PARAMS_QUAD_COST Returns the cost parameters for quadratic costs.
[Q, C] = OM.PARAMS_QUAD_COST()
[Q, C] = OM.PARAMS_QUAD_COST(NAME)
[Q, C] = OM.PARAMS_QUAD_COST(NAME, IDX)
[Q, C, K] = OM.PARAMS_QUAD_COST(...)
[Q, C, K, VS] = OM.PARAMS_QUAD_COST(...)
With no input parameters, it assembles and returns the parameters
for the aggregate quadratic cost from all quadratic cost sets added
using ADD_QUAD_COST. The values of these parameters are cached
for subsequent calls. The parameters are Q, C, and optionally K,
where the quadratic cost is of the form
F(X) = 1/2 * X'*Q*X + C'*X + K
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. In this case, Q and K may be vectors, corresponding
to a cost function of the form
F(X) = 1/2 * Q .* X.^2 + C .* X + K
An optional 4th output argument VS indicates the variable sets used by
this cost set. The size of Q and C will be consistent with VS.
See also OPT_MODEL, ADD_QUAD_COST.0001 function [Q, c, k, vs] = params_quad_cost(om, name, idx) 0002 %PARAMS_QUAD_COST Returns the cost parameters for quadratic costs. 0003 % [Q, C] = OM.PARAMS_QUAD_COST() 0004 % [Q, C] = OM.PARAMS_QUAD_COST(NAME) 0005 % [Q, C] = OM.PARAMS_QUAD_COST(NAME, IDX) 0006 % [Q, C, K] = OM.PARAMS_QUAD_COST(...) 0007 % [Q, C, K, VS] = OM.PARAMS_QUAD_COST(...) 0008 % 0009 % With no input parameters, it assembles and returns the parameters 0010 % for the aggregate quadratic cost from all quadratic cost sets added 0011 % using ADD_QUAD_COST. The values of these parameters are cached 0012 % for subsequent calls. The parameters are Q, C, and optionally K, 0013 % where the quadratic cost is of the form 0014 % F(X) = 1/2 * X'*Q*X + C'*X + K 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. In this case, Q and K may be vectors, corresponding 0019 % to a cost function of the form 0020 % F(X) = 1/2 * Q .* X.^2 + C .* X + K 0021 % 0022 % An optional 4th output argument VS indicates the variable sets used by 0023 % this cost set. The size of Q and C will be consistent with VS. 0024 % 0025 % See also OPT_MODEL, ADD_QUAD_COST. 0026 0027 % MATPOWER 0028 % Copyright (c) 2017, Power Systems Engineering Research Center (PSERC) 0029 % by Ray Zimmerman, PSERC Cornell 0030 % 0031 % This file is part of MATPOWER. 0032 % Covered by the 3-clause BSD License (see LICENSE file for details). 0033 % See https://matpower.org for more info. 0034 0035 if nargin > 1 %% individual set 0036 if nargin < 3 0037 idx = {}; 0038 end 0039 if isempty(idx) %% name, no index provided 0040 if prod(size(om.qdc.idx.i1.(name))) == 1 %% simple named set 0041 Q = om.qdc.data.Q.(name); 0042 c = om.qdc.data.c.(name); 0043 k = om.qdc.data.k.(name); 0044 if nargout > 3 0045 vs = om.qdc.data.vs.(name); 0046 end 0047 else %% indexing required 0048 error('@opt_model/params_quad_cost: quadratic cost set ''%s'' requires an IDX arg', name); 0049 end 0050 else %% indexed named set 0051 % (calls to substruct() are relatively expensive ... 0052 % s = substruct('.', name, '{}', idx); 0053 % ... so replace it with these more efficient lines) 0054 sc = struct('type', {'.', '{}'}, 'subs', {name, idx}); 0055 Q = subsref(om.qdc.data.Q, sc); 0056 c = subsref(om.qdc.data.c, sc); 0057 k = subsref(om.qdc.data.k, sc); 0058 if nargout > 3 0059 vs = subsref(om.qdc.data.vs, sc); 0060 end 0061 end 0062 else %% aggregate 0063 cache = om.qdc.params; 0064 if isempty(cache) %% build the aggregate 0065 nx = om.var.N; %% number of variables 0066 Qt = sparse(nx, nx); %% transpose of quadratic coefficients 0067 c = zeros(nx, 1); %% linear coefficients 0068 k = 0; %% constant term 0069 for i = 1:om.qdc.NS 0070 name = om.qdc.order(i).name; 0071 idx = om.qdc.order(i).idx; 0072 [Qk, ck, kk, vs] = om.params_quad_cost(name, idx); 0073 haveQ = ~isempty(Qk); 0074 havec = ~isempty(ck); 0075 nk = max(size(Qk, 1), size(ck, 1)); %% size of Qk and/or ck 0076 if isempty(vs) 0077 if nk == nx %% full size 0078 if size(Qk, 2) == 1 %% Qk is a column vector 0079 Qkt_full = spdiags(Qk, 0, nx, nx); 0080 elseif haveQ %% Qk is a matrix 0081 Qkt_full = Qk'; 0082 end 0083 if havec 0084 ck_full = ck; 0085 end 0086 else %% vars added since adding this cost set 0087 if size(Qk, 2) == 1 %% Qk is a column vector 0088 Qkt_full = sparse(1:nk, 1:nk, Qk, nx, nx); 0089 elseif haveQ %% Qk is a matrix 0090 Qk_all_cols = sparse(nk, nx); 0091 Qk_all_cols(:, 1:nk) = Qk; 0092 Qkt_full(:, 1:nk) = Qk_all_cols'; 0093 end 0094 if havec 0095 ck_full = zeros(nx, 1); 0096 ck_full(1:nk) = ck; 0097 end 0098 end 0099 else 0100 jj = om.varsets_idx(vs); %% indices for var set 0101 if size(Qk, 2) == 1 %% Qk is a column vector 0102 Qkt_full = sparse(jj, jj, Qk, nx, nx); 0103 elseif haveQ %% Qk is a matrix 0104 Qk_all_cols = sparse(nk, nx); 0105 Qk_all_cols(:, jj) = Qk; 0106 Qkt_full = sparse(nx, nx); 0107 Qkt_full(:, jj) = Qk_all_cols'; 0108 end 0109 if havec 0110 ck_full = zeros(nx, 1); 0111 ck_full(jj) = ck; 0112 end 0113 end 0114 if haveQ 0115 Qt = Qt + Qkt_full; 0116 end 0117 if havec 0118 c = c + ck_full; 0119 end 0120 k = k + sum(kk); 0121 end 0122 Q = Qt'; 0123 0124 %% cache aggregated parameters 0125 om.qdc.params = struct('Q', Q, 'c', c, 'k', k); 0126 else %% return cached values 0127 Q = cache.Q; 0128 c = cache.c; 0129 k = cache.k; 0130 end 0131 if nargout > 3 0132 vs = {}; 0133 end 0134 end