problem_type

PROBLEM_TYPE  Return a string identifying the type of mathematical program
   PROB_TYPE = OM.PROBLEM_TYPE()
   PROB_TYPE = OM.PROBLEM_TYPE(RECHECK)

   Returns a string identifying the type of mathematical program
   represented by the current model, based on the variables, costs,
   and constraints that have been added to the model. Used to
   automatically select an appropriate solver.

   Linear and nonlinear equations are models with no costs, no inequality
   constraints, and an equal number of continuous variables and equality
   constraints.

   Outputs:
       PROB_TYPE : problem type, one of the following strings:
           'LEQ'   - linear equations
           'NLEQ'  - nonlinear equations
           'LP'    - linear program
           'QP'    - quadratic program
           'NLP'   - nonlinear program
           'MILP'  - mixed-integer linear program
           'MIQP'  - mixed-integer quadratic program
           'MINLP' - mixed-integer nonlinear program

   The output value is cached for future calls, but calling with a true
   value for the optional RECHECK argument will force it to recheck in
   case the problem type has changed due to modifying the variables,
   constraints or costs in the model.

   See also OPT_MODEL

0001 function prob = problem_type(om, recheck)
0002 %PROBLEM_TYPE  Return a string identifying the type of mathematical program
0003 %   PROB_TYPE = OM.PROBLEM_TYPE()
0004 %   PROB_TYPE = OM.PROBLEM_TYPE(RECHECK)
0005 %
0006 %   Returns a string identifying the type of mathematical program
0007 %   represented by the current model, based on the variables, costs,
0008 %   and constraints that have been added to the model. Used to
0009 %   automatically select an appropriate solver.
0010 %
0011 %   Linear and nonlinear equations are models with no costs, no inequality
0012 %   constraints, and an equal number of continuous variables and equality
0013 %   constraints.
0014 %
0015 %   Outputs:
0016 %       PROB_TYPE : problem type, one of the following strings:
0017 %           'LEQ'   - linear equations
0018 %           'NLEQ'  - nonlinear equations
0019 %           'LP'    - linear program
0020 %           'QP'    - quadratic program
0021 %           'NLP'   - nonlinear program
0022 %           'MILP'  - mixed-integer linear program
0023 %           'MIQP'  - mixed-integer quadratic program
0024 %           'MINLP' - mixed-integer nonlinear program
0025 %
0026 %   The output value is cached for future calls, but calling with a true
0027 %   value for the optional RECHECK argument will force it to recheck in
0028 %   case the problem type has changed due to modifying the variables,
0029 %   constraints or costs in the model.
0030 %
0031 %   See also OPT_MODEL
0032 
0033 %   MP-Opt-Model
0034 %   Copyright (c) 2020, Power Systems Engineering Research Center (PSERC)
0035 %   by Ray Zimmerman, PSERC Cornell
0036 %
0037 %   This file is part of MP-Opt-Model.
0038 %   Covered by the 3-clause BSD License (see LICENSE file for details).
0039 %   See https://github.com/MATPOWER/mp-opt-model for more info.
0040 
0041 if isempty(om.prob_type) || nargin > 1 && recheck
0042     nleN = om.getN('nle');      %% nonlinear equalities
0043     nliN = om.getN('nli');      %% nonlinear inequalities
0044     nlcN = om.getN('nlc');      %% general nonlinear costs
0045     qdcN = om.getN('qdc');      %% quadratic costs
0046     linN = om.getN('lin');      %% linear constraints
0047     varN = om.getN('var');      %% variables
0048     if nlcN || qdcN         %% problem has costs
0049         if nliN || nleN || nlcN %% nonlinear
0050             prob = 'NLP';           %% nonlinear program
0051         else                    %% linear constraints, no general nonlinear costs
0052             %% get quadratic cost coefficients
0053             H = om.params_quad_cost();
0054             if isempty(H) || ~any(any(H))
0055                 prob = 'LP';        %% linear program
0056             else
0057                 prob = 'QP';        %% quadratic program
0058             end
0059         end
0060     else                    %% problem has no costs
0061         if nliN
0062             error('@opt_model/problem_type: invalid problem - nonlinear inequality constraints with no costs');
0063         end
0064         if nleN + linN == varN  %% square system
0065             if linN > 0
0066                 %% get lower & upper bounds
0067                 [A, l, u] = om.params_lin_constraint();
0068                 if any(l ~= u)
0069                     error('@opt_model/problem_type: invalid problem - linear inequality constraints with no costs');
0070                 end
0071             end
0072             if nleN
0073                 prob = 'NLEQ';      %% square nonlinear set of equations
0074             else
0075                 prob = 'LEQ';       %% square linear set of equations
0076             end
0077         else
0078             error('@opt_model/problem_type: invalid problem - non-square system with no costs');
0079         end
0080     end
0081     if om.is_mixed_integer() && ~strcmp(prob, 'NLEQ')
0082         prob = ['MI' prob];
0083     end
0084     om.prob_type = prob;    %% cache it
0085 else
0086     prob = om.prob_type;    %% return cached type
0087 end