eval_nln_constraint

EVAL_NLN_CONSTRAINT  Builds and returns full set of nonlinear constraints.
   [G, DG] = OM.EVAL_NLN_CONSTRAINT(X, ISEQ)
   Builds a full set of nonlinear equality or inequality constraints and
   their gradients for a given value of the optimization vector X,
   based on constraints added by ADD_NLN_CONSTRAINT.

       g(X) = 0
       h(X) <= 0

   Example:
       [g, dg] = om.eval_nln_constraint(x, 1)
       [h, dh] = om.eval_nln_constraint(x, 0)

   See also OPT_MODEL, ADD_NLN_CONSTRAINT, EVAL_NLN_CONSTRAINT_HESS.

0001 function [g, dg] = eval_nln_constraint(om, x, iseq)
0002 %EVAL_NLN_CONSTRAINT  Builds and returns full set of nonlinear constraints.
0003 %   [G, DG] = OM.EVAL_NLN_CONSTRAINT(X, ISEQ)
0004 %   Builds a full set of nonlinear equality or inequality constraints and
0005 %   their gradients for a given value of the optimization vector X,
0006 %   based on constraints added by ADD_NLN_CONSTRAINT.
0007 %
0008 %       g(X) = 0
0009 %       h(X) <= 0
0010 %
0011 %   Example:
0012 %       [g, dg] = om.eval_nln_constraint(x, 1)
0013 %       [h, dh] = om.eval_nln_constraint(x, 0)
0014 %
0015 %   See also OPT_MODEL, ADD_NLN_CONSTRAINT, EVAL_NLN_CONSTRAINT_HESS.
0016 
0017 %   MATPOWER
0018 %   Copyright (c) 2008-2017, Power Systems Engineering Research Center (PSERC)
0019 %   by Ray Zimmerman, PSERC Cornell
0020 %
0021 %   This file is part of MATPOWER.
0022 %   Covered by the 3-clause BSD License (see LICENSE file for details).
0023 %   See https://matpower.org for more info.
0024 
0025 %% get constraint type
0026 if iseq         %% equality constraints
0027     om_nlx = om.nle;
0028 else            %% inequality constraints
0029     om_nlx = om.nli;
0030 end
0031 
0032 %% initialize g, dg
0033 g = NaN(om_nlx.N, 1);
0034 dg = sparse(0, om.var.N);   %% build gradient by stacking
0035 
0036 %% calls to substruct() are relatively expensive, so we pre-build the
0037 %% structs for addressing cell and numeric array fields, updating only
0038 %% the subscripts before use
0039 sc = struct('type', {'.', '{}'}, 'subs', {'', 1});  %% cell array field
0040 sn = sc; sn(2).type = '()';                         %% num array field
0041 
0042 %% fill in each piece
0043 for k = 1:om_nlx.NS
0044     name = om_nlx.order(k).name;
0045     idx  = om_nlx.order(k).idx;
0046     if isempty(idx)
0047         if ~isfield(om_nlx.data.fcn, name)
0048             continue;   %% skip, there is no function handle stored here,
0049                         %% the function value for this named set was included
0050                         %% in the value computed by a previous named set
0051         end
0052         N = om_nlx.idx.N.(name);    %% number of constraint functions
0053                                     %% evaluated for this named set
0054         if isfield(om_nlx.data.include, name)
0055             N = N + sum(om_nlx.data.include.(name).N);
0056         end
0057     else
0058         % (calls to substruct() are relatively expensive ...
0059         % sn = substruct('.', name, '()', idx);
0060         % sc = substruct('.', name, '{}', idx);
0061         % ... so replace them with these more efficient lines)
0062         sn(1).subs = name;
0063         sn(2).subs = idx;
0064         sc(1).subs = name;
0065         sc(2).subs = idx;
0066         N = subsref(om_nlx.idx.N, sn);
0067     end
0068     if N                                %% non-zero number of rows
0069         if isempty(idx)
0070             fcn = om_nlx.data.fcn.(name);       %% fcn for kth constraint set
0071             i1 = om_nlx.idx.i1.(name);          %% starting row index
0072             iN = i1 + N - 1;                    %% ending row index
0073             vs = om_nlx.data.vs.(name);         %% var sets
0074         else
0075             fcn = subsref(om_nlx.data.fcn, sc); %% fcn for kth constraint set
0076             i1 = subsref(om_nlx.idx.i1, sn);    %% starting row index
0077             iN = subsref(om_nlx.idx.iN, sn);    %% ending row index
0078             vs = subsref(om_nlx.data.vs, sc);   %% var sets
0079         end
0080         xx = om.varsets_x(x, vs);
0081         [gk, dgk] = fcn(xx);    %% evaluate kth constraint and gradient
0082         g(i1:iN) = gk;          %% assign kth constraint
0083         
0084         if isempty(vs)          %% all rows of x
0085             if size(dgk, 2) == om.var.N
0086                 dg = [dg; dgk];
0087             else                %% must have added vars since adding
0088                                 %% this constraint set
0089                 dg(i1:iN, 1:size(dgk, 2)) = dgk;
0090             end
0091         else                    %% selected rows of x
0092             jj = om.varsets_idx(vs);    %% column indices for var set
0093             dgi = sparse(N, om.var.N);
0094             dgi(:, jj) = dgk;
0095             dg = [dg; dgi];
0096         end
0097     end
0098 end