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 and bounds for optimization variables:
get_v
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
.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)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 and bounds for optimization variables: 0026 % get_v 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 % .order - struct array of names/indices for variable 0075 % blocks in the order they appear in x 0076 % .name - name of the block, e.g. Pg 0077 % .idx - indices for name, {2,3} => Pg(2,3) 0078 % .nln - data for nonlinear constraints that make up the 0079 % full set of nonlinear constraints ghn(x) 0080 % .idx 0081 % .i1 - starting index within ghn(x) 0082 % .iN - ending index within ghn(x) 0083 % .N - number of elements in this constraint set 0084 % .N - total number of elements in ghn(x) 0085 % .NS - number of nonlinear constraint sets or named blocks 0086 % .order - struct array of names/indices for nonlinear constraint 0087 % blocks in the order they appear in ghn(x) 0088 % .name - name of the block, e.g. Pmis 0089 % .idx - indices for name, {2,3} => Pmis(2,3) 0090 % .lin - data for linear constraints that make up the 0091 % full set of linear constraints ghl(x) 0092 % .idx 0093 % .i1 - starting index within ghl(x) 0094 % .iN - ending index within ghl(x) 0095 % .N - number of elements in this constraint set 0096 % .N - total number of elements in ghl(x) 0097 % .NS - number of linear constraint sets or named blocks 0098 % .data - data for l <= A*xx <= u linear constraints 0099 % .A - sparse linear constraint matrix 0100 % .l - left hand side vector, bounding A*x below 0101 % .u - right hand side vector, bounding A*x above 0102 % .vs - cell array of variable sets that define the xx for 0103 % this constraint block 0104 % .order - struct array of names/indices for linear constraint 0105 % blocks in the order they appear in ghl(x) 0106 % .name - name of the block, e.g. Pmis 0107 % .idx - indices for name, {2,3} => Pmis(2,3) 0108 % .cost - data for user-defined costs 0109 % .idx 0110 % .i1 - starting row index within full N matrix 0111 % .iN - ending row index within full N matrix 0112 % .N - number of rows in this cost block in full N matrix 0113 % .N - total number of rows in full N matrix 0114 % .NS - number of cost blocks 0115 % .data - data for each user-defined cost block 0116 % .N - see help for ADD_COSTS for details 0117 % .H - " 0118 % .Cw - " 0119 % .dd - " 0120 % .rr - " 0121 % .kk - " 0122 % .mm - " 0123 % .vs - cell array of variable sets that define xx for this 0124 % cost block, where the N for this block multiplies xx 0125 % .order - struct array of names/indices for cost blocks in the 0126 % order they appear in the rows of the full N matrix 0127 % .name - name of the block, e.g. R 0128 % .idx - indices for name, {2,3} => R(2,3) 0129 % .userdata - any user defined data added via USERDATA 0130 % .(user defined fields) 0131 0132 % MATPOWER 0133 % $Id: opt_model.m 2208 2013-10-11 20:19:14Z ray $ 0134 % by Ray Zimmerman, PSERC Cornell 0135 % Copyright (c) 2008-2012 by Power System Engineering Research Center (PSERC) 0136 % 0137 % This file is part of MATPOWER. 0138 % See http://www.pserc.cornell.edu/matpower/ for more info. 0139 % 0140 % MATPOWER is free software: you can redistribute it and/or modify 0141 % it under the terms of the GNU General Public License as published 0142 % by the Free Software Foundation, either version 3 of the License, 0143 % or (at your option) any later version. 0144 % 0145 % MATPOWER is distributed in the hope that it will be useful, 0146 % but WITHOUT ANY WARRANTY; without even the implied warranty of 0147 % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 0148 % GNU General Public License for more details. 0149 % 0150 % You should have received a copy of the GNU General Public License 0151 % along with MATPOWER. If not, see <http://www.gnu.org/licenses/>. 0152 % 0153 % Additional permission under GNU GPL version 3 section 7 0154 % 0155 % If you modify MATPOWER, or any covered work, to interface with 0156 % other modules (such as MATLAB code and MEX-files) available in a 0157 % MATLAB(R) or comparable environment containing parts covered 0158 % under other licensing terms, the licensors of MATPOWER grant 0159 % you additional permission to convey the resulting work. 0160 0161 es = struct(); 0162 if nargin == 0 0163 om.var.idx.i1 = es; 0164 om.var.idx.iN = es; 0165 om.var.idx.N = es; 0166 om.var.N = 0; 0167 om.var.NS = 0; 0168 om.var.order = struct('name', [], 'idx', []); 0169 om.var.data.v0 = es; 0170 om.var.data.vl = es; 0171 om.var.data.vu = es; 0172 0173 om.nln.idx.i1 = es; 0174 om.nln.idx.iN = es; 0175 om.nln.idx.N = es; 0176 om.nln.N = 0; 0177 om.nln.NS = 0; 0178 om.nln.order = struct('name', [], 'idx', []); 0179 0180 om.lin.idx.i1 = es; 0181 om.lin.idx.iN = es; 0182 om.lin.idx.N = es; 0183 om.lin.N = 0; 0184 om.lin.NS = 0; 0185 om.lin.order = struct('name', [], 'idx', []); 0186 om.lin.data.A = es; 0187 om.lin.data.l = es; 0188 om.lin.data.u = es; 0189 om.lin.data.vs = es; 0190 0191 om.cost.idx.i1 = es; 0192 om.cost.idx.iN = es; 0193 om.cost.idx.N = es; 0194 om.cost.N = 0; 0195 om.cost.NS = 0; 0196 om.cost.order = struct('name', [], 'idx', []); 0197 om.cost.data.N = es; 0198 om.cost.data.H = es; 0199 om.cost.data.Cw = es; 0200 om.cost.data.dd = es; 0201 om.cost.data.rh = es; 0202 om.cost.data.kk = es; 0203 om.cost.data.mm = es; 0204 om.cost.data.vs = es; 0205 om.cost.params = es; 0206 0207 om.userdata = es; 0208 0209 om = class(om, 'opt_model'); 0210 elseif isa(s,'opt_model') 0211 om = s; 0212 elseif isfield(s, 'var') && isfield(s, 'nln') && ... 0213 isfield(s, 'lin') && isfield(s, 'cost') && isfield(s, 'userdata') 0214 om = class(s, 'opt_model'); 0215 else 0216 error('@opt_model/opt_model: input must be an OPT_MODEL object or struct with corresponding fields'); 0217 end