
    mip : bool
        Does the solver require mixed integer linear programming capabilities?
    qp : bool
        Does the solver require quadratic programming capabilities?

    Returns
    -------
    string
        The name of feasible solver.

    Raises
    ------
    SolverNotFound
        If no suitable solver could be found.
    """
    if len(solvers) == 0:
        raise SolverNotFound("no solvers installed")
    # Those lists need to be updated as optlang implements more solvers
    mip_order = ["gurobi", "cplex", "glpk"]
    lp_order = ["glpk", "cplex", "gurobi"]
    qp_order = ["gurobi", "cplex"]

    if mip is False and qp is False:
        for solver_name in lp_order:
            if solver_name in solvers:
                return solver_name
        # none of them are in the list order - so return the first one
        return list(solvers)[0]
    elif qp:  # mip does not yet matter for this determination
        for solver_name in qp_order:
            if solver_name in solvers:
                return solver_name
        raise SolverNotFound("no qp-capable solver found")
    else:
        for solver_name in mip_order:
            if solver_name in solvers:
                return solver_name
    raise SolverNotFound("no mip-capable solver found")
def get_solver_name(mip=False, qp=False):
    """Select a solver for a given optimization problem.

    Parameters
    ----------
    mip : bool
        Does the solver require mixed integer linear programming capabilities?
    qp : bool
        Does the solver require quadratic programming capabilities?

    Returns
    -------
    string
        The name of feasible solver.

    Raises
    ------
    SolverNotFound
        If no suitable solver could be found.
    """
    if len(solvers) == 0:
        raise SolverNotFound("no solvers installed")
    # Those lists need to be updated as optlang implements more solvers
    mip_order = ["gurobi", "cplex", "glpk"]
    lp_order = ["glpk", "cplex", "gurobi"]
    qp_order = ["gurobi", "cplex"]

    if mip is False and qp is False:
        for solver_name in lp_order:
            if solver_name in solvers:
                return solver_name
        # none of them are in the list order - so return the first one
        return list(solvers)[0]
    elif qp:  # mip does not yet matter for this determination
        for solver_name in qp_order:
            if solver_name in solvers:
                return solver_name
        raise SolverNotFound("no qp-capable solver found")
    else:
        for solver_name in mip_order:
            if solver_name in solvers:
                return solver_name
    raise SolverNotFound("no mip-capable solver found")
def get_solver_name ( mip = False , qp = False ) : if len ( solvers ) == 0 : raise SolverNotFound ( "no solvers installed" ) # Those lists need to be updated as optlang implements more solvers mip_order = [ "gurobi" , "cplex" , "glpk" ] lp_order = [ "glpk" , "cplex" , "gurobi" ] qp_order = [ "gurobi" , "cplex" ] if mip is False and qp is False : for solver_name in lp_order : if solver_name in solvers : return solver_name # none of them are in the list order - so return the first one return list ( solvers ) [ 0 ] elif qp : # mip does not yet matter for this determination for solver_name in qp_order : if solver_name in solvers : return solver_name raise SolverNotFound ( "no qp-capable solver found" ) else : for solver_name in mip_order : if solver_name in solvers : return solver_name raise SolverNotFound ( "no mip-capable solver found" )
Select a solver for a given optimization problem.

    Parameters
    ----------
    mip : bool
        Does the solver require mixed integer linear programming capabilities?
    qp : bool
        Does the solver require quadratic programming capabilities?

    Returns
    -------
    string
        The name of feasible solver.

    Raises
    ------
    SolverNotFound
        If no suitable solver could be found.
Select a solver for a given optimization problem .