PARAMS_LIN_CONSTRAINT Builds and returns linear constraint parameters.
[A, L, U] = OM.PARAMS_LIN_CONSTRAINT()
[A, L, U] = OM.PARAMS_LIN_CONSTRAINT(NAME)
[A, L, U] = OM.PARAMS_LIN_CONSTRAINT(NAME, IDX)
[A, L, U, VS] = OM.PARAMS_LIN_CONSTRAINT(...)
[A, L, U, VS, I1, IN] = OM.PARAMS_LIN_CONSTRAINT(...)
With no input parameters, it assembles and returns the parameters
for the aggregate linear constraints from all linear constraint sets
added using ADD_LIN_CONSTRAINT. The values of these parameters are
cached for subsequent calls. The parameters are A, L and U where the
linear constraint is of the form
L <= A * x <= U
If a NAME is provided then it simply returns the parameters for the
corresponding named set. Likewise for indexed named sets specified
by NAME and IDX.
An optional 4th output argument VS indicates the variable sets used by
this cost set. The size of A will be consistent with VS.
If NAME is provided, optional 5th and 6th output arguments I1 and IN
indicate the starting and ending row indices of the corresponding
constraint set in the full aggregate constraint matrix.
Examples:
[A, l, u] = om.params_lin_constraint();
[A, l, u, vs, i1, i2] = om.params_lin_constraint('Pmis');
See also OPT_MODEL, ADD_LIN_CONSTRAINT.0001 function [A, l, u, vs, i1, iN] = params_lin_constraint(om, name, idx) 0002 %PARAMS_LIN_CONSTRAINT Builds and returns linear constraint parameters. 0003 % [A, L, U] = OM.PARAMS_LIN_CONSTRAINT() 0004 % [A, L, U] = OM.PARAMS_LIN_CONSTRAINT(NAME) 0005 % [A, L, U] = OM.PARAMS_LIN_CONSTRAINT(NAME, IDX) 0006 % [A, L, U, VS] = OM.PARAMS_LIN_CONSTRAINT(...) 0007 % [A, L, U, VS, I1, IN] = OM.PARAMS_LIN_CONSTRAINT(...) 0008 % 0009 % With no input parameters, it assembles and returns the parameters 0010 % for the aggregate linear constraints from all linear constraint sets 0011 % added using ADD_LIN_CONSTRAINT. The values of these parameters are 0012 % cached for subsequent calls. The parameters are A, L and U where the 0013 % linear constraint is of the form 0014 % L <= A * x <= U 0015 % 0016 % If a NAME is provided then it simply returns the parameters for the 0017 % corresponding named set. Likewise for indexed named sets specified 0018 % by NAME and IDX. 0019 % 0020 % An optional 4th output argument VS indicates the variable sets used by 0021 % this cost set. The size of A will be consistent with VS. 0022 % 0023 % If NAME is provided, optional 5th and 6th output arguments I1 and IN 0024 % indicate the starting and ending row indices of the corresponding 0025 % constraint set in the full aggregate constraint matrix. 0026 % 0027 % Examples: 0028 % [A, l, u] = om.params_lin_constraint(); 0029 % [A, l, u, vs, i1, i2] = om.params_lin_constraint('Pmis'); 0030 % 0031 % See also OPT_MODEL, ADD_LIN_CONSTRAINT. 0032 0033 % MATPOWER 0034 % Copyright (c) 2008-2017, Power Systems Engineering Research Center (PSERC) 0035 % by Ray Zimmerman, PSERC Cornell 0036 % 0037 % This file is part of MATPOWER. 0038 % Covered by the 3-clause BSD License (see LICENSE file for details). 0039 % See https://matpower.org for more info. 0040 0041 if nargin > 1 %% individual set 0042 if nargin < 3 0043 idx = {}; 0044 end 0045 if isempty(idx) 0046 if prod(size(om.lin.idx.i1.(name))) == 1 0047 A = om.lin.data.A.(name); 0048 l = om.lin.data.l.(name); 0049 u = om.lin.data.u.(name); 0050 if nargout > 3 0051 vs = om.lin.data.vs.(name); 0052 if nargout > 5 0053 i1 = om.lin.idx.i1.(name); %% starting row index 0054 iN = om.lin.idx.iN.(name); %% ending row index 0055 end 0056 end 0057 else 0058 error('@opt_model/params_lin_constraint: linear constraint set ''%s'' requires an IDX arg', name); 0059 end 0060 else 0061 % (calls to substruct() are relatively expensive ... 0062 % s = substruct('.', name, '{}', idx); 0063 % ... so replace it with these more efficient lines) 0064 sc = struct('type', {'.', '{}'}, 'subs', {name, idx}); 0065 A = subsref(om.lin.data.A, sc); 0066 l = subsref(om.lin.data.l, sc); 0067 u = subsref(om.lin.data.u, sc); 0068 if nargout > 3 0069 vs = subsref(om.lin.data.vs, sc); 0070 if nargout > 5 0071 sn = sc; sn(2).type = '()'; %% num array field 0072 i1 = subsref(om.lin.idx.i1, sn); %% starting row index 0073 iN = subsref(om.lin.idx.iN, sn); %% ending row index 0074 end 0075 end 0076 end 0077 else %% aggregate 0078 cache = om.lin.params; 0079 if isempty(cache) %% build the aggregate 0080 nx = om.var.N; %% number of variables 0081 nlin = om.lin.N; %% number of linear constraints 0082 At = sparse(nx, nlin); %% transpose of constraint matrix 0083 u = Inf(nlin, 1); %% upper bound 0084 l = -u; %% lower bound 0085 0086 %% fill in each piece 0087 for k = 1:om.lin.NS 0088 name = om.lin.order(k).name; 0089 idx = om.lin.order(k).idx; 0090 [Ak, lk, uk, vs, i1, iN] = om.params_lin_constraint(name, idx); 0091 [mk, nk] = size(Ak); %% size of Ak 0092 if mk 0093 Akt_full = sparse(nx, nlin); 0094 if isempty(vs) 0095 if nk == nx %% full size 0096 Akt_full(:, i1:iN) = Ak'; 0097 else %% vars added since adding this cost set 0098 Ak_all_cols = sparse(mk, nx); 0099 Ak_all_cols(:, 1:nk) = Ak; 0100 Akt_full(:, i1:iN) = Ak_all_cols'; 0101 end 0102 else 0103 jj = om.varsets_idx(vs); %% indices for var set 0104 Ak_all_cols = sparse(mk, nx); 0105 Ak_all_cols(:, jj) = Ak; 0106 Akt_full(:, i1:iN) = Ak_all_cols'; 0107 end 0108 At = At + Akt_full; 0109 l(i1:iN) = lk; 0110 u(i1:iN) = uk; 0111 end 0112 end 0113 A = At'; 0114 0115 %% cache aggregated parameters 0116 om.lin.params = struct('A', A, 'l', l, 'u', u); 0117 else %% return cached values 0118 A = cache.A; 0119 l = cache.l; 0120 u = cache.u; 0121 end 0122 if nargout > 3 0123 vs = {}; 0124 end 0125 end