init_indexed_name

INIT_INDEXED_NAME  Initializes the dimensions for an indexed named set.

   OM.INIT_INDEXED_NAME(SET_TYPE, NAME, DIM_LIST)

   Initializes the dimensions for an indexed named variable, constraint
   or cost set.

   Variables, constraints and costs are referenced in OPT_MODEL in terms
   of named sets. The specific type of named set being referenced is
   given by SET_TYPE, with the following valid options:
       SET_TYPE = 'var'   => variable set
       SET_TYPE = 'lin'   => linear constraint set
       SET_TYPE = 'nle'   => nonlinear equality constraint set
       SET_TYPE = 'nli'   => nonlinear inequality constraint set
       SET_TYPE = 'qdc'   => quadratic cost set
       SET_TYPE = 'nlc'   => nonlinear cost set

   Indexed Named Sets

   A variable, constraint or cost set can be identified by a single NAME,
   such as 'Pmismatch', or by a name that is indexed by one or more indices,
   such as 'Pmismatch(3,4)'. For an indexed named set, before adding the
   indexed variable, constraint or cost sets themselves, the dimensions of
   the indexed set must be set by calling INIT_INDEXED_NAME, where
   DIM_LIST is a cell array of the dimensions.

   Examples:
       %% linear constraints with indexed named set 'R(i,j)'
       om.init_indexed_name('lin', 'R', {2, 3});
       for i = 1:2
         for j = 1:3
           om.add_lin_constraint('R', {i, j}, A{i,j}, ...);
         end
       end

   See also OPT_MODEL, ADD_VAR, ADD_LIN_CONSTRAINT, ADD_NLN_CONSTRAINT,
            ADD_QUAD_COST and ADD_NLN_COST.

0001 function om = init_indexed_name(om, set_type, name, dim_list)
0002 %INIT_INDEXED_NAME  Initializes the dimensions for an indexed named set.
0003 %
0004 %   OM.INIT_INDEXED_NAME(SET_TYPE, NAME, DIM_LIST)
0005 %
0006 %   Initializes the dimensions for an indexed named variable, constraint
0007 %   or cost set.
0008 %
0009 %   Variables, constraints and costs are referenced in OPT_MODEL in terms
0010 %   of named sets. The specific type of named set being referenced is
0011 %   given by SET_TYPE, with the following valid options:
0012 %       SET_TYPE = 'var'   => variable set
0013 %       SET_TYPE = 'lin'   => linear constraint set
0014 %       SET_TYPE = 'nle'   => nonlinear equality constraint set
0015 %       SET_TYPE = 'nli'   => nonlinear inequality constraint set
0016 %       SET_TYPE = 'qdc'   => quadratic cost set
0017 %       SET_TYPE = 'nlc'   => nonlinear cost set
0018 %
0019 %   Indexed Named Sets
0020 %
0021 %   A variable, constraint or cost set can be identified by a single NAME,
0022 %   such as 'Pmismatch', or by a name that is indexed by one or more indices,
0023 %   such as 'Pmismatch(3,4)'. For an indexed named set, before adding the
0024 %   indexed variable, constraint or cost sets themselves, the dimensions of
0025 %   the indexed set must be set by calling INIT_INDEXED_NAME, where
0026 %   DIM_LIST is a cell array of the dimensions.
0027 %
0028 %   Examples:
0029 %       %% linear constraints with indexed named set 'R(i,j)'
0030 %       om.init_indexed_name('lin', 'R', {2, 3});
0031 %       for i = 1:2
0032 %         for j = 1:3
0033 %           om.add_lin_constraint('R', {i, j}, A{i,j}, ...);
0034 %         end
0035 %       end
0036 %
0037 %   See also OPT_MODEL, ADD_VAR, ADD_LIN_CONSTRAINT, ADD_NLN_CONSTRAINT,
0038 %            ADD_QUAD_COST and ADD_NLN_COST.
0039 
0040 %   MP-Opt-Model
0041 %   Copyright (c) 2008-2020, Power Systems Engineering Research Center (PSERC)
0042 %   by Ray Zimmerman, PSERC Cornell
0043 %
0044 %   This file is part of MP-Opt-Model.
0045 %   Covered by the 3-clause BSD License (see LICENSE file for details).
0046 %   See https://github.com/MATPOWER/mp-opt-model for more info.
0047 
0048 %% use column vector if single dimension
0049 if length(dim_list) == 1
0050     dim_list = {dim_list{:}, 1};
0051 end
0052 
0053 %% call parent method (also checks for valid type for named set)
0054 om = init_indexed_name@mp_idx_manager(om, set_type, name, dim_list);
0055 
0056 %% add type-specific info about this named set
0057 empty_cell  = cell(dim_list{:});
0058 switch set_type
0059     case 'var'          %% variable set
0060         om.var.data.v0.(name)   = empty_cell;   %% initial value
0061         om.var.data.vl.(name)   = empty_cell;   %% lower bound
0062         om.var.data.vu.(name)   = empty_cell;   %% upper bound
0063         om.var.data.vt.(name)   = empty_cell;   %% variable type
0064     case 'lin'          %% linear constraint set
0065         om.lin.data.A.(name)    = empty_cell;
0066         om.lin.data.l.(name)    = empty_cell;
0067         om.lin.data.u.(name)    = empty_cell;
0068         om.lin.data.vs.(name)   = empty_cell;
0069     case 'nle'          %% nonlinear equality constraint set
0070         om.nle.data.fcn.(name)  = empty_cell;
0071         om.nle.data.hess.(name) = empty_cell;
0072         om.nle.data.vs.(name)   = empty_cell;
0073     case 'nli'          %% nonlinear inequality constraint set
0074         om.nli.data.fcn.(name)  = empty_cell;
0075         om.nli.data.hess.(name) = empty_cell;
0076         om.nli.data.vs.(name)   = empty_cell;
0077     case 'nlc'          %% nonlinear cost set
0078         om.nlc.data.fcn.(name)  = empty_cell;
0079         om.nlc.data.vs.(name)   = empty_cell;
0080     case 'qdc'          %% quadratic cost set
0081         om.qdc.data.Q.(name)    = empty_cell;
0082         om.qdc.data.c.(name)    = empty_cell;
0083         om.qdc.data.k.(name)    = empty_cell;
0084         om.qdc.data.vs.(name)   = empty_cell;
0085 end