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ipopt_options

PURPOSE ^

IPOPT_OPTIONS Sets options for IPOPT.

SYNOPSIS ^

function opt = ipopt_options(overrides, mpopt)

DESCRIPTION ^

IPOPT_OPTIONS  Sets options for IPOPT.

   OPT = IPOPT_OPTIONS
   OPT = IPOPT_OPTIONS(OVERRIDES)
   OPT = IPOPT_OPTIONS(OVERRIDES, FNAME)
   OPT = IPOPT_OPTIONS(OVERRIDES, MPOPT)

   Sets the values for the options.ipopt struct normally passed to
   IPOPT.

   Inputs are all optional, second argument must be either a string
   (FNAME) or a struct (MPOPT):

       OVERRIDES - struct containing values to override the defaults
       FNAME - name of user-supplied function called after default
           options are set to modify them. Calling syntax is:
                   MODIFIED_OPT = FNAME(DEFAULT_OPT);
       MPOPT - MATPOWER options struct, uses the following fields:
           opf.violation   - used to set opt.constr_viol_tol
           verbose         - used to opt.print_level
           ipopt.opts      - struct containing values to use as OVERRIDES
           ipopt.opt_fname - name of user-supplied function used as FNAME,
               except with calling syntax:
                   MODIFIED_OPT = FNAME(DEFAULT_OPT, MPOPT);
           ipopt.opt       - numbered user option function, if and only if
               ipopt.opt_fname is empty and ipopt.opt is non-zero, the value
               of ipopt.opt_fname is generated by appending ipopt.opt to
               'ipopt_user_options_' (for backward compatibility with old
               MATPOWER option IPOPT_OPT).

   Output is an options.ipopt struct to pass to IPOPT.

   There are multiple ways of providing values to override the default
   options. Their precedence and order of application are as follows:

   With inputs OVERRIDES and FNAME
       1. FNAME is called
       2. OVERRIDES are applied
   With inputs OVERRIDES and MPOPT
       1. FNAME (from ipopt.opt_fname or ipopt.opt) is called
       2. ipopt.opts (if not empty) are applied
       3. OVERRIDES are applied

   Example:

   If ipopt.opt = 3, then after setting the default IPOPT options,
   IPOPT_OPTIONS will execute the following user-defined function
   to allow option overrides:

       opt = ipopt_user_options_3(opt, mpopt);

   The contents of ipopt_user_options_3.m, could be something like:

       function opt = ipopt_user_options_3(opt, mpopt)
       opt.nlp_scaling_method = 'none';
       opt.max_iter           = 500;
       opt.derivative_test    = 'first-order';

   See the options reference section in the IPOPT documentation for
   details on the available options.

       http://www.coin-or.org/Ipopt/documentation/

   See also IPOPT, MPOPTION.

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function opt = ipopt_options(overrides, mpopt)
0002 %IPOPT_OPTIONS  Sets options for IPOPT.
0003 %
0004 %   OPT = IPOPT_OPTIONS
0005 %   OPT = IPOPT_OPTIONS(OVERRIDES)
0006 %   OPT = IPOPT_OPTIONS(OVERRIDES, FNAME)
0007 %   OPT = IPOPT_OPTIONS(OVERRIDES, MPOPT)
0008 %
0009 %   Sets the values for the options.ipopt struct normally passed to
0010 %   IPOPT.
0011 %
0012 %   Inputs are all optional, second argument must be either a string
0013 %   (FNAME) or a struct (MPOPT):
0014 %
0015 %       OVERRIDES - struct containing values to override the defaults
0016 %       FNAME - name of user-supplied function called after default
0017 %           options are set to modify them. Calling syntax is:
0018 %                   MODIFIED_OPT = FNAME(DEFAULT_OPT);
0019 %       MPOPT - MATPOWER options struct, uses the following fields:
0020 %           opf.violation   - used to set opt.constr_viol_tol
0021 %           verbose         - used to opt.print_level
0022 %           ipopt.opts      - struct containing values to use as OVERRIDES
0023 %           ipopt.opt_fname - name of user-supplied function used as FNAME,
0024 %               except with calling syntax:
0025 %                   MODIFIED_OPT = FNAME(DEFAULT_OPT, MPOPT);
0026 %           ipopt.opt       - numbered user option function, if and only if
0027 %               ipopt.opt_fname is empty and ipopt.opt is non-zero, the value
0028 %               of ipopt.opt_fname is generated by appending ipopt.opt to
0029 %               'ipopt_user_options_' (for backward compatibility with old
0030 %               MATPOWER option IPOPT_OPT).
0031 %
0032 %   Output is an options.ipopt struct to pass to IPOPT.
0033 %
0034 %   There are multiple ways of providing values to override the default
0035 %   options. Their precedence and order of application are as follows:
0036 %
0037 %   With inputs OVERRIDES and FNAME
0038 %       1. FNAME is called
0039 %       2. OVERRIDES are applied
0040 %   With inputs OVERRIDES and MPOPT
0041 %       1. FNAME (from ipopt.opt_fname or ipopt.opt) is called
0042 %       2. ipopt.opts (if not empty) are applied
0043 %       3. OVERRIDES are applied
0044 %
0045 %   Example:
0046 %
0047 %   If ipopt.opt = 3, then after setting the default IPOPT options,
0048 %   IPOPT_OPTIONS will execute the following user-defined function
0049 %   to allow option overrides:
0050 %
0051 %       opt = ipopt_user_options_3(opt, mpopt);
0052 %
0053 %   The contents of ipopt_user_options_3.m, could be something like:
0054 %
0055 %       function opt = ipopt_user_options_3(opt, mpopt)
0056 %       opt.nlp_scaling_method = 'none';
0057 %       opt.max_iter           = 500;
0058 %       opt.derivative_test    = 'first-order';
0059 %
0060 %   See the options reference section in the IPOPT documentation for
0061 %   details on the available options.
0062 %
0063 %       http://www.coin-or.org/Ipopt/documentation/
0064 %
0065 %   See also IPOPT, MPOPTION.
0066 
0067 %   MATPOWER
0068 %   Copyright (c) 2010-2016, Power Systems Engineering Research Center (PSERC)
0069 %   by Ray Zimmerman, PSERC Cornell
0070 %
0071 %   This file is part of MATPOWER.
0072 %   Covered by the 3-clause BSD License (see LICENSE file for details).
0073 %   See http://www.pserc.cornell.edu/matpower/ for more info.
0074 
0075 %%-----  initialization and arg handling  -----
0076 %% defaults
0077 verbose = 2;
0078 fname   = '';
0079 
0080 %% second argument
0081 if nargin > 1 && ~isempty(mpopt)
0082     if ischar(mpopt)        %% 2nd arg is FNAME (string)
0083         fname = mpopt;
0084         have_mpopt = 0;
0085     else                    %% 2nd arg is MPOPT (MATPOWER options struct)
0086         have_mpopt = 1;
0087         verbose = mpopt.verbose;
0088         if isfield(mpopt.ipopt, 'opt_fname') && ~isempty(mpopt.ipopt.opt_fname)
0089             fname = mpopt.ipopt.opt_fname;
0090         elseif mpopt.ipopt.opt
0091             fname = sprintf('ipopt_user_options_%d', mpopt.ipopt.opt);
0092         end
0093     end
0094 else
0095     have_mpopt = 0;
0096 end
0097 
0098 %%-----  set default options for IPOPT  -----
0099 %% printing
0100 if verbose
0101     opt.print_level = min(12, verbose*2+1);
0102 else
0103     opt.print_level = 0;
0104 end
0105 
0106 %% convergence
0107 opt.tol             = 1e-8;                %% default 1e-8
0108 opt.max_iter        = 250;                  %% default 3000
0109 opt.dual_inf_tol    = 0.1;                  %% default 1
0110 if have_mpopt
0111     opt.constr_viol_tol = mpopt.opf.violation;                  %% default 1e-4
0112     opt.acceptable_constr_viol_tol = mpopt.opf.violation*100;   %% default 1e-2
0113 end
0114 opt.compl_inf_tol   = 1e-5;                 %% default 1e-4
0115 opt.acceptable_tol  = 1e-8;                 %% default 1e-6
0116 % opt.acceptable_iter = 15;                   %% default 15
0117 % opt.acceptable_dual_inf_tol     = 1e+10;    %% default 1e+10
0118 opt.acceptable_compl_inf_tol    = 1e-3;     %% default 1e-2
0119 % opt.acceptable_obj_change_tol   = 1e+20;    %% default 1e+20
0120 % opt.diverging_iterates_tol      = 1e+20;    %% default 1e+20
0121 
0122 %% NLP scaling
0123 % opt.nlp_scaling_method  = 'none';           %% default 'gradient-based'
0124 
0125 %% NLP
0126 % opt.fixed_variable_treatment    = 'make_constraint';    %% default 'make_parameter'
0127 % opt.honor_original_bounds       = 'no';                 %% default 'yes'
0128 % opt.check_derivatives_for_naninf = 'yes';               %% default 'no'
0129 
0130 %% initialization
0131 % opt.least_square_init_primal    = 'yes';        %% default 'no'
0132 % opt.least_square_init_duals     = 'yes';        %% default 'no'
0133 
0134 %% barrier parameter update
0135 opt.mu_strategy                 = 'adaptive';   %% default 'monotone'
0136 
0137 %% linear solver
0138 % opt.linear_solver   = 'ma27';
0139 % opt.linear_solver   = 'ma57';
0140 % opt.linear_solver   = 'pardiso';
0141 % opt.linear_solver   = 'wsmp';
0142 % opt.linear_solver   = 'mumps';          %% default 'mumps'
0143 % opt.linear_solver   = 'custom';
0144 % opt.linear_scaling_on_demand    = 'no'; %% default 'yes'
0145 
0146 %% step calculation
0147 % opt.mehrotra_algorithm      = 'yes';    %% default 'no'
0148 % opt.fast_step_computation   = 'yes';    %% default 'no'
0149 
0150 %% restoration phase
0151 % opt.expect_infeasible_problem   = 'yes';    %% default 'no'
0152 
0153 %% derivative checker
0154 % opt.derivative_test         = 'second-order';   %% default 'none'
0155 
0156 %% hessian approximation
0157 % opt.hessian_approximation   = 'limited-memory'; %% default 'exact'
0158 
0159 % ma57 options
0160 %opt.ma57_pre_alloc = 3;
0161 %opt.ma57_pivot_order = 4;
0162 
0163 %%-----  call user function to modify defaults  -----
0164 if ~isempty(fname)
0165     if have_mpopt
0166         opt = feval(fname, opt, mpopt);
0167     else
0168         opt = feval(fname, opt);
0169     end
0170 end
0171 
0172 %%-----  apply overrides  -----
0173 if have_mpopt && isfield(mpopt.ipopt, 'opts') && ~isempty(mpopt.ipopt.opts)
0174     opt = nested_struct_copy(opt, mpopt.ipopt.opts);
0175 end
0176 if nargin > 0 && ~isempty(overrides)
0177     opt = nested_struct_copy(opt, overrides);
0178 end
0179 
0180 
0181 %--------------------------  Options Documentation  --------------------------
0182 % (as printed by IPOPT 3.8)
0183 % ### Output ###
0184 %
0185 % print_level                            0 <= (          5) <= 12
0186 %    Output verbosity level.
0187 %      Sets the default verbosity level for console output. The larger this
0188 %      value the more detailed is the output.
0189 %
0190 % output_file                   ("")
0191 %    File name of desired output file (leave unset for no file output).
0192 %      NOTE: This option only works when read from the ipopt.opt options file!
0193 %      An output file with this name will be written (leave unset for no file
0194 %      output).  The verbosity level is by default set to "print_level", but can
0195 %      be overridden with "file_print_level".  The file name is changed to use
0196 %      only small letters.
0197 %    Possible values:
0198 %     - *                       [Any acceptable standard file name]
0199 %
0200 % file_print_level                       0 <= (          5) <= 12
0201 %    Verbosity level for output file.
0202 %      NOTE: This option only works when read from the ipopt.opt options file!
0203 %      Determines the verbosity level for the file specified by "output_file".
0204 %      By default it is the same as "print_level".
0205 %
0206 % print_user_options            ("no")
0207 %    Print all options set by the user.
0208 %      If selected, the algorithm will print the list of all options set by the
0209 %      user including their values and whether they have been used.  In some
0210 %      cases this information might be incorrect, due to the internal program
0211 %      flow.
0212 %    Possible values:
0213 %     - no                      [don't print options]
0214 %     - yes                     [print options]
0215 %
0216 % print_options_documentation   ("no")
0217 %    Switch to print all algorithmic options.
0218 %      If selected, the algorithm will print the list of all available
0219 %      algorithmic options with some documentation before solving the
0220 %      optimization problem.
0221 %    Possible values:
0222 %     - no                      [don't print list]
0223 %     - yes                     [print list]
0224 %
0225 % print_timing_statistics       ("no")
0226 %    Switch to print timing statistics.
0227 %      If selected, the program will print the CPU usage (user time) for
0228 %      selected tasks.
0229 %    Possible values:
0230 %     - no                      [don't print statistics]
0231 %     - yes                     [print all timing statistics]
0232 %
0233 % option_file_name              ("")
0234 %    File name of options file (to overwrite default).
0235 %      By default, the name of the Ipopt options file is "ipopt.opt" - or
0236 %      something else if specified in the IpoptApplication::Initialize call. If
0237 %      this option is set by SetStringValue BEFORE the options file is read, it
0238 %      specifies the name of the options file.  It does not make any sense to
0239 %      specify this option within the options file.
0240 %    Possible values:
0241 %     - *                       [Any acceptable standard file name]
0242 %
0243 % replace_bounds                ("no")
0244 %    Indicates if all variable bounds should be replaced by inequality
0245 %    constraints
0246 %      This option must be set for the inexact algorithm
0247 %    Possible values:
0248 %     - no                      [leave bounds on variables]
0249 %     - yes                     [replace variable bounds by inequality
0250 %                                constraints]
0251 %
0252 % skip_finalize_solution_call   ("no")
0253 %    Indicates if call to NLP::FinalizeSolution after optimization should be
0254 %    suppressed
0255 %      In some Ipopt applications, the user might want to call the
0256 %      FinalizeSolution method separately.  Setting this option to "yes" will
0257 %      cause the IpoptApplication object to suppress the default call to that
0258 %      method.
0259 %    Possible values:
0260 %     - no                      [call FinalizeSolution]
0261 %     - yes                     [do not call FinalizeSolution]
0262 %
0263 % print_info_string             ("no")
0264 %    Enables printing of additional info string at end of iteration output.
0265 %      This string contains some insider information about the current iteration.
0266 %    Possible values:
0267 %     - no                      [don't print string]
0268 %     - yes                     [print string at end of each iteration output]
0269 %
0270 %
0271 %
0272 % ### Convergence ###
0273 %
0274 % tol                                    0 <  (      1e-08) <  +inf
0275 %    Desired convergence tolerance (relative).
0276 %      Determines the convergence tolerance for the algorithm.  The algorithm
0277 %      terminates successfully, if the (scaled) NLP error becomes smaller than
0278 %      this value, and if the (absolute) criteria according to "dual_inf_tol",
0279 %      "primal_inf_tol", and "cmpl_inf_tol" are met.  (This is epsilon_tol in
0280 %      Eqn. (6) in implementation paper).  See also "acceptable_tol" as a second
0281 %      termination criterion.  Note, some other algorithmic features also use
0282 %      this quantity to determine thresholds etc.
0283 %
0284 % s_max                                  0 <  (        100) <  +inf
0285 %    Scaling threshold for the NLP error.
0286 %      (See paragraph after Eqn. (6) in the implementation paper.)
0287 %
0288 % max_iter                               0 <= (       3000) <  +inf
0289 %    Maximum number of iterations.
0290 %      The algorithm terminates with an error message if the number of
0291 %      iterations exceeded this number.
0292 %
0293 % max_cpu_time                           0 <  (      1e+06) <  +inf
0294 %    Maximum number of CPU seconds.
0295 %      A limit on CPU seconds that Ipopt can use to solve one problem.  If
0296 %      during the convergence check this limit is exceeded, Ipopt will terminate
0297 %      with a corresponding error message.
0298 %
0299 % dual_inf_tol                           0 <  (          1) <  +inf
0300 %    Desired threshold for the dual infeasibility.
0301 %      Absolute tolerance on the dual infeasibility. Successful termination
0302 %      requires that the max-norm of the (unscaled) dual infeasibility is less
0303 %      than this threshold.
0304 %
0305 % constr_viol_tol                        0 <  (     0.0001) <  +inf
0306 %    Desired threshold for the constraint violation.
0307 %      Absolute tolerance on the constraint violation. Successful termination
0308 %      requires that the max-norm of the (unscaled) constraint violation is less
0309 %      than this threshold.
0310 %
0311 % compl_inf_tol                          0 <  (     0.0001) <  +inf
0312 %    Desired threshold for the complementarity conditions.
0313 %      Absolute tolerance on the complementarity. Successful termination
0314 %      requires that the max-norm of the (unscaled) complementarity is less than
0315 %      this threshold.
0316 %
0317 % acceptable_tol                         0 <  (      1e-06) <  +inf
0318 %    "Acceptable" convergence tolerance (relative).
0319 %      Determines which (scaled) overall optimality error is considered to be
0320 %      "acceptable." There are two levels of termination criteria.  If the usual
0321 %      "desired" tolerances (see tol, dual_inf_tol etc) are satisfied at an
0322 %      iteration, the algorithm immediately terminates with a success message.
0323 %      On the other hand, if the algorithm encounters "acceptable_iter" many
0324 %      iterations in a row that are considered "acceptable", it will terminate
0325 %      before the desired convergence tolerance is met. This is useful in cases
0326 %      where the algorithm might not be able to achieve the "desired" level of
0327 %      accuracy.
0328 %
0329 % acceptable_iter                        0 <= (         15) <  +inf
0330 %    Number of "acceptable" iterates before triggering termination.
0331 %      If the algorithm encounters this many successive "acceptable" iterates
0332 %      (see "acceptable_tol"), it terminates, assuming that the problem has been
0333 %      solved to best possible accuracy given round-off.  If it is set to zero,
0334 %      this heuristic is disabled.
0335 %
0336 % acceptable_dual_inf_tol                0 <  (      1e+10) <  +inf
0337 %    "Acceptance" threshold for the dual infeasibility.
0338 %      Absolute tolerance on the dual infeasibility. "Acceptable" termination
0339 %      requires that the (max-norm of the unscaled) dual infeasibility is less
0340 %      than this threshold; see also acceptable_tol.
0341 %
0342 % acceptable_constr_viol_tol             0 <  (       0.01) <  +inf
0343 %    "Acceptance" threshold for the constraint violation.
0344 %      Absolute tolerance on the constraint violation. "Acceptable" termination
0345 %      requires that the max-norm of the (unscaled) constraint violation is less
0346 %      than this threshold; see also acceptable_tol.
0347 %
0348 % acceptable_compl_inf_tol               0 <  (       0.01) <  +inf
0349 %    "Acceptance" threshold for the complementarity conditions.
0350 %      Absolute tolerance on the complementarity. "Acceptable" termination
0351 %      requires that the max-norm of the (unscaled) complementarity is less than
0352 %      this threshold; see also acceptable_tol.
0353 %
0354 % acceptable_obj_change_tol              0 <= (      1e+20) <  +inf
0355 %    "Acceptance" stopping criterion based on objective function change.
0356 %      If the relative change of the objective function (scaled by
0357 %      Max(1,|f(x)|)) is less than this value, this part of the acceptable
0358 %      tolerance termination is satisfied; see also acceptable_tol.  This is
0359 %      useful for the quasi-Newton option, which has trouble to bring down the
0360 %      dual infeasibility.
0361 %
0362 % diverging_iterates_tol                 0 <  (      1e+20) <  +inf
0363 %    Threshold for maximal value of primal iterates.
0364 %      If any component of the primal iterates exceeded this value (in absolute
0365 %      terms), the optimization is aborted with the exit message that the
0366 %      iterates seem to be diverging.
0367 %
0368 %
0369 %
0370 % ### NLP Scaling ###
0371 %
0372 % nlp_scaling_method            ("gradient-based")
0373 %    Select the technique used for scaling the NLP.
0374 %      Selects the technique used for scaling the problem internally before it
0375 %      is solved. For user-scaling, the parameters come from the NLP. If you are
0376 %      using AMPL, they can be specified through suffixes ("scaling_factor")
0377 %    Possible values:
0378 %     - none                    [no problem scaling will be performed]
0379 %     - user-scaling            [scaling parameters will come from the user]
0380 %     - gradient-based          [scale the problem so the maximum gradient at
0381 %                                the starting point is scaling_max_gradient]
0382 %     - equilibration-based     [scale the problem so that first derivatives are
0383 %                                of order 1 at random points (only available
0384 %                                with MC19)]
0385 %
0386 % obj_scaling_factor                  -inf <  (          1) <  +inf
0387 %    Scaling factor for the objective function.
0388 %      This option sets a scaling factor for the objective function. The scaling
0389 %      is seen internally by Ipopt but the unscaled objective is reported in the
0390 %      console output. If additional scaling parameters are computed (e.g.
0391 %      user-scaling or gradient-based), both factors are multiplied. If this
0392 %      value is chosen to be negative, Ipopt will maximize the objective
0393 %      function instead of minimizing it.
0394 %
0395 % nlp_scaling_max_gradient               0 <  (        100) <  +inf
0396 %    Maximum gradient after NLP scaling.
0397 %      This is the gradient scaling cut-off. If the maximum gradient is above
0398 %      this value, then gradient based scaling will be performed. Scaling
0399 %      parameters are calculated to scale the maximum gradient back to this
0400 %      value. (This is g_max in Section 3.8 of the implementation paper.) Note:
0401 %      This option is only used if "nlp_scaling_method" is chosen as
0402 %      "gradient-based".
0403 %
0404 % nlp_scaling_obj_target_gradient         0 <= (          0) <  +inf
0405 %    Target value for objective function gradient size.
0406 %      If a positive number is chosen, the scaling factor the objective function
0407 %      is computed so that the gradient has the max norm of the given size at
0408 %      the starting point.  This overrides nlp_scaling_max_gradient for the
0409 %      objective function.
0410 %
0411 % nlp_scaling_constr_target_gradient         0 <= (          0) <  +inf
0412 %    Target value for constraint function gradient size.
0413 %      If a positive number is chosen, the scaling factor the constraint
0414 %      functions is computed so that the gradient has the max norm of the given
0415 %      size at the starting point.  This overrides nlp_scaling_max_gradient for
0416 %      the constraint functions.
0417 %
0418 %
0419 %
0420 % ### NLP ###
0421 %
0422 % nlp_lower_bound_inf                 -inf <  (     -1e+19) <  +inf
0423 %    any bound less or equal this value will be considered -inf (i.e. not lower
0424 %    bounded).
0425 %
0426 % nlp_upper_bound_inf                 -inf <  (      1e+19) <  +inf
0427 %    any bound greater or this value will be considered +inf (i.e. not upper
0428 %    bounded).
0429 %
0430 % fixed_variable_treatment      ("make_parameter")
0431 %    Determines how fixed variables should be handled.
0432 %      The main difference between those options is that the starting point in
0433 %      the "make_constraint" case still has the fixed variables at their given
0434 %      values, whereas in the case "make_parameter" the functions are always
0435 %      evaluated with the fixed values for those variables.  Also, for
0436 %      "relax_bounds", the fixing bound constraints are relaxed (according to"
0437 %      bound_relax_factor"). For both "make_constraints" and "relax_bounds",
0438 %      bound multipliers are computed for the fixed variables.
0439 %    Possible values:
0440 %     - make_parameter          [Remove fixed variable from optimization
0441 %                                variables]
0442 %     - make_constraint         [Add equality constraints fixing variables]
0443 %     - relax_bounds            [Relax fixing bound constraints]
0444 %
0445 % dependency_detector           ("none")
0446 %    Indicates which linear solver should be used to detect linearly dependent
0447 %    equality constraints.
0448 %      The default and available choices depend on how Ipopt has been compiled.
0449 %      This is experimental and does not work well.
0450 %    Possible values:
0451 %     - none                    [don't check; no extra work at beginning]
0452 %     - mumps                   [use MUMPS]
0453 %     - wsmp                    [use WSMP]
0454 %     - ma28                    [use MA28]
0455 %
0456 % dependency_detection_with_rhs ("no")
0457 %    Indicates if the right hand sides of the constraints should be considered
0458 %    during dependency detection
0459 %    Possible values:
0460 %     - no                      [only look at gradients]
0461 %     - yes                     [also consider right hand side]
0462 %
0463 % num_linear_variables                   0 <= (          0) <  +inf
0464 %    Number of linear variables
0465 %      When the Hessian is approximated, it is assumed that the first
0466 %      num_linear_variables variables are linear.  The Hessian is then not
0467 %      approximated in this space.  If the get_number_of_nonlinear_variables
0468 %      method in the TNLP is implemented, this option is ignored.
0469 %
0470 % kappa_d                                0 <= (      1e-05) <  +inf
0471 %    Weight for linear damping term (to handle one-sided bounds).
0472 %      (see Section 3.7 in implementation paper.)
0473 %
0474 % bound_relax_factor                     0 <= (      1e-08) <  +inf
0475 %    Factor for initial relaxation of the bounds.
0476 %      Before start of the optimization, the bounds given by the user are
0477 %      relaxed.  This option sets the factor for this relaxation.  If it is set
0478 %      to zero, then then bounds relaxation is disabled. (See Eqn.(35) in
0479 %      implementation paper.)
0480 %
0481 % honor_original_bounds         ("yes")
0482 %    Indicates whether final points should be projected into original bounds.
0483 %      Ipopt might relax the bounds during the optimization (see, e.g., option
0484 %      "bound_relax_factor").  This option determines whether the final point
0485 %      should be projected back into the user-provide original bounds after the
0486 %      optimization.
0487 %    Possible values:
0488 %     - no                      [Leave final point unchanged]
0489 %     - yes                     [Project final point back into original bounds]
0490 %
0491 % check_derivatives_for_naninf  ("no")
0492 %    Indicates whether it is desired to check for Nan/Inf in derivative matrices
0493 %      Activating this option will cause an error if an invalid number is
0494 %      detected in the constraint Jacobians or the Lagrangian Hessian.  If this
0495 %      is not activated, the test is skipped, and the algorithm might proceed
0496 %      with invalid numbers and fail.
0497 %    Possible values:
0498 %     - no                      [Don't check (faster).]
0499 %     - yes                     [Check Jacobians and Hessian for Nan and Inf.]
0500 %
0501 % jac_c_constant                ("no")
0502 %    Indicates whether all equality constraints are linear
0503 %      Activating this option will cause Ipopt to ask for the Jacobian of the
0504 %      equality constraints only once from the NLP and reuse this information
0505 %      later.
0506 %    Possible values:
0507 %     - no                      [Don't assume that all equality constraints are
0508 %                                linear]
0509 %     - yes                     [Assume that equality constraints Jacobian are
0510 %                                constant]
0511 %
0512 % jac_d_constant                ("no")
0513 %    Indicates whether all inequality constraints are linear
0514 %      Activating this option will cause Ipopt to ask for the Jacobian of the
0515 %      inequality constraints only once from the NLP and reuse this information
0516 %      later.
0517 %    Possible values:
0518 %     - no                      [Don't assume that all inequality constraints
0519 %                                are linear]
0520 %     - yes                     [Assume that equality constraints Jacobian are
0521 %                                constant]
0522 %
0523 % hessian_constant              ("no")
0524 %    Indicates whether the problem is a quadratic problem
0525 %      Activating this option will cause Ipopt to ask for the Hessian of the
0526 %      Lagrangian function only once from the NLP and reuse this information
0527 %      later.
0528 %    Possible values:
0529 %     - no                      [Assume that Hessian changes]
0530 %     - yes                     [Assume that Hessian is constant]
0531 %
0532 %
0533 %
0534 % ### Initialization ###
0535 %
0536 % bound_push                             0 <  (       0.01) <  +inf
0537 %    Desired minimum absolute distance from the initial point to bound.
0538 %      Determines how much the initial point might have to be modified in order
0539 %      to be sufficiently inside the bounds (together with "bound_frac").  (This
0540 %      is kappa_1 in Section 3.6 of implementation paper.)
0541 %
0542 % bound_frac                             0 <  (       0.01) <= 0.5
0543 %    Desired minimum relative distance from the initial point to bound.
0544 %      Determines how much the initial point might have to be modified in order
0545 %      to be sufficiently inside the bounds (together with "bound_push").  (This
0546 %      is kappa_2 in Section 3.6 of implementation paper.)
0547 %
0548 % slack_bound_push                       0 <  (       0.01) <  +inf
0549 %    Desired minimum absolute distance from the initial slack to bound.
0550 %      Determines how much the initial slack variables might have to be modified
0551 %      in order to be sufficiently inside the inequality bounds (together with
0552 %      "slack_bound_frac").  (This is kappa_1 in Section 3.6 of implementation
0553 %      paper.)
0554 %
0555 % slack_bound_frac                       0 <  (       0.01) <= 0.5
0556 %    Desired minimum relative distance from the initial slack to bound.
0557 %      Determines how much the initial slack variables might have to be modified
0558 %      in order to be sufficiently inside the inequality bounds (together with
0559 %      "slack_bound_push").  (This is kappa_2 in Section 3.6 of implementation
0560 %      paper.)
0561 %
0562 % constr_mult_init_max                   0 <= (       1000) <  +inf
0563 %    Maximum allowed least-square guess of constraint multipliers.
0564 %      Determines how large the initial least-square guesses of the constraint
0565 %      multipliers are allowed to be (in max-norm). If the guess is larger than
0566 %      this value, it is discarded and all constraint multipliers are set to
0567 %      zero.  This options is also used when initializing the restoration phase.
0568 %      By default, "resto.constr_mult_init_max" (the one used in
0569 %      RestoIterateInitializer) is set to zero.
0570 %
0571 % bound_mult_init_val                    0 <  (          1) <  +inf
0572 %    Initial value for the bound multipliers.
0573 %      All dual variables corresponding to bound constraints are initialized to
0574 %      this value.
0575 %
0576 % bound_mult_init_method        ("constant")
0577 %    Initialization method for bound multipliers
0578 %      This option defines how the iterates for the bound multipliers are
0579 %      initialized.  If "constant" is chosen, then all bound multipliers are
0580 %      initialized to the value of "bound_mult_init_val".  If "mu-based" is
0581 %      chosen, the each value is initialized to the the value of "mu_init"
0582 %      divided by the corresponding slack variable.  This latter option might be
0583 %      useful if the starting point is close to the optimal solution.
0584 %    Possible values:
0585 %     - constant                [set all bound multipliers to the value of
0586 %                                bound_mult_init_val]
0587 %     - mu-based                [initialize to mu_init/x_slack]
0588 %
0589 % least_square_init_primal      ("no")
0590 %    Least square initialization of the primal variables
0591 %      If set to yes, Ipopt ignores the user provided point and solves a least
0592 %      square problem for the primal variables (x and s), to fit the linearized
0593 %      equality and inequality constraints.  This might be useful if the user
0594 %      doesn't know anything about the starting point, or for solving an LP or
0595 %      QP.
0596 %    Possible values:
0597 %     - no                      [take user-provided point]
0598 %     - yes                     [overwrite user-provided point with least-square
0599 %                                estimates]
0600 %
0601 % least_square_init_duals       ("no")
0602 %    Least square initialization of all dual variables
0603 %      If set to yes, Ipopt tries to compute least-square multipliers
0604 %      (considering ALL dual variables).  If successful, the bound multipliers
0605 %      are possibly corrected to be at least bound_mult_init_val. This might be
0606 %      useful if the user doesn't know anything about the starting point, or for
0607 %      solving an LP or QP.  This overwrites option "bound_mult_init_method".
0608 %    Possible values:
0609 %     - no                      [use bound_mult_init_val and least-square
0610 %                                equality constraint multipliers]
0611 %     - yes                     [overwrite user-provided point with least-square
0612 %                                estimates]
0613 %
0614 %
0615 %
0616 % ### Barrier Parameter Update ###
0617 %
0618 % mu_max_fact                            0 <  (       1000) <  +inf
0619 %    Factor for initialization of maximum value for barrier parameter.
0620 %      This option determines the upper bound on the barrier parameter.  This
0621 %      upper bound is computed as the average complementarity at the initial
0622 %      point times the value of this option. (Only used if option "mu_strategy"
0623 %      is chosen as "adaptive".)
0624 %
0625 % mu_max                                 0 <  (     100000) <  +inf
0626 %    Maximum value for barrier parameter.
0627 %      This option specifies an upper bound on the barrier parameter in the
0628 %      adaptive mu selection mode.  If this option is set, it overwrites the
0629 %      effect of mu_max_fact. (Only used if option "mu_strategy" is chosen as
0630 %      "adaptive".)
0631 %
0632 % mu_min                                 0 <  (      1e-11) <  +inf
0633 %    Minimum value for barrier parameter.
0634 %      This option specifies the lower bound on the barrier parameter in the
0635 %      adaptive mu selection mode. By default, it is set to the minimum of 1e-11
0636 %      and min("tol","compl_inf_tol")/("barrier_tol_factor"+1), which should be
0637 %      a reasonable value. (Only used if option "mu_strategy" is chosen as
0638 %      "adaptive".)
0639 %
0640 % adaptive_mu_globalization     ("obj-constr-filter")
0641 %    Globalization strategy for the adaptive mu selection mode.
0642 %      To achieve global convergence of the adaptive version, the algorithm has
0643 %      to switch to the monotone mode (Fiacco-McCormick approach) when
0644 %      convergence does not seem to appear.  This option sets the criterion used
0645 %      to decide when to do this switch. (Only used if option "mu_strategy" is
0646 %      chosen as "adaptive".)
0647 %    Possible values:
0648 %     - kkt-error               [nonmonotone decrease of kkt-error]
0649 %     - obj-constr-filter       [2-dim filter for objective and constraint
0650 %                                violation]
0651 %     - never-monotone-mode     [disables globalization]
0652 %
0653 % adaptive_mu_kkterror_red_iters         0 <= (          4) <  +inf
0654 %    Maximum number of iterations requiring sufficient progress.
0655 %      For the "kkt-error" based globalization strategy, sufficient progress
0656 %      must be made for "adaptive_mu_kkterror_red_iters" iterations. If this
0657 %      number of iterations is exceeded, the globalization strategy switches to
0658 %      the monotone mode.
0659 %
0660 % adaptive_mu_kkterror_red_fact          0 <  (     0.9999) <  1
0661 %    Sufficient decrease factor for "kkt-error" globalization strategy.
0662 %      For the "kkt-error" based globalization strategy, the error must decrease
0663 %      by this factor to be deemed sufficient decrease.
0664 %
0665 % filter_margin_fact                     0 <  (      1e-05) <  1
0666 %    Factor determining width of margin for obj-constr-filter adaptive
0667 %    globalization strategy.
0668 %      When using the adaptive globalization strategy, "obj-constr-filter",
0669 %      sufficient progress for a filter entry is defined as follows: (new obj) <
0670 %      (filter obj) - filter_margin_fact*(new constr-viol) OR (new constr-viol)
0671 %      < (filter constr-viol) - filter_margin_fact*(new constr-viol).  For the
0672 %      description of the "kkt-error-filter" option see "filter_max_margin".
0673 %
0674 % filter_max_margin                      0 <  (          1) <  +inf
0675 %    Maximum width of margin in obj-constr-filter adaptive globalization
0676 %    strategy.
0677 %
0678 % adaptive_mu_restore_previous_iterate("no")
0679 %    Indicates if the previous iterate should be restored if the monotone mode
0680 %    is entered.
0681 %      When the globalization strategy for the adaptive barrier algorithm
0682 %      switches to the monotone mode, it can either start from the most recent
0683 %      iterate (no), or from the last iterate that was accepted (yes).
0684 %    Possible values:
0685 %     - no                      [don't restore accepted iterate]
0686 %     - yes                     [restore accepted iterate]
0687 %
0688 % adaptive_mu_monotone_init_factor         0 <  (        0.8) <  +inf
0689 %    Determines the initial value of the barrier parameter when switching to the
0690 %    monotone mode.
0691 %      When the globalization strategy for the adaptive barrier algorithm
0692 %      switches to the monotone mode and fixed_mu_oracle is chosen as
0693 %      "average_compl", the barrier parameter is set to the current average
0694 %      complementarity times the value of "adaptive_mu_monotone_init_factor".
0695 %
0696 % adaptive_mu_kkt_norm_type     ("2-norm-squared")
0697 %    Norm used for the KKT error in the adaptive mu globalization strategies.
0698 %      When computing the KKT error for the globalization strategies, the norm
0699 %      to be used is specified with this option. Note, this options is also used
0700 %      in the QualityFunctionMuOracle.
0701 %    Possible values:
0702 %     - 1-norm                  [use the 1-norm (abs sum)]
0703 %     - 2-norm-squared          [use the 2-norm squared (sum of squares)]
0704 %     - max-norm                [use the infinity norm (max)]
0705 %     - 2-norm                  [use 2-norm]
0706 %
0707 % mu_strategy                   ("monotone")
0708 %    Update strategy for barrier parameter.
0709 %      Determines which barrier parameter update strategy is to be used.
0710 %    Possible values:
0711 %     - monotone                [use the monotone (Fiacco-McCormick) strategy]
0712 %     - adaptive                [use the adaptive update strategy]
0713 %
0714 % mu_oracle                     ("quality-function")
0715 %    Oracle for a new barrier parameter in the adaptive strategy.
0716 %      Determines how a new barrier parameter is computed in each "free-mode"
0717 %      iteration of the adaptive barrier parameter strategy. (Only considered if
0718 %      "adaptive" is selected for option "mu_strategy").
0719 %    Possible values:
0720 %     - probing                 [Mehrotra's probing heuristic]
0721 %     - loqo                    [LOQO's centrality rule]
0722 %     - quality-function        [minimize a quality function]
0723 %
0724 % fixed_mu_oracle               ("average_compl")
0725 %    Oracle for the barrier parameter when switching to fixed mode.
0726 %      Determines how the first value of the barrier parameter should be
0727 %      computed when switching to the "monotone mode" in the adaptive strategy.
0728 %      (Only considered if "adaptive" is selected for option "mu_strategy".)
0729 %    Possible values:
0730 %     - probing                 [Mehrotra's probing heuristic]
0731 %     - loqo                    [LOQO's centrality rule]
0732 %     - quality-function        [minimize a quality function]
0733 %     - average_compl           [base on current average complementarity]
0734 %
0735 % mu_init                                0 <  (        0.1) <  +inf
0736 %    Initial value for the barrier parameter.
0737 %      This option determines the initial value for the barrier parameter (mu).
0738 %      It is only relevant in the monotone, Fiacco-McCormick version of the
0739 %      algorithm. (i.e., if "mu_strategy" is chosen as "monotone")
0740 %
0741 % barrier_tol_factor                     0 <  (         10) <  +inf
0742 %    Factor for mu in barrier stop test.
0743 %      The convergence tolerance for each barrier problem in the monotone mode
0744 %      is the value of the barrier parameter times "barrier_tol_factor". This
0745 %      option is also used in the adaptive mu strategy during the monotone mode.
0746 %      (This is kappa_epsilon in implementation paper).
0747 %
0748 % mu_linear_decrease_factor              0 <  (        0.2) <  1
0749 %    Determines linear decrease rate of barrier parameter.
0750 %      For the Fiacco-McCormick update procedure the new barrier parameter mu is
0751 %      obtained by taking the minimum of mu*"mu_linear_decrease_factor" and
0752 %      mu^"superlinear_decrease_power".  (This is kappa_mu in implementation
0753 %      paper.) This option is also used in the adaptive mu strategy during the
0754 %      monotone mode.
0755 %
0756 % mu_superlinear_decrease_power          1 <  (        1.5) <  2
0757 %    Determines superlinear decrease rate of barrier parameter.
0758 %      For the Fiacco-McCormick update procedure the new barrier parameter mu is
0759 %      obtained by taking the minimum of mu*"mu_linear_decrease_factor" and
0760 %      mu^"superlinear_decrease_power".  (This is theta_mu in implementation
0761 %      paper.) This option is also used in the adaptive mu strategy during the
0762 %      monotone mode.
0763 %
0764 % mu_allow_fast_monotone_decrease("yes")
0765 %    Allow skipping of barrier problem if barrier test is already met.
0766 %      If set to "no", the algorithm enforces at least one iteration per barrier
0767 %      problem, even if the barrier test is already met for the updated barrier
0768 %      parameter.
0769 %    Possible values:
0770 %     - no                      [Take at least one iteration per barrier problem]
0771 %     - yes                     [Allow fast decrease of mu if barrier test it met]
0772 %
0773 % tau_min                                0 <  (       0.99) <  1
0774 %    Lower bound on fraction-to-the-boundary parameter tau.
0775 %      (This is tau_min in the implementation paper.)  This option is also used
0776 %      in the adaptive mu strategy during the monotone mode.
0777 %
0778 % sigma_max                              0 <  (        100) <  +inf
0779 %    Maximum value of the centering parameter.
0780 %      This is the upper bound for the centering parameter chosen by the quality
0781 %      function based barrier parameter update. (Only used if option "mu_oracle"
0782 %      is set to "quality-function".)
0783 %
0784 % sigma_min                              0 <= (      1e-06) <  +inf
0785 %    Minimum value of the centering parameter.
0786 %      This is the lower bound for the centering parameter chosen by the quality
0787 %      function based barrier parameter update. (Only used if option "mu_oracle"
0788 %      is set to "quality-function".)
0789 %
0790 % quality_function_norm_type    ("2-norm-squared")
0791 %    Norm used for components of the quality function.
0792 %      (Only used if option "mu_oracle" is set to "quality-function".)
0793 %    Possible values:
0794 %     - 1-norm                  [use the 1-norm (abs sum)]
0795 %     - 2-norm-squared          [use the 2-norm squared (sum of squares)]
0796 %     - max-norm                [use the infinity norm (max)]
0797 %     - 2-norm                  [use 2-norm]
0798 %
0799 % quality_function_centrality   ("none")
0800 %    The penalty term for centrality that is included in quality function.
0801 %      This determines whether a term is added to the quality function to
0802 %      penalize deviation from centrality with respect to complementarity.  The
0803 %      complementarity measure here is the xi in the Loqo update rule. (Only
0804 %      used if option "mu_oracle" is set to "quality-function".)
0805 %    Possible values:
0806 %     - none                    [no penalty term is added]
0807 %     - log                     [complementarity * the log of the centrality
0808 %                                measure]
0809 %     - reciprocal              [complementarity * the reciprocal of the
0810 %                                centrality measure]
0811 %     - cubed-reciprocal        [complementarity * the reciprocal of the
0812 %                                centrality measure cubed]
0813 %
0814 % quality_function_balancing_term("none")
0815 %    The balancing term included in the quality function for centrality.
0816 %      This determines whether a term is added to the quality function that
0817 %      penalizes situations where the complementarity is much smaller than dual
0818 %      and primal infeasibilities. (Only used if option "mu_oracle" is set to
0819 %      "quality-function".)
0820 %    Possible values:
0821 %     - none                    [no balancing term is added]
0822 %     - cubic                   [Max(0,Max(dual_inf,primal_inf)-compl)^3]
0823 %
0824 % quality_function_max_section_steps         0 <= (          8) <  +inf
0825 %    Maximum number of search steps during direct search procedure determining
0826 %    the optimal centering parameter.
0827 %      The golden section search is performed for the quality function based mu
0828 %      oracle. (Only used if option "mu_oracle" is set to "quality-function".)
0829 %
0830 % quality_function_section_sigma_tol         0 <= (       0.01) <  1
0831 %    Tolerance for the section search procedure determining the optimal
0832 %    centering parameter (in sigma space).
0833 %      The golden section search is performed for the quality function based mu
0834 %      oracle. (Only used if option "mu_oracle" is set to "quality-function".)
0835 %
0836 % quality_function_section_qf_tol         0 <= (          0) <  1
0837 %    Tolerance for the golden section search procedure determining the optimal
0838 %    centering parameter (in the function value space).
0839 %      The golden section search is performed for the quality function based mu
0840 %      oracle. (Only used if option "mu_oracle" is set to "quality-function".)
0841 %
0842 %
0843 %
0844 % ### Line Search ###
0845 %
0846 % alpha_red_factor                       0 <  (        0.5) <  1
0847 %    Fractional reduction of the trial step size in the backtracking line search.
0848 %      At every step of the backtracking line search, the trial step size is
0849 %      reduced by this factor.
0850 %
0851 % accept_every_trial_step       ("no")
0852 %    Always accept the first trial step.
0853 %      Setting this option to "yes" essentially disables the line search and
0854 %      makes the algorithm take aggressive steps, without global convergence
0855 %      guarantees.
0856 %    Possible values:
0857 %     - no                      [don't arbitrarily accept the full step]
0858 %     - yes                     [always accept the full step]
0859 %
0860 % accept_after_max_steps                -1 <= (         -1) <  +inf
0861 %    Accept a trial point after maximal this number of steps.
0862 %      Even if it does not satisfy line search conditions.
0863 %
0864 % alpha_for_y                   ("primal")
0865 %    Method to determine the step size for constraint multipliers.
0866 %      This option determines how the step size (alpha_y) will be calculated
0867 %      when updating the constraint multipliers.
0868 %    Possible values:
0869 %     - primal                  [use primal step size]
0870 %     - bound-mult              [use step size for the bound multipliers (good
0871 %                                for LPs)]
0872 %     - min                     [use the min of primal and bound multipliers]
0873 %     - max                     [use the max of primal and bound multipliers]
0874 %     - full                    [take a full step of size one]
0875 %     - min-dual-infeas         [choose step size minimizing new dual
0876 %                                infeasibility]
0877 %     - safer-min-dual-infeas   [like "min_dual_infeas", but safeguarded by
0878 %                                "min" and "max"]
0879 %     - primal-and-full         [use the primal step size, and full step if
0880 %                                delta_x <= alpha_for_y_tol]
0881 %     - dual-and-full           [use the dual step size, and full step if
0882 %                                delta_x <= alpha_for_y_tol]
0883 %     - acceptor                [Call LSAcceptor to get step size for y]
0884 %
0885 % alpha_for_y_tol                        0 <= (         10) <  +inf
0886 %    Tolerance for switching to full equality multiplier steps.
0887 %      This is only relevant if "alpha_for_y" is chosen "primal-and-full" or
0888 %      "dual-and-full".  The step size for the equality constraint multipliers
0889 %      is taken to be one if the max-norm of the primal step is less than this
0890 %      tolerance.
0891 %
0892 % tiny_step_tol                          0 <= (2.22045e-15) <  +inf
0893 %    Tolerance for detecting numerically insignificant steps.
0894 %      If the search direction in the primal variables (x and s) is, in relative
0895 %      terms for each component, less than this value, the algorithm accepts the
0896 %      full step without line search.  If this happens repeatedly, the algorithm
0897 %      will terminate with a corresponding exit message. The default value is 10
0898 %      times machine precision.
0899 %
0900 % tiny_step_y_tol                        0 <= (       0.01) <  +inf
0901 %    Tolerance for quitting because of numerically insignificant steps.
0902 %      If the search direction in the primal variables (x and s) is, in relative
0903 %      terms for each component, repeatedly less than tiny_step_tol, and the
0904 %      step in the y variables is smaller than this threshold, the algorithm
0905 %      will terminate.
0906 %
0907 % watchdog_shortened_iter_trigger         0 <= (         10) <  +inf
0908 %    Number of shortened iterations that trigger the watchdog.
0909 %      If the number of successive iterations in which the backtracking line
0910 %      search did not accept the first trial point exceeds this number, the
0911 %      watchdog procedure is activated.  Choosing "0" here disables the watchdog
0912 %      procedure.
0913 %
0914 % watchdog_trial_iter_max                1 <= (          3) <  +inf
0915 %    Maximum number of watchdog iterations.
0916 %      This option determines the number of trial iterations allowed before the
0917 %      watchdog procedure is aborted and the algorithm returns to the stored
0918 %      point.
0919 %
0920 % theta_max_fact                         0 <  (      10000) <  +inf
0921 %    Determines upper bound for constraint violation in the filter.
0922 %      The algorithmic parameter theta_max is determined as theta_max_fact times
0923 %      the maximum of 1 and the constraint violation at initial point.  Any
0924 %      point with a constraint violation larger than theta_max is unacceptable
0925 %      to the filter (see Eqn. (21) in the implementation paper).
0926 %
0927 % theta_min_fact                         0 <  (     0.0001) <  +inf
0928 %    Determines constraint violation threshold in the switching rule.
0929 %      The algorithmic parameter theta_min is determined as theta_min_fact times
0930 %      the maximum of 1 and the constraint violation at initial point.  The
0931 %      switching rules treats an iteration as an h-type iteration whenever the
0932 %      current constraint violation is larger than theta_min (see paragraph
0933 %      before Eqn. (19) in the implementation paper).
0934 %
0935 % eta_phi                                0 <  (      1e-08) <  0.5
0936 %    Relaxation factor in the Armijo condition.
0937 %      (See Eqn. (20) in the implementation paper)
0938 %
0939 % delta                                  0 <  (          1) <  +inf
0940 %    Multiplier for constraint violation in the switching rule.
0941 %      (See Eqn. (19) in the implementation paper.)
0942 %
0943 % s_phi                                  1 <  (        2.3) <  +inf
0944 %    Exponent for linear barrier function model in the switching rule.
0945 %      (See Eqn. (19) in the implementation paper.)
0946 %
0947 % s_theta                                1 <  (        1.1) <  +inf
0948 %    Exponent for current constraint violation in the switching rule.
0949 %      (See Eqn. (19) in the implementation paper.)
0950 %
0951 % gamma_phi                              0 <  (      1e-08) <  1
0952 %    Relaxation factor in the filter margin for the barrier function.
0953 %      (See Eqn. (18a) in the implementation paper.)
0954 %
0955 % gamma_theta                            0 <  (      1e-05) <  1
0956 %    Relaxation factor in the filter margin for the constraint violation.
0957 %      (See Eqn. (18b) in the implementation paper.)
0958 %
0959 % alpha_min_frac                         0 <  (       0.05) <  1
0960 %    Safety factor for the minimal step size (before switching to restoration
0961 %    phase).
0962 %      (This is gamma_alpha in Eqn. (20) in the implementation paper.)
0963 %
0964 % max_soc                                0 <= (          4) <  +inf
0965 %    Maximum number of second order correction trial steps at each iteration.
0966 %      Choosing 0 disables the second order corrections. (This is p^{max} of
0967 %      Step A-5.9 of Algorithm A in the implementation paper.)
0968 %
0969 % kappa_soc                              0 <  (       0.99) <  +inf
0970 %    Factor in the sufficient reduction rule for second order correction.
0971 %      This option determines how much a second order correction step must
0972 %      reduce the constraint violation so that further correction steps are
0973 %      attempted.  (See Step A-5.9 of Algorithm A in the implementation paper.)
0974 %
0975 % obj_max_inc                            1 <  (          5) <  +inf
0976 %    Determines the upper bound on the acceptable increase of barrier objective
0977 %    function.
0978 %      Trial points are rejected if they lead to an increase in the barrier
0979 %      objective function by more than obj_max_inc orders of magnitude.
0980 %
0981 % max_filter_resets                      0 <= (          5) <  +inf
0982 %    Maximal allowed number of filter resets
0983 %      A positive number enables a heuristic that resets the filter, whenever in
0984 %      more than "filter_reset_trigger" successive iterations the last rejected
0985 %      trial steps size was rejected because of the filter.  This option
0986 %      determine the maximal number of resets that are allowed to take place.
0987 %
0988 % filter_reset_trigger                   1 <= (          5) <  +inf
0989 %    Number of iterations that trigger the filter reset.
0990 %      If the filter reset heuristic is active and the number of successive
0991 %      iterations in which the last rejected trial step size was rejected
0992 %      because of the filter, the filter is reset.
0993 %
0994 % corrector_type                ("none")
0995 %    The type of corrector steps that should be taken (unsupported!).
0996 %      If "mu_strategy" is "adaptive", this option determines what kind of
0997 %      corrector steps should be tried.
0998 %    Possible values:
0999 %     - none                    [no corrector]
1000 %     - affine                  [corrector step towards mu=0]
1001 %     - primal-dual             [corrector step towards current mu]
1002 %
1003 % skip_corr_if_neg_curv         ("yes")
1004 %    Skip the corrector step in negative curvature iteration (unsupported!).
1005 %      The corrector step is not tried if negative curvature has been
1006 %      encountered during the computation of the search direction in the current
1007 %      iteration. This option is only used if "mu_strategy" is "adaptive".
1008 %    Possible values:
1009 %     - no                      [don't skip]
1010 %     - yes                     [skip]
1011 %
1012 % skip_corr_in_monotone_mode    ("yes")
1013 %    Skip the corrector step during monotone barrier parameter mode
1014 %    (unsupported!).
1015 %      The corrector step is not tried if the algorithm is currently in the
1016 %      monotone mode (see also option "barrier_strategy").This option is only
1017 %      used if "mu_strategy" is "adaptive".
1018 %    Possible values:
1019 %     - no                      [don't skip]
1020 %     - yes                     [skip]
1021 %
1022 % corrector_compl_avrg_red_fact          0 <  (          1) <  +inf
1023 %    Complementarity tolerance factor for accepting corrector step
1024 %    (unsupported!).
1025 %      This option determines the factor by which complementarity is allowed to
1026 %      increase for a corrector step to be accepted.
1027 %
1028 % nu_init                                0 <  (      1e-06) <  +inf
1029 %    Initial value of the penalty parameter.
1030 %
1031 % nu_inc                                 0 <  (     0.0001) <  +inf
1032 %    Increment of the penalty parameter.
1033 %
1034 % rho                                    0 <  (        0.1) <  1
1035 %    Value in penalty parameter update formula.
1036 %
1037 % kappa_sigma                            0 <  (      1e+10) <  +inf
1038 %    Factor limiting the deviation of dual variables from primal estimates.
1039 %      If the dual variables deviate from their primal estimates, a correction
1040 %      is performed. (See Eqn. (16) in the implementation paper.) Setting the
1041 %      value to less than 1 disables the correction.
1042 %
1043 % recalc_y                      ("no")
1044 %    Tells the algorithm to recalculate the equality and inequality multipliers
1045 %    as least square estimates.
1046 %      This asks the algorithm to recompute the multipliers, whenever the
1047 %      current infeasibility is less than recalc_y_feas_tol. Choosing yes might
1048 %      be helpful in the quasi-Newton option.  However, each recalculation
1049 %      requires an extra factorization of the linear system.  If a limited
1050 %      memory quasi-Newton option is chosen, this is used by default.
1051 %    Possible values:
1052 %     - no                      [use the Newton step to update the multipliers]
1053 %     - yes                     [use least-square multiplier estimates]
1054 %
1055 % recalc_y_feas_tol                      0 <  (      1e-06) <  +inf
1056 %    Feasibility threshold for recomputation of multipliers.
1057 %      If recalc_y is chosen and the current infeasibility is less than this
1058 %      value, then the multipliers are recomputed.
1059 %
1060 % slack_move                             0 <= (1.81899e-12) <  +inf
1061 %    Correction size for very small slacks.
1062 %      Due to numerical issues or the lack of an interior, the slack variables
1063 %      might become very small.  If a slack becomes very small compared to
1064 %      machine precision, the corresponding bound is moved slightly.  This
1065 %      parameter determines how large the move should be.  Its default value is
1066 %      mach_eps^{3/4}.  (See also end of Section 3.5 in implementation paper -
1067 %      but actual implementation might be somewhat different.)
1068 %
1069 %
1070 %
1071 % ### Warm Start ###
1072 %
1073 % warm_start_init_point         ("no")
1074 %    Warm-start for initial point
1075 %      Indicates whether this optimization should use a warm start
1076 %      initialization, where values of primal and dual variables are given
1077 %      (e.g., from a previous optimization of a related problem.)
1078 %    Possible values:
1079 %     - no                      [do not use the warm start initialization]
1080 %     - yes                     [use the warm start initialization]
1081 %
1082 % warm_start_same_structure     ("no")
1083 %    Indicates whether a problem with a structure identical to the previous one
1084 %    is to be solved.
1085 %      If "yes" is chosen, then the algorithm assumes that an NLP is now to be
1086 %      solved, whose structure is identical to one that already was considered
1087 %      (with the same NLP object).
1088 %    Possible values:
1089 %     - no                      [Assume this is a new problem.]
1090 %     - yes                     [Assume this is problem has known structure]
1091 %
1092 % warm_start_bound_push                  0 <  (      0.001) <  +inf
1093 %    same as bound_push for the regular initializer.
1094 %
1095 % warm_start_bound_frac                  0 <  (      0.001) <= 0.5
1096 %    same as bound_frac for the regular initializer.
1097 %
1098 % warm_start_slack_bound_push            0 <  (      0.001) <  +inf
1099 %    same as slack_bound_push for the regular initializer.
1100 %
1101 % warm_start_slack_bound_frac            0 <  (      0.001) <= 0.5
1102 %    same as slack_bound_frac for the regular initializer.
1103 %
1104 % warm_start_mult_bound_push             0 <  (      0.001) <  +inf
1105 %    same as mult_bound_push for the regular initializer.
1106 %
1107 % warm_start_mult_init_max            -inf <  (      1e+06) <  +inf
1108 %    Maximum initial value for the equality multipliers.
1109 %
1110 % warm_start_entire_iterate     ("no")
1111 %    Tells algorithm whether to use the GetWarmStartIterate method in the NLP.
1112 %    Possible values:
1113 %     - no                      [call GetStartingPoint in the NLP]
1114 %     - yes                     [call GetWarmStartIterate in the NLP]
1115 %
1116 %
1117 %
1118 % ### Linear Solver ###
1119 %
1120 % linear_solver                 ("mumps")
1121 %    Linear solver used for step computations.
1122 %      Determines which linear algebra package is to be used for the solution of
1123 %      the augmented linear system (for obtaining the search directions). Note,
1124 %      the code must have been compiled with the linear solver you want to
1125 %      choose. Depending on your Ipopt installation, not all options are
1126 %      available.
1127 %    Possible values:
1128 %     - ma27                    [use the Harwell routine MA27]
1129 %     - ma57                    [use the Harwell routine MA57]
1130 %     - pardiso                 [use the Pardiso package]
1131 %     - wsmp                    [use WSMP package]
1132 %     - mumps                   [use MUMPS package]
1133 %     - custom                  [use custom linear solver]
1134 %
1135 % linear_system_scaling         ("none")
1136 %    Method for scaling the linear system.
1137 %      Determines the method used to compute symmetric scaling factors for the
1138 %      augmented system (see also the "linear_scaling_on_demand" option).  This
1139 %      scaling is independent of the NLP problem scaling.  By default, MC19 is
1140 %      only used if MA27 or MA57 are selected as linear solvers. This option is
1141 %      only available if Ipopt has been compiled with MC19.
1142 %    Possible values:
1143 %     - none                    [no scaling will be performed]
1144 %     - mc19                    [use the Harwell routine MC19]
1145 %
1146 % linear_scaling_on_demand      ("yes")
1147 %    Flag indicating that linear scaling is only done if it seems required.
1148 %      This option is only important if a linear scaling method (e.g., mc19) is
1149 %      used.  If you choose "no", then the scaling factors are computed for
1150 %      every linear system from the start.  This can be quite expensive.
1151 %      Choosing "yes" means that the algorithm will start the scaling method
1152 %      only when the solutions to the linear system seem not good, and then use
1153 %      it until the end.
1154 %    Possible values:
1155 %     - no                      [Always scale the linear system.]
1156 %     - yes                     [Start using linear system scaling if solutions
1157 %                                seem not good.]
1158 %
1159 %
1160 %
1161 % ### Step Calculation ###
1162 %
1163 % mehrotra_algorithm            ("no")
1164 %    Indicates if we want to do Mehrotra's algorithm.
1165 %      If set to yes, Ipopt runs as Mehrotra's predictor-corrector algorithm.
1166 %      This works usually very well for LPs and convex QPs.  This automatically
1167 %      disables the line search, and chooses the (unglobalized) adaptive mu
1168 %      strategy with the "probing" oracle, and uses "corrector_type=affine"
1169 %      without any safeguards; you should not set any of those options
1170 %      explicitly in addition.  Also, unless otherwise specified, the values of
1171 %      "bound_push", "bound_frac", and "bound_mult_init_val" are set more
1172 %      aggressive, and sets "alpha_for_y=bound_mult".
1173 %    Possible values:
1174 %     - no                      [Do the usual Ipopt algorithm.]
1175 %     - yes                     [Do Mehrotra's predictor-corrector algorithm.]
1176 %
1177 % fast_step_computation         ("no")
1178 %    Indicates if the linear system should be solved quickly.
1179 %      If set to yes, the algorithm assumes that the linear system that is
1180 %      solved to obtain the search direction, is solved sufficiently well. In
1181 %      that case, no residuals are computed, and the computation of the search
1182 %      direction is a little faster.
1183 %    Possible values:
1184 %     - no                      [Verify solution of linear system by computing
1185 %                                residuals.]
1186 %     - yes                     [Trust that linear systems are solved well.]
1187 %
1188 % min_refinement_steps                   0 <= (          1) <  +inf
1189 %    Minimum number of iterative refinement steps per linear system solve.
1190 %      Iterative refinement (on the full unsymmetric system) is performed for
1191 %      each right hand side.  This option determines the minimum number of
1192 %      iterative refinements (i.e. at least "min_refinement_steps" iterative
1193 %      refinement steps are enforced per right hand side.)
1194 %
1195 % max_refinement_steps                   0 <= (         10) <  +inf
1196 %    Maximum number of iterative refinement steps per linear system solve.
1197 %      Iterative refinement (on the full unsymmetric system) is performed for
1198 %      each right hand side.  This option determines the maximum number of
1199 %      iterative refinement steps.
1200 %
1201 % residual_ratio_max                     0 <  (      1e-10) <  +inf
1202 %    Iterative refinement tolerance
1203 %      Iterative refinement is performed until the residual test ratio is less
1204 %      than this tolerance (or until "max_refinement_steps" refinement steps are
1205 %      performed).
1206 %
1207 % residual_ratio_singular                0 <  (      1e-05) <  +inf
1208 %    Threshold for declaring linear system singular after failed iterative
1209 %    refinement.
1210 %      If the residual test ratio is larger than this value after failed
1211 %      iterative refinement, the algorithm pretends that the linear system is
1212 %      singular.
1213 %
1214 % residual_improvement_factor            0 <  (          1) <  +inf
1215 %    Minimal required reduction of residual test ratio in iterative refinement.
1216 %      If the improvement of the residual test ratio made by one iterative
1217 %      refinement step is not better than this factor, iterative refinement is
1218 %      aborted.
1219 %
1220 % neg_curv_test_tol                      0 <  (          0) <  +inf
1221 %    Tolerance for heuristic to ignore wrong inertia.
1222 %      If positive, incorrect inertia in the augmented system is ignored, and we
1223 %      test if the direction is a direction of positive curvature.  This
1224 %      tolerance determines when the direction is considered to be sufficiently
1225 %      positive.
1226 %
1227 % max_hessian_perturbation               0 <  (      1e+20) <  +inf
1228 %    Maximum value of regularization parameter for handling negative curvature.
1229 %      In order to guarantee that the search directions are indeed proper
1230 %      descent directions, Ipopt requires that the inertia of the (augmented)
1231 %      linear system for the step computation has the correct number of negative
1232 %      and positive eigenvalues. The idea is that this guides the algorithm away
1233 %      from maximizers and makes Ipopt more likely converge to first order
1234 %      optimal points that are minimizers. If the inertia is not correct, a
1235 %      multiple of the identity matrix is added to the Hessian of the Lagrangian
1236 %      in the augmented system. This parameter gives the maximum value of the
1237 %      regularization parameter. If a regularization of that size is not enough,
1238 %      the algorithm skips this iteration and goes to the restoration phase.
1239 %      (This is delta_w^max in the implementation paper.)
1240 %
1241 % min_hessian_perturbation               0 <= (      1e-20) <  +inf
1242 %    Smallest perturbation of the Hessian block.
1243 %      The size of the perturbation of the Hessian block is never selected
1244 %      smaller than this value, unless no perturbation is necessary. (This is
1245 %      delta_w^min in implementation paper.)
1246 %
1247 % perturb_inc_fact_first                 1 <  (        100) <  +inf
1248 %    Increase factor for x-s perturbation for very first perturbation.
1249 %      The factor by which the perturbation is increased when a trial value was
1250 %      not sufficient - this value is used for the computation of the very first
1251 %      perturbation and allows a different value for for the first perturbation
1252 %      than that used for the remaining perturbations. (This is bar_kappa_w^+ in
1253 %      the implementation paper.)
1254 %
1255 % perturb_inc_fact                       1 <  (          8) <  +inf
1256 %    Increase factor for x-s perturbation.
1257 %      The factor by which the perturbation is increased when a trial value was
1258 %      not sufficient - this value is used for the computation of all
1259 %      perturbations except for the first. (This is kappa_w^+ in the
1260 %      implementation paper.)
1261 %
1262 % perturb_dec_fact                       0 <  (   0.333333) <  1
1263 %    Decrease factor for x-s perturbation.
1264 %      The factor by which the perturbation is decreased when a trial value is
1265 %      deduced from the size of the most recent successful perturbation. (This
1266 %      is kappa_w^- in the implementation paper.)
1267 %
1268 % first_hessian_perturbation             0 <  (     0.0001) <  +inf
1269 %    Size of first x-s perturbation tried.
1270 %      The first value tried for the x-s perturbation in the inertia correction
1271 %      scheme.(This is delta_0 in the implementation paper.)
1272 %
1273 % jacobian_regularization_value          0 <= (      1e-08) <  +inf
1274 %    Size of the regularization for rank-deficient constraint Jacobians.
1275 %      (This is bar delta_c in the implementation paper.)
1276 %
1277 % jacobian_regularization_exponent         0 <= (       0.25) <  +inf
1278 %    Exponent for mu in the regularization for rank-deficient constraint
1279 %    Jacobians.
1280 %      (This is kappa_c in the implementation paper.)
1281 %
1282 % perturb_always_cd             ("no")
1283 %    Active permanent perturbation of constraint linearization.
1284 %      This options makes the delta_c and delta_d perturbation be used for the
1285 %      computation of every search direction.  Usually, it is only used when the
1286 %      iteration matrix is singular.
1287 %    Possible values:
1288 %     - no                      [perturbation only used when required]
1289 %     - yes                     [always use perturbation]
1290 %
1291 %
1292 %
1293 % ### Restoration Phase ###
1294 %
1295 % expect_infeasible_problem     ("no")
1296 %    Enable heuristics to quickly detect an infeasible problem.
1297 %      This options is meant to activate heuristics that may speed up the
1298 %      infeasibility determination if you expect that there is a good chance for
1299 %      the problem to be infeasible.  In the filter line search procedure, the
1300 %      restoration phase is called more quickly than usually, and more reduction
1301 %      in the constraint violation is enforced before the restoration phase is
1302 %      left. If the problem is square, this option is enabled automatically.
1303 %    Possible values:
1304 %     - no                      [the problem probably be feasible]
1305 %     - yes                     [the problem has a good chance to be infeasible]
1306 %
1307 % expect_infeasible_problem_ctol         0 <= (      0.001) <  +inf
1308 %    Threshold for disabling "expect_infeasible_problem" option.
1309 %      If the constraint violation becomes smaller than this threshold, the
1310 %      "expect_infeasible_problem" heuristics in the filter line search are
1311 %      disabled. If the problem is square, this options is set to 0.
1312 %
1313 % expect_infeasible_problem_ytol         0 <  (      1e+08) <  +inf
1314 %    Multiplier threshold for activating "expect_infeasible_problem" option.
1315 %      If the max norm of the constraint multipliers becomes larger than this
1316 %      value and "expect_infeasible_problem" is chosen, then the restoration
1317 %      phase is entered.
1318 %
1319 % start_with_resto              ("no")
1320 %    Tells algorithm to switch to restoration phase in first iteration.
1321 %      Setting this option to "yes" forces the algorithm to switch to the
1322 %      feasibility restoration phase in the first iteration. If the initial
1323 %      point is feasible, the algorithm will abort with a failure.
1324 %    Possible values:
1325 %     - no                      [don't force start in restoration phase]
1326 %     - yes                     [force start in restoration phase]
1327 %
1328 % soft_resto_pderror_reduction_factor         0 <= (     0.9999) <  +inf
1329 %    Required reduction in primal-dual error in the soft restoration phase.
1330 %      The soft restoration phase attempts to reduce the primal-dual error with
1331 %      regular steps. If the damped primal-dual step (damped only to satisfy the
1332 %      fraction-to-the-boundary rule) is not decreasing the primal-dual error by
1333 %      at least this factor, then the regular restoration phase is called.
1334 %      Choosing "0" here disables the soft restoration phase.
1335 %
1336 % max_soft_resto_iters                   0 <= (         10) <  +inf
1337 %    Maximum number of iterations performed successively in soft restoration
1338 %    phase.
1339 %      If the soft restoration phase is performed for more than so many
1340 %      iterations in a row, the regular restoration phase is called.
1341 %
1342 % required_infeasibility_reduction         0 <= (        0.9) <  1
1343 %    Required reduction of infeasibility before leaving restoration phase.
1344 %      The restoration phase algorithm is performed, until a point is found that
1345 %      is acceptable to the filter and the infeasibility has been reduced by at
1346 %      least the fraction given by this option.
1347 %
1348 % max_resto_iter                         0 <= (    3000000) <  +inf
1349 %    Maximum number of successive iterations in restoration phase.
1350 %      The algorithm terminates with an error message if the number of
1351 %      iterations successively taken in the restoration phase exceeds this
1352 %      number.
1353 %
1354 % evaluate_orig_obj_at_resto_trial("yes")
1355 %    Determines if the original objective function should be evaluated at
1356 %    restoration phase trial points.
1357 %      Setting this option to "yes" makes the restoration phase algorithm
1358 %      evaluate the objective function of the original problem at every trial
1359 %      point encountered during the restoration phase, even if this value is not
1360 %      required.  In this way, it is guaranteed that the original objective
1361 %      function can be evaluated without error at all accepted iterates;
1362 %      otherwise the algorithm might fail at a point where the restoration phase
1363 %      accepts an iterate that is good for the restoration phase problem, but
1364 %      not the original problem.  On the other hand, if the evaluation of the
1365 %      original objective is expensive, this might be costly.
1366 %    Possible values:
1367 %     - no                      [skip evaluation]
1368 %     - yes                     [evaluate at every trial point]
1369 %
1370 % resto_penalty_parameter                0 <  (       1000) <  +inf
1371 %    Penalty parameter in the restoration phase objective function.
1372 %      This is the parameter rho in equation (31a) in the Ipopt implementation
1373 %      paper.
1374 %
1375 % bound_mult_reset_threshold             0 <= (       1000) <  +inf
1376 %    Threshold for resetting bound multipliers after the restoration phase.
1377 %      After returning from the restoration phase, the bound multipliers are
1378 %      updated with a Newton step for complementarity.  Here, the change in the
1379 %      primal variables during the entire restoration phase is taken to be the
1380 %      corresponding primal Newton step. However, if after the update the
1381 %      largest bound multiplier exceeds the threshold specified by this option,
1382 %      the multipliers are all reset to 1.
1383 %
1384 % constr_mult_reset_threshold            0 <= (          0) <  +inf
1385 %    Threshold for resetting equality and inequality multipliers after
1386 %    restoration phase.
1387 %      After returning from the restoration phase, the constraint multipliers
1388 %      are recomputed by a least square estimate.  This option triggers when
1389 %      those least-square estimates should be ignored.
1390 %
1391 %
1392 %
1393 % ### Derivative Checker ###
1394 %
1395 % derivative_test               ("none")
1396 %    Enable derivative checker
1397 %      If this option is enabled, a (slow!) derivative test will be performed
1398 %      before the optimization.  The test is performed at the user provided
1399 %      starting point and marks derivative values that seem suspicious
1400 %    Possible values:
1401 %     - none                    [do not perform derivative test]
1402 %     - first-order             [perform test of first derivatives at starting
1403 %                                point]
1404 %     - second-order            [perform test of first and second derivatives at
1405 %                                starting point]
1406 %     - only-second-order       [perform test of second derivatives at starting
1407 %                                point]
1408 %
1409 % derivative_test_first_index           -2 <= (         -2) <  +inf
1410 %    Index of first quantity to be checked by derivative checker
1411 %      If this is set to -2, then all derivatives are checked.  Otherwise, for
1412 %      the first derivative test it specifies the first variable for which the
1413 %      test is done (counting starts at 0).  For second derivatives, it
1414 %      specifies the first constraint for which the test is done; counting of
1415 %      constraint indices starts at 0, and -1 refers to the objective function
1416 %      Hessian.
1417 %
1418 % derivative_test_perturbation           0 <  (      1e-08) <  +inf
1419 %    Size of the finite difference perturbation in derivative test.
1420 %      This determines the relative perturbation of the variable entries.
1421 %
1422 % derivative_test_tol                    0 <  (     0.0001) <  +inf
1423 %    Threshold for indicating wrong derivative.
1424 %      If the relative deviation of the estimated derivative from the given one
1425 %      is larger than this value, the corresponding derivative is marked as
1426 %      wrong.
1427 %
1428 % derivative_test_print_all     ("no")
1429 %    Indicates whether information for all estimated derivatives should be
1430 %    printed.
1431 %      Determines verbosity of derivative checker.
1432 %    Possible values:
1433 %     - no                      [Print only suspect derivatives]
1434 %     - yes                     [Print all derivatives]
1435 %
1436 % jacobian_approximation        ("exact")
1437 %    Specifies technique to compute constraint Jacobian
1438 %    Possible values:
1439 %     - exact                   [user-provided derivatives]
1440 %     - finite-difference-values [user-provided structure, values by finite
1441 %                                differences]
1442 %
1443 % findiff_perturbation                   0 <  (      1e-07) <  +inf
1444 %    Size of the finite difference perturbation for derivative approximation.
1445 %      This determines the relative perturbation of the variable entries.
1446 %
1447 % point_perturbation_radius              0 <= (         10) <  +inf
1448 %    Maximal perturbation of an evaluation point.
1449 %      If a random perturbation of a points is required, this number indicates
1450 %      the maximal perturbation.  This is for example used when determining the
1451 %      center point at which the finite difference derivative test is executed.
1452 %
1453 %
1454 %
1455 % ### Hessian Approximation ###
1456 %
1457 % limited_memory_max_history             0 <= (          6) <  +inf
1458 %    Maximum size of the history for the limited quasi-Newton Hessian
1459 %    approximation.
1460 %      This option determines the number of most recent iterations that are
1461 %      taken into account for the limited-memory quasi-Newton approximation.
1462 %
1463 % limited_memory_update_type    ("bfgs")
1464 %    Quasi-Newton update formula for the limited memory approximation.
1465 %      Determines which update formula is to be used for the limited-memory
1466 %      quasi-Newton approximation.
1467 %    Possible values:
1468 %     - bfgs                    [BFGS update (with skipping)]
1469 %     - sr1                     [SR1 (not working well)]
1470 %
1471 % limited_memory_initialization ("scalar1")
1472 %    Initialization strategy for the limited memory quasi-Newton approximation.
1473 %      Determines how the diagonal Matrix B_0 as the first term in the limited
1474 %      memory approximation should be computed.
1475 %    Possible values:
1476 %     - scalar1                 [sigma = s^Ty/s^Ts]
1477 %     - scalar2                 [sigma = y^Ty/s^Ty]
1478 %     - constant                [sigma = limited_memory_init_val]
1479 %
1480 % limited_memory_init_val                0 <  (          1) <  +inf
1481 %    Value for B0 in low-rank update.
1482 %      The starting matrix in the low rank update, B0, is chosen to be this
1483 %      multiple of the identity in the first iteration (when no updates have
1484 %      been performed yet), and is constantly chosen as this value, if
1485 %      "limited_memory_initialization" is "constant".
1486 %
1487 % limited_memory_init_val_max            0 <  (      1e+08) <  +inf
1488 %    Upper bound on value for B0 in low-rank update.
1489 %      The starting matrix in the low rank update, B0, is chosen to be this
1490 %      multiple of the identity in the first iteration (when no updates have
1491 %      been performed yet), and is constantly chosen as this value, if
1492 %      "limited_memory_initialization" is "constant".
1493 %
1494 % limited_memory_init_val_min            0 <  (      1e-08) <  +inf
1495 %    Lower bound on value for B0 in low-rank update.
1496 %      The starting matrix in the low rank update, B0, is chosen to be this
1497 %      multiple of the identity in the first iteration (when no updates have
1498 %      been performed yet), and is constantly chosen as this value, if
1499 %      "limited_memory_initialization" is "constant".
1500 %
1501 % limited_memory_max_skipping            1 <= (          2) <  +inf
1502 %    Threshold for successive iterations where update is skipped.
1503 %      If the update is skipped more than this number of successive iterations,
1504 %      we quasi-Newton approximation is reset.
1505 %
1506 % hessian_approximation         ("exact")
1507 %    Indicates what Hessian information is to be used.
1508 %      This determines which kind of information for the Hessian of the
1509 %      Lagrangian function is used by the algorithm.
1510 %    Possible values:
1511 %     - exact                   [Use second derivatives provided by the NLP.]
1512 %     - limited-memory          [Perform a limited-memory quasi-Newton
1513 %                                approximation]
1514 %
1515 % hessian_approximation_space   ("nonlinear-variables")
1516 %    Indicates in which subspace the Hessian information is to be approximated.
1517 %    Possible values:
1518 %     - nonlinear-variables     [only in space of nonlinear variables.]
1519 %     - all-variables           [in space of all variables (without slacks)]
1520 %
1521 %
1522 %
1523 % ### MA27 Linear Solver ###
1524 %
1525 % ma27_pivtol                            0 <  (      1e-08) <  1
1526 %    Pivot tolerance for the linear solver MA27.
1527 %      A smaller number pivots for sparsity, a larger number pivots for
1528 %      stability.  This option is only available if Ipopt has been compiled with
1529 %      MA27.
1530 %
1531 % ma27_pivtolmax                         0 <  (     0.0001) <  1
1532 %    Maximum pivot tolerance for the linear solver MA27.
1533 %      Ipopt may increase pivtol as high as pivtolmax to get a more accurate
1534 %      solution to the linear system.  This option is only available if Ipopt
1535 %      has been compiled with MA27.
1536 %
1537 % ma27_liw_init_factor                   1 <= (          5) <  +inf
1538 %    Integer workspace memory for MA27.
1539 %      The initial integer workspace memory = liw_init_factor * memory required
1540 %      by unfactored system. Ipopt will increase the workspace size by
1541 %      meminc_factor if required.  This option is only available if Ipopt has
1542 %      been compiled with MA27.
1543 %
1544 % ma27_la_init_factor                    1 <= (          5) <  +inf
1545 %    Real workspace memory for MA27.
1546 %      The initial real workspace memory = la_init_factor * memory required by
1547 %      unfactored system. Ipopt will increase the workspace size by
1548 %      meminc_factor if required.  This option is only available if  Ipopt has
1549 %      been compiled with MA27.
1550 %
1551 % ma27_meminc_factor                     1 <= (         10) <  +inf
1552 %    Increment factor for workspace size for MA27.
1553 %      If the integer or real workspace is not large enough, Ipopt will increase
1554 %      its size by this factor.  This option is only available if Ipopt has been
1555 %      compiled with MA27.
1556 %
1557 % ma27_skip_inertia_check       ("no")
1558 %    Always pretend inertia is correct.
1559 %      Setting this option to "yes" essentially disables inertia check. This
1560 %      option makes the algorithm non-robust and easily fail, but it might give
1561 %      some insight into the necessity of inertia control.
1562 %    Possible values:
1563 %     - no                      [check inertia]
1564 %     - yes                     [skip inertia check]
1565 %
1566 % ma27_ignore_singularity       ("no")
1567 %    Enables MA27's ability to solve a linear system even if the matrix is
1568 %    singular.
1569 %      Setting this option to "yes" means that Ipopt will call MA27 to compute
1570 %      solutions for right hand sides, even if MA27 has detected that the matrix
1571 %      is singular (but is still able to solve the linear system). In some cases
1572 %      this might be better than using Ipopt's heuristic of small perturbation
1573 %      of the lower diagonal of the KKT matrix.
1574 %    Possible values:
1575 %     - no                      [Don't have MA27 solve singular systems]
1576 %     - yes                     [Have MA27 solve singular systems]
1577 %
1578 %
1579 %
1580 % ### MA57 Linear Solver ###
1581 %
1582 % ma57_pivtol                            0 <  (      1e-08) <  1
1583 %    Pivot tolerance for the linear solver MA57.
1584 %      A smaller number pivots for sparsity, a larger number pivots for
1585 %      stability. This option is only available if Ipopt has been compiled with
1586 %      MA57.
1587 %
1588 % ma57_pivtolmax                         0 <  (     0.0001) <  1
1589 %    Maximum pivot tolerance for the linear solver MA57.
1590 %      Ipopt may increase pivtol as high as ma57_pivtolmax to get a more
1591 %      accurate solution to the linear system.  This option is only available if
1592 %      Ipopt has been compiled with MA57.
1593 %
1594 % ma57_pre_alloc                         1 <= (          3) <  +inf
1595 %    Safety factor for work space memory allocation for the linear solver MA57.
1596 %      If 1 is chosen, the suggested amount of work space is used.  However,
1597 %      choosing a larger number might avoid reallocation if the suggest values
1598 %      do not suffice.  This option is only available if Ipopt has been compiled
1599 %      with MA57.
1600 %
1601 % ma57_pivot_order                       0 <= (          5) <= 5
1602 %    Controls pivot order in MA57
1603 %      This is INCTL(6) in MA57.
1604 %
1605 %
1606 %
1607 % ### Pardiso Linear Solver ###
1608 %
1609 % pardiso_matching_strategy     ("complete+2x2")
1610 %    Matching strategy to be used by Pardiso
1611 %      This is IPAR(13) in Pardiso manual.  This option is only available if
1612 %      Ipopt has been compiled with Pardiso.
1613 %    Possible values:
1614 %     - complete                [Match complete (IPAR(13)=1)]
1615 %     - complete+2x2            [Match complete+2x2 (IPAR(13)=2)]
1616 %     - constraints             [Match constraints (IPAR(13)=3)]
1617 %
1618 % pardiso_redo_symbolic_fact_only_if_inertia_wrong("no")
1619 %    Toggle for handling case when elements were perturbed by Pardiso.
1620 %      This option is only available if Ipopt has been compiled with Pardiso.
1621 %    Possible values:
1622 %     - no                      [Always redo symbolic factorization when
1623 %                                elements were perturbed]
1624 %     - yes                     [Only redo symbolic factorization when elements
1625 %                                were perturbed if also the inertia was wrong]
1626 %
1627 % pardiso_repeated_perturbation_means_singular("no")
1628 %    Interpretation of perturbed elements.
1629 %      This option is only available if Ipopt has been compiled with Pardiso.
1630 %    Possible values:
1631 %     - no                      [Don't assume that matrix is singular if
1632 %                                elements were perturbed after recent symbolic
1633 %                                factorization]
1634 %     - yes                     [Assume that matrix is singular if elements were
1635 %                                perturbed after recent symbolic factorization]
1636 %
1637 % pardiso_out_of_core_power              0 <= (          0) <  +inf
1638 %    Enables out-of-core variant of Pardiso
1639 %      Setting this option to a positive integer k makes Pardiso work in the
1640 %      out-of-core variant where the factor is split in 2^k subdomains.  This is
1641 %      IPARM(50) in the Pardiso manual.  This option is only available if Ipopt
1642 %      has been compiled with Pardiso.
1643 %
1644 % pardiso_msglvl                         0 <= (          0) <  +inf
1645 %    Pardiso message level
1646 %      This determines the amount of analysis output from the Pardiso solver.
1647 %      This is MSGLVL in the Pardiso manual.
1648 %
1649 % pardiso_skip_inertia_check    ("no")
1650 %    Always pretent inertia is correct.
1651 %      Setting this option to "yes" essentially disables inertia check. This
1652 %      option makes the algorithm non-robust and easily fail, but it might give
1653 %      some insight into the necessity of inertia control.
1654 %    Possible values:
1655 %     - no                      [check inertia]
1656 %     - yes                     [skip inertia check]
1657 %
1658 % pardiso_max_iter                       1 <= (        500) <  +inf
1659 %    Maximum number of Krylov-Subspace Iteration
1660 %      DPARM(1)
1661 %
1662 % pardiso_iter_relative_tol              0 <  (      1e-06) <  1
1663 %    Relative Residual Convergence
1664 %      DPARM(2)
1665 %
1666 % pardiso_iter_coarse_size               1 <= (       5000) <  +inf
1667 %    Maximum Size of Coarse Grid Matrix
1668 %      DPARM(3)
1669 %
1670 % pardiso_iter_max_levels                1 <= (      10000) <  +inf
1671 %    Maximum Size of Grid Levels
1672 %      DPARM(4)
1673 %
1674 % pardiso_iter_dropping_factor           0 <  (        0.5) <  1
1675 %    dropping value for incomplete factor
1676 %      DPARM(5)
1677 %
1678 % pardiso_iter_dropping_schur            0 <  (        0.1) <  1
1679 %    dropping value for sparsify schur complement factor
1680 %      DPARM(6)
1681 %
1682 % pardiso_iter_max_row_fill              1 <= (   10000000) <  +inf
1683 %    max fill for each row
1684 %      DPARM(7)
1685 %
1686 % pardiso_iter_inverse_norm_factor         1 <  (      5e+06) <  +inf
1687 %
1688 %      DPARM(8)
1689 %
1690 % pardiso_iterative             ("no")
1691 %    Switch on iterative solver in Pardiso library
1692 %    Possible values:
1693 %     - no                      []
1694 %     - yes                     []
1695 %
1696 % pardiso_max_droptol_corrections         1 <= (          4) <  +inf
1697 %    Maximal number of decreases of drop tolerance during one solve.
1698 %      This is relevant only for iterative Pardiso options.
1699 %
1700 %
1701 %
1702 % ### Mumps Linear Solver ###
1703 %
1704 % mumps_pivtol                           0 <= (      1e-06) <= 1
1705 %    Pivot tolerance for the linear solver MUMPS.
1706 %      A smaller number pivots for sparsity, a larger number pivots for
1707 %      stability.  This option is only available if Ipopt has been compiled with
1708 %      MUMPS.
1709 %
1710 % mumps_pivtolmax                        0 <= (        0.1) <= 1
1711 %    Maximum pivot tolerance for the linear solver MUMPS.
1712 %      Ipopt may increase pivtol as high as pivtolmax to get a more accurate
1713 %      solution to the linear system.  This option is only available if Ipopt
1714 %      has been compiled with MUMPS.
1715 %
1716 % mumps_mem_percent                      0 <= (       1000) <  +inf
1717 %    Percentage increase in the estimated working space for MUMPS.
1718 %      In MUMPS when significant extra fill-in is caused by numerical pivoting,
1719 %      larger values of mumps_mem_percent may help use the workspace more
1720 %      efficiently.  On the other hand, if memory requirement are too large at
1721 %      the very beginning of the optimization, choosing a much smaller value for
1722 %      this option, such as 5, might reduce memory requirements.
1723 %
1724 % mumps_permuting_scaling                0 <= (          7) <= 7
1725 %    Controls permuting and scaling in MUMPS
1726 %      This is ICNTL(6) in MUMPS.
1727 %
1728 % mumps_pivot_order                      0 <= (          7) <= 7
1729 %    Controls pivot order in MUMPS
1730 %      This is ICNTL(7) in MUMPS.
1731 %
1732 % mumps_scaling                         -2 <= (         77) <= 77
1733 %    Controls scaling in MUMPS
1734 %      This is ICNTL(8) in MUMPS.
1735 %
1736 % mumps_dep_tol                       -inf <  (         -1) <  +inf
1737 %    Pivot threshold for detection of linearly dependent constraints in MUMPS.
1738 %      When MUMPS is used to determine linearly dependent constraints, this is
1739 %      determines the threshold for a pivot to be considered zero.  This is
1740 %      CNTL(3) in MUMPS.
1741 %
1742 %
1743 %
1744 % ### MA28 Linear Solver ###
1745 %
1746 % ma28_pivtol                            0 <  (       0.01) <= 1
1747 %    Pivot tolerance for linear solver MA28.
1748 %      This is used when MA28 tries to find the dependent constraints.
1749 %
1750 %
1751 %
1752 % ### Uncategorized ###
1753 %
1754 % warm_start_target_mu                -inf <  (          0) <  +inf
1755 %    Unsupported!

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