Home > matpower5.1 > @opt_model > opt_model.m

opt_model

PURPOSE ^

OPT_MODEL Constructor for optimization model class.

SYNOPSIS ^

function om = opt_model(s)

DESCRIPTION ^

OPT_MODEL  Constructor for optimization model class.
   OM = OPT_MODEL
   OM = OPT_MODEL(S)

   This class implements the optimization model object used to encapsulate
   a given optimization problem formulation. It allows for access to
   optimization variables, constraints and costs in named blocks, keeping
   track of the ordering and indexing of the blocks as variables,
   constraints and costs are added to the problem.

   Below are the list of available methods for use with the Opt Model class.
   Please see the help on each individual method for more details:

   Modify the OPF formulation by adding named blocks of constraints, costs
   or variables:
       add_constraints
       add_costs
       add_vars

   Return the number of linear constraints, nonlinear constraints,
   variables or cost rows, optionally for a single named block:
       getN

   Return the intial values, bounds and type for optimization variables:
       getv

   Build and return full set of linear constraints:
       linear_constraints

   Return index structure for variables, linear and nonlinear constraints
   and costs:
       get_idx

   Build and return cost parameters and evaluate user-defined costs:
       build_cost_params
       get_cost_params
       compute_cost

   Save/retreive user data in the model object:
       userdata

   Display the object (called automatically when you omit the semicolon
   at the command-line):
       display

   Return the value of an individual field:
       get

   Indentify variable, constraint or cost row indices:
       describe_idx

   The following is the structure of the data in the OPF model object.
   Each field of .idx or .data is a struct whose field names are the names
   of the corresponding blocks of vars, constraints or costs (found in
   order in the corresponding .order field). The description next to these
   fields gives the meaning of the value for each named sub-field.
   E.g. om.var.data.v0.Pg contains a vector of initial values for the 'Pg'
   block of variables.

   om
       .var        - data for optimization variable sets that make up
                     the full optimization variable x
           .idx
               .i1 - starting index within x
               .iN - ending index within x
               .N  - number of elements in this variable set
           .N      - total number of elements in x
           .NS     - number of variable sets or named blocks
           .data   - bounds and initial value data
               .v0 - vector of initial values
               .vl - vector of lower bounds
               .vu - vector of upper bounds
               .vt - scalar or vector of variable types
                       'C' = continuous
                       'I' = integer
                       'B' = binary
           .order  - struct array of names/indices for variable
                     blocks in the order they appear in x
               .name   - name of the block, e.g. Pg
               .idx    - indices for name, {2,3} => Pg(2,3)
       .nln        - data for nonlinear constraints that make up the
                     full set of nonlinear constraints ghn(x)
           .idx
               .i1 - starting index within ghn(x)
               .iN - ending index within ghn(x)
               .N  - number of elements in this constraint set
           .N      - total number of elements in ghn(x)
           .NS     - number of nonlinear constraint sets or named blocks
           .order  - struct array of names/indices for nonlinear constraint
                     blocks in the order they appear in ghn(x)
               .name   - name of the block, e.g. Pmis
               .idx    - indices for name, {2,3} => Pmis(2,3)
       .lin        - data for linear constraints that make up the
                     full set of linear constraints ghl(x)
           .idx
               .i1 - starting index within ghl(x)
               .iN - ending index within ghl(x)
               .N  - number of elements in this constraint set
           .N      - total number of elements in ghl(x)
           .NS     - number of linear constraint sets or named blocks
           .data   - data for l <= A*xx <= u linear constraints
               .A  - sparse linear constraint matrix
               .l  - left hand side vector, bounding A*x below
               .u  - right hand side vector, bounding A*x above
               .vs - cell array of variable sets that define the xx for
                     this constraint block
           .order  - struct array of names/indices for linear constraint
                     blocks in the order they appear in ghl(x)
               .name   - name of the block, e.g. Pmis
               .idx    - indices for name, {2,3} => Pmis(2,3)
       .cost       - data for user-defined costs
           .idx
               .i1 - starting row index within full N matrix
               .iN - ending row index within full N matrix
               .N  - number of rows in this cost block in full N matrix
           .N      - total number of rows in full N matrix
           .NS     - number of cost blocks
           .data   - data for each user-defined cost block
               .N  - see help for ADD_COSTS for details
               .H  -               "
               .Cw -               "
               .dd -               "
               .rr -               "
               .kk -               "
               .mm -               "
               .vs - cell array of variable sets that define xx for this
                     cost block, where the N for this block multiplies xx
           .order  - struct array of names/indices for cost blocks in the
                     order they appear in the rows of the full N matrix
               .name   - name of the block, e.g. R
               .idx    - indices for name, {2,3} => R(2,3)
       .userdata   - any user defined data added via USERDATA
           .(user defined fields)

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function om = opt_model(s)
0002 %OPT_MODEL  Constructor for optimization model class.
0003 %   OM = OPT_MODEL
0004 %   OM = OPT_MODEL(S)
0005 %
0006 %   This class implements the optimization model object used to encapsulate
0007 %   a given optimization problem formulation. It allows for access to
0008 %   optimization variables, constraints and costs in named blocks, keeping
0009 %   track of the ordering and indexing of the blocks as variables,
0010 %   constraints and costs are added to the problem.
0011 %
0012 %   Below are the list of available methods for use with the Opt Model class.
0013 %   Please see the help on each individual method for more details:
0014 %
0015 %   Modify the OPF formulation by adding named blocks of constraints, costs
0016 %   or variables:
0017 %       add_constraints
0018 %       add_costs
0019 %       add_vars
0020 %
0021 %   Return the number of linear constraints, nonlinear constraints,
0022 %   variables or cost rows, optionally for a single named block:
0023 %       getN
0024 %
0025 %   Return the intial values, bounds and type for optimization variables:
0026 %       getv
0027 %
0028 %   Build and return full set of linear constraints:
0029 %       linear_constraints
0030 %
0031 %   Return index structure for variables, linear and nonlinear constraints
0032 %   and costs:
0033 %       get_idx
0034 %
0035 %   Build and return cost parameters and evaluate user-defined costs:
0036 %       build_cost_params
0037 %       get_cost_params
0038 %       compute_cost
0039 %
0040 %   Save/retreive user data in the model object:
0041 %       userdata
0042 %
0043 %   Display the object (called automatically when you omit the semicolon
0044 %   at the command-line):
0045 %       display
0046 %
0047 %   Return the value of an individual field:
0048 %       get
0049 %
0050 %   Indentify variable, constraint or cost row indices:
0051 %       describe_idx
0052 %
0053 %   The following is the structure of the data in the OPF model object.
0054 %   Each field of .idx or .data is a struct whose field names are the names
0055 %   of the corresponding blocks of vars, constraints or costs (found in
0056 %   order in the corresponding .order field). The description next to these
0057 %   fields gives the meaning of the value for each named sub-field.
0058 %   E.g. om.var.data.v0.Pg contains a vector of initial values for the 'Pg'
0059 %   block of variables.
0060 %
0061 %   om
0062 %       .var        - data for optimization variable sets that make up
0063 %                     the full optimization variable x
0064 %           .idx
0065 %               .i1 - starting index within x
0066 %               .iN - ending index within x
0067 %               .N  - number of elements in this variable set
0068 %           .N      - total number of elements in x
0069 %           .NS     - number of variable sets or named blocks
0070 %           .data   - bounds and initial value data
0071 %               .v0 - vector of initial values
0072 %               .vl - vector of lower bounds
0073 %               .vu - vector of upper bounds
0074 %               .vt - scalar or vector of variable types
0075 %                       'C' = continuous
0076 %                       'I' = integer
0077 %                       'B' = binary
0078 %           .order  - struct array of names/indices for variable
0079 %                     blocks in the order they appear in x
0080 %               .name   - name of the block, e.g. Pg
0081 %               .idx    - indices for name, {2,3} => Pg(2,3)
0082 %       .nln        - data for nonlinear constraints that make up the
0083 %                     full set of nonlinear constraints ghn(x)
0084 %           .idx
0085 %               .i1 - starting index within ghn(x)
0086 %               .iN - ending index within ghn(x)
0087 %               .N  - number of elements in this constraint set
0088 %           .N      - total number of elements in ghn(x)
0089 %           .NS     - number of nonlinear constraint sets or named blocks
0090 %           .order  - struct array of names/indices for nonlinear constraint
0091 %                     blocks in the order they appear in ghn(x)
0092 %               .name   - name of the block, e.g. Pmis
0093 %               .idx    - indices for name, {2,3} => Pmis(2,3)
0094 %       .lin        - data for linear constraints that make up the
0095 %                     full set of linear constraints ghl(x)
0096 %           .idx
0097 %               .i1 - starting index within ghl(x)
0098 %               .iN - ending index within ghl(x)
0099 %               .N  - number of elements in this constraint set
0100 %           .N      - total number of elements in ghl(x)
0101 %           .NS     - number of linear constraint sets or named blocks
0102 %           .data   - data for l <= A*xx <= u linear constraints
0103 %               .A  - sparse linear constraint matrix
0104 %               .l  - left hand side vector, bounding A*x below
0105 %               .u  - right hand side vector, bounding A*x above
0106 %               .vs - cell array of variable sets that define the xx for
0107 %                     this constraint block
0108 %           .order  - struct array of names/indices for linear constraint
0109 %                     blocks in the order they appear in ghl(x)
0110 %               .name   - name of the block, e.g. Pmis
0111 %               .idx    - indices for name, {2,3} => Pmis(2,3)
0112 %       .cost       - data for user-defined costs
0113 %           .idx
0114 %               .i1 - starting row index within full N matrix
0115 %               .iN - ending row index within full N matrix
0116 %               .N  - number of rows in this cost block in full N matrix
0117 %           .N      - total number of rows in full N matrix
0118 %           .NS     - number of cost blocks
0119 %           .data   - data for each user-defined cost block
0120 %               .N  - see help for ADD_COSTS for details
0121 %               .H  -               "
0122 %               .Cw -               "
0123 %               .dd -               "
0124 %               .rr -               "
0125 %               .kk -               "
0126 %               .mm -               "
0127 %               .vs - cell array of variable sets that define xx for this
0128 %                     cost block, where the N for this block multiplies xx
0129 %           .order  - struct array of names/indices for cost blocks in the
0130 %                     order they appear in the rows of the full N matrix
0131 %               .name   - name of the block, e.g. R
0132 %               .idx    - indices for name, {2,3} => R(2,3)
0133 %       .userdata   - any user defined data added via USERDATA
0134 %           .(user defined fields)
0135 
0136 %   MATPOWER
0137 %   Copyright (c) 2008-2015 by Power System Engineering Research Center (PSERC)
0138 %   by Ray Zimmerman, PSERC Cornell
0139 %
0140 %   $Id: opt_model.m 2644 2015-03-11 19:34:22Z ray $
0141 %
0142 %   This file is part of MATPOWER.
0143 %   Covered by the 3-clause BSD License (see LICENSE file for details).
0144 %   See http://www.pserc.cornell.edu/matpower/ for more info.
0145 
0146 es = struct();
0147 if nargin == 0
0148     om.var.idx.i1 = es;
0149     om.var.idx.iN = es;
0150     om.var.idx.N = es;
0151     om.var.N = 0;
0152     om.var.NS = 0;
0153     om.var.order = struct('name', [], 'idx', []);
0154     om.var.data.v0 = es;
0155     om.var.data.vl = es;
0156     om.var.data.vu = es;
0157     om.var.data.vt = es;
0158 
0159     om.nln.idx.i1 = es;
0160     om.nln.idx.iN = es;
0161     om.nln.idx.N = es;
0162     om.nln.N = 0;
0163     om.nln.NS = 0;
0164     om.nln.order = struct('name', [], 'idx', []);
0165 
0166     om.lin.idx.i1 = es;
0167     om.lin.idx.iN = es;
0168     om.lin.idx.N = es;
0169     om.lin.N = 0;
0170     om.lin.NS = 0;
0171     om.lin.order = struct('name', [], 'idx', []);
0172     om.lin.data.A = es;
0173     om.lin.data.l = es;
0174     om.lin.data.u = es;
0175     om.lin.data.vs = es;
0176     
0177     om.cost.idx.i1 = es;
0178     om.cost.idx.iN = es;
0179     om.cost.idx.N = es;
0180     om.cost.N = 0;
0181     om.cost.NS = 0;
0182     om.cost.order = struct('name', [], 'idx', []);
0183     om.cost.data.N = es;
0184     om.cost.data.H = es;
0185     om.cost.data.Cw = es;
0186     om.cost.data.dd = es;
0187     om.cost.data.rh = es;
0188     om.cost.data.kk = es;
0189     om.cost.data.mm = es;
0190     om.cost.data.vs = es;
0191     om.cost.params = es;
0192     
0193     om.userdata = es;
0194 
0195     om = class(om, 'opt_model');
0196 elseif isa(s,'opt_model') 
0197     om = s;
0198 elseif isfield(s, 'var') && isfield(s, 'nln') && ...
0199         isfield(s, 'lin') && isfield(s, 'cost') && isfield(s, 'userdata')
0200     om = class(s, 'opt_model');
0201 else
0202     error('@opt_model/opt_model: input must be an OPT_MODEL object or struct with corresponding fields');
0203 end

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