100 lines
3.0 KiB
Mathematica
100 lines
3.0 KiB
Mathematica
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function output = callmosek(model)
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% Retrieve needed data
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options = model.options;
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F_struc = model.F_struc;
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c = model.c;
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Q = model.Q;
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K = model.K;
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x0 = model.x0;
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integer_variables = model.integer_variables;
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binary_variables = model.binary_variables;
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extended_variables = model.extended_variables;
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ub = model.ub;
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lb = model.lb;
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mt = model.monomtable;
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% *********************************
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% What type of variables do we have
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% *********************************
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model.linear_variables = find((sum(abs(mt),2)==1) & (any(mt==1,2)));
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model.nonlinear_variables = setdiff((1:size(mt,1))',model.linear_variables);
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model.sigmonial_variables = find(any(0>mt,2) | any(mt-fix(mt),2));
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% Some meta-solver thinks we handle binaries
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if ~isempty(model.binary_variables)
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integer_variables = union(model.integer_variables, model.binary_variables);
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if isempty(lb)
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lb = repmat(-inf,1,length(model.c));
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end
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lb(model.binary_variables) = max(lb(model.binary_variables),0);
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if isempty(ub)
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ub = repmat(inf,1,length(model.c));
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end
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ub(model.binary_variables) = min(ub(model.binary_variables),1);
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model.lb = lb;
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model.ub = ub;
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model.integer_variables = integer_variables;
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end
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% Some meta solvers might construct model with empty cones
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if any(model.K.s) && any(model.K.s == 0)
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model.K.s(model.K.s==0)=[];
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end
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if any(model.K.q) && any(model.K.q == 0)
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model.K.q(model.K.q==0)=[];
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end
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if ~isempty(model.sigmonial_variables) | isequal(model.solver.version,'GEOMETRIC')
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[x,D_struc,problem,r,res,solvertime,prob] = call_mosek_geometric(model);
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else
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[x,D_struc,problem,r,res,solvertime,prob] = call_mosek_lpqpsocpsdp(model);
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end
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% YALMIP has introduced internal variables for socp/exp cones etc
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if length(x) > 0 && length(x) ~= length(model.c)
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x = x(1:length(model.c));
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end
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infostr = yalmiperror(problem,'MOSEK');
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% Save all data sent to solver?
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if options.savesolverinput
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solverinput.prob = prob;
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solverinput.param = options.mosek;
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else
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solverinput = [];
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end
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% Save all data from the solver?
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if options.savesolveroutput
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solveroutput.r = r;
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solveroutput.res = res;
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else
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solveroutput = [];
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end
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% Standard interface
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output = createOutputStructure(x,D_struc,[],problem,infostr,solverinput,solveroutput,solvertime);
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function [x,D_struc,problem,r,res,solvertime,prob] = call_mosek_lpqpsocpsdp(model);
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[model,output] = normalizeExponentialCone(model);
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if output.problem
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error('Failed in normalizing exponential cone operators')
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end
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if nnz(model.Q)==0 && isempty(model.integer_variables) && isempty(model.x0) && model.K.e==0
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% Standard cone problem which we can model by sending our standard dual
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% and then recover solution via Moseks dual
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[x,D_struc,problem,r,res,solvertime,prob] = call_mosek_dual(model);
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else
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% Integer conic program
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% Quadratic objective
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% Exponential cones
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[x,D_struc,problem,r,res,solvertime,prob] = call_mosek_primal(model);
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end
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