Пример #1
0
  void
  BonCbcFullNodeInfo::registerOptions(Ipopt::SmartPtr<Bonmin::RegisteredOptions> roptions)
  {


    roptions->SetRegisteringCategory("Nonconvex problems", RegisteredOptions::BonminCategory);
    roptions->AddLowerBoundedIntegerOption("max_consecutive_infeasible",
        "Number of consecutive infeasible subproblems before aborting a"
        " branch.",
        0,0,
        "Will continue exploring a branch of the tree until \"max_consecutive_infeasible\""
        "consecutive problems are locally infeasible by the NLP sub-solver.");
    roptions->setOptionExtraInfo("max_consecutive_infeasible",8);

    roptions->SetRegisteringCategory("NLP solution robustness", RegisteredOptions::BonminCategory);
    roptions->AddLowerBoundedIntegerOption
    ("max_consecutive_failures",
     "(temporarily removed) Number $n$ of consecutive unsolved problems before aborting a branch of the tree.",
     0,10,
     "When $n > 0$, continue exploring a branch of the tree until $n$ "
     "consecutive problems in the branch are unsolved (we call unsolved a problem for which Ipopt can not "
     "guarantee optimality within the specified tolerances).");
    roptions->setOptionExtraInfo("max_consecutive_failures",8);

  }
Пример #2
0
 void
 LpBranchingSolver::registerOptions(Ipopt::SmartPtr<Bonmin::RegisteredOptions> roptions)
 {
   roptions->SetRegisteringCategory("ECP based strong branching",RegisteredOptions::UndocumentedCategory);
   roptions->AddLowerBoundedIntegerOption
   ("ecp_max_rounds_strong",
    "Set the maximal number of rounds of ECP cuts in strong branching.",
    0,0,
    "");
   roptions->setOptionExtraInfo("ecp_max_rounds_strong",63);
   roptions->AddLowerBoundedNumberOption
   ("ecp_abs_tol_strong",
    "Set the absolute termination tolerance for ECP rounds in strong branching.",
    0,false,1e-6,
    "");
   roptions->setOptionExtraInfo("ecp_abs_tol_strong",63);
   roptions->AddLowerBoundedNumberOption
   ("ecp_rel_tol_strong",
    "Set the relative termination tolerance for ECP rounds in strong branching.",
    0,false,1e-1,
    "");
   roptions->setOptionExtraInfo("ecp_rel_tol_strong",63);
   roptions->AddStringOption2
   ("lp_strong_warmstart_method",
    "Choose method to use for warm starting lp in strong branching",
    "Basis",
    "Basis", "Use optimal basis of node",
    "Clone", "Clone optimal problem of node",
    "(Advanced stuff)");
   roptions->setOptionExtraInfo("lp_strong_warmstart_method",63);
 }
Пример #3
0
  /** Register OA feasibility checker  options.*/
  void
  OaFeasibilityChecker::registerOptions(Ipopt::SmartPtr<Bonmin::RegisteredOptions> roptions)
  {
    roptions->SetRegisteringCategory("Feasibility checker using OA cuts", RegisteredOptions::BonminCategory);
    roptions->AddStringOption2("feas_check_cut_types", "Choose the type of cuts generated when an integer feasible solution is found",
                               "outer-approx",
                               "outer-approx", "Generate a set of Outer Approximations cuts.",
                               "Benders", "Generate a single Benders cut.",
                               "If it seems too much memory is used should try Benders to use less");
    roptions->setOptionExtraInfo("feas_check_cut_types", 19);
    

    roptions->AddStringOption3("feas_check_discard_policy", "How cuts from feasibility checker are discarded",
                               "detect-cycles",
                               "detect-cycles", "Detect if a cycle occurs and only in this case force not to discard.",
                               "keep-all", "Force cuts from feasibility checker not to be discarded (memory hungry but sometimes better).",
                               "treated-as-normal", "Cuts from memory checker can be discarded as any other cuts (code may cycle then)",
                               "Normally to avoid cycle cuts from feasibility checker should not be discarded in the node where they are generated. "
                               "However Cbc sometimes does it if no care is taken which can lead to an infinite loop in Bonmin (usually on simple problems). "
                               "To avoid this one can instruct Cbc to never discard a cut but if we do that for all cuts it can lead to memory problems. "
                               "The default policy here is to detect cycles and only then impose to Cbc to keep the cut. "
                               "The two other alternative are to instruct Cbc to keep all cuts or to just ignore the problem and hope for the best");
    roptions->setOptionExtraInfo("feas_check_discard_policy", 19);

    roptions->AddLowerBoundedIntegerOption("generate_benders_after_so_many_oa", "Specify that after so many oa cuts have been generated Benders cuts should be generated instead.",
                                           0, 5000,
                                           "It seems that sometimes generating too many oa cuts slows down the optimization compared to Benders due to the size of the LP. "
                                           "With this option we specify that after so many OA cuts have been generated we should switch to Benders cuts.");
    roptions->setOptionExtraInfo("generate_benders_after_so_many_oa", 19);
  }
Пример #4
0
  void
  BonNWayChoose::registerOptions(Ipopt::SmartPtr<Bonmin::RegisteredOptions> roptions)
  {
    roptions->SetRegisteringCategory("NWay Strong branching setup", RegisteredOptions::BonminCategory);
    roptions->AddLowerBoundedIntegerOption("nway_branch_log_level",
                                           "Log level for the branching on nways",
                                           0,1,
                                           "");

    roptions->AddLowerBoundedIntegerOption("strong_branch_depth",
                                           "To which level do we perform strong-branching",
                                           0,0,
                                           "");

    roptions->AddLowerBoundedNumberOption("cutoff_multiplier",
                                           "multiplier applied to cutoff_ for computing pseudo-cost of infeasible sub-problems",
                                           1.,0,3.,
                                           "");

    roptions->AddLowerBoundedNumberOption("pseudocost_trust_value",
                                           "Trust pseudo cost of best nway object if it is above this value",
                                           0.,0,0,
                                           "");

    roptions->AddStringOption2("use_geo_means", "Use geometrical means to average pseudo-costs",
                               "yes", 
                               "no", "Use artihmetical means",
                               "yes", "Use geometrical means","");

    roptions->AddStringOption4("do_fixings",
        "Do we fix variables in strong branching?",
        "all",
        "none", "Don't do any.",
        "in-tree", "Fix only variables in the tree",
        "strong-branching", "Fix variable in strong branching only",
        "all", "Fix whenever possible",
        "");


  }
Пример #5
0
  void RobotSetup::registerAllOptions(Ipopt::SmartPtr<Bonmin::RegisteredOptions> roptions)
  {
     BonminSetup::registerAllOptions(roptions);
     BonNWayChoose::registerOptions(roptions);


    roptions->AddLowerBoundedIntegerOption("branch_on_frac_only",
        "Starting at given depth branch on the subset of fractional variables (and set the last branch that one of them is 1)",
        0,INT_MAX,"");

    roptions->AddStringOption2("do_a_quick_one",
        "Do we try our luck?",
        "no",
        "no", "Don't (of course).",
        "yes", "Be crazy",
        "");

  }
void HeuristicInnerApproximation::registerOptions(Ipopt::SmartPtr<
    Bonmin::RegisteredOptions> roptions) {
  roptions->SetRegisteringCategory("Initial Approximations descriptions",
      Bonmin::RegisteredOptions::UndocumentedCategory);
  roptions->AddStringOption2("heuristic_inner_approximation",
      "if yes runs the InnerApproximation heuristic", "yes", "no",
      "don't run it", "yes", "runs the heuristic", "");

 roptions->setOptionExtraInfo("heuristic_inner_approximation", 63);

 roptions->AddLowerBoundedIntegerOption("number_inner_approximation_points",
       "Set the number of points to use for linear inner approximation of nonlinear functions in heuristic",
       1, 20);
 roptions->setOptionExtraInfo("number_inner_approximation_points", 63);

 roptions->AddLowerBoundedNumberOption("inner_time_limit",
       "Time limit for inner approximation",
       0, true, 10, "");
 roptions->setOptionExtraInfo("number_inner_approximation_points", 63);
}