compileinterfacedata.m

matlab波形优化算法经常要用到的matlab toolbox工具箱:yalmip

            solution = diagnostic;
            solution.variables = used_variables(:);
            solution.optvar = x_equ;
            % And we are done! Save the result
            % sdpvar('setSolution',solution);
            return
        end
        % We dont need the rows for equalities anymore
        oldF_struc = F_struc;
        oldc = c;
        oldQ = Q;
        oldK = K;
        F_struc = F_struc(K.f+1:end,:);
        K.f = 0;
        Fold = F;
        [nlmi neq]=size(F);
        iseq = is(Fold(1:(nlmi+neq)),'equality');
        F = Fold(find(~iseq));
        Fremoved = Fold(find(iseq));
%         
%         F = lmi;
%         Fremoved = set([]);
%         F = 
%         for i = 1:nlmi+neq
%             if ~is(Fold(i),'equality')
%                 F = F + Fold(i);
%             else
%                 Fremoved = Fremoved + Fold(i);
%             end
%         end

        % No variables left. Problem solved!
        if size(H,2)==0
            diagnostic.solvertime = 0;
            diagnostic.info = yalmiperror(0,'YALMIP');
            diagnostic.problem = 0;
            solution = diagnostic;
            solution.variables = used_variables(:);
            solution.optvar = x_equ;
            % And we are done! Save the result
            % Note, no dual is saved
            sdpvar('setSolution',solution);
            p = checkset(F);
            if any(p<1e-5)
                diagnostic.info = yalmiperror(1,'YALMIP');
                diagnostic.problem = 1;
            end
            return
        end
        showprogress('Converting problem to new basis',options.showprogress)

        % objective in new basis
        f = f + x_equ'*Q*x_equ;
        c = H'*c + 2*H'*Q*x_equ;
        Q = H'*Q*H;Q=((Q+Q')/2);
        % LMI in new basis
        F_struc = [F_struc*[1;x_equ] F_struc(:,2:end)*H];
    else
        % Solver does not support equality constraints and user specifies
        % double-sided inequalitis to remove them
        if (solver.constraint.equalities.linear==0 | options.removeequalities==-1)
            % Add equalities
            F_struc = [-F_struc(1:1:K.f,:);F_struc];
            K.l = K.l+K.f*2;
            % Keep this in mind...
            K.fold = K.f;
            K.f = 0;
        end
        % For simpliciy we introduce a dummy coordinate change
        x_equ   = 0;
        H       = 1;
        factors = [];
    end
else
    x_equ   = 0;
    H       = 1;
    factors = [];
end


% *************************************************************************
%% Setup the initial solution
% *************************************************************************
x0 = [];
if options.usex0
    if options.relax
        x0_used = relaxdouble(recover(used_variables));
    else
        %FIX : Do directly using yalmip('solution')
        x0_used = double(recover(used_variables));
    end
    x0 = zeros(sdpvar('nvars'),1);
    x0(used_variables)  = x0_used(:);
    x0(isnan(x0))=0;
end
if ~isempty(x0)
    % Get a coordinate in the reduced space
    x0 = H\(x0(used_variables)-x_equ);
end

% Monomial table for nonlinear variables
% FIX : Why here!!! mt handled above also
[mt,variabletype] = yalmip('monomtable');
if size(mt,1)>size(mt,2)
    mt(size(mt,1),size(mt,1)) = 0;
end
% In local variables
mt = mt(used_variables,used_variables);
variabletype = variabletype(used_variables);
if (options.relax)|(options.relax==3)
    mt = eye(length(used_variables));
end

% FIX : Make sure these things work...
lub = yalmip('getbounds',used_variables);
lb = lub(:,1)-inf;
ub = lub(:,2)+inf;
lb(old_binary_variables) = max(lb(old_binary_variables),0);
ub(old_binary_variables) = min(ub(old_binary_variables),1);

% This does not work if we have used removeequalities, so we clear them for
% safety. note that bounds are not guaranteed to be used according to the
% manual, so this is allowed, although it might be a bit inconsistent to
% some users.
if ~isempty(oldc)
    lb = [];
    ub = [];
end

% *************************************************************************
%% GENERAL DATA EXCHANGE WITH SOLVER
% *************************************************************************
interfacedata.F_struc = F_struc;
interfacedata.c = c;
interfacedata.Q = Q;
interfacedata.f = f;
interfacedata.K = K;
interfacedata.lb = lb;
interfacedata.ub = ub;
interfacedata.x0 = x0;
interfacedata.options = options;
interfacedata.solver  = solver;
interfacedata.monomtable = mt;
interfacedata.variabletype = variabletype;
interfacedata.integer_variables   = integer_variables;
interfacedata.binary_variables    = binary_variables;
interfacedata.uncertain_variables = [];
interfacedata.parametric_variables= parametric_variables;
interfacedata.extended_variables  = extended_variables;
interfacedata.used_variables      = used_variables;
interfacedata.lowrankdetails = lowrankdetails;
interfacedata.problemclass = ProblemClass;
interfacedata.KCut = KCut;
interfacedata.getsolvertime = 1;
% Data to be able to recover duals when model is reduced
interfacedata.oldF_struc = oldF_struc;
interfacedata.oldc = oldc;
interfacedata.oldK = oldK;
interfacedata.factors = factors;
interfacedata.Fremoved = Fremoved;
interfacedata.evalMap = evalMap;
interfacedata.evalVariables = evalVariables;
interfacedata.evalVariables = evalVariables;
interfacedata.evaluation_scheme = [];

% *************************************************************************
%% GENERAL DATA EXCANGE TO RECOVER SOLUTION AND UPDATE YALMIP VARIABLES
% *************************************************************************
recoverdata.H = H;
recoverdata.x_equ = x_equ;
recoverdata.used_variables = used_variables;

%%
function yesno = warningon

s = warning;
if isa(s,'char')
    yesno = isequal(s,'on');
else
    yesno = isequal(s(1).state,'on');
end

%%
function [evalMap,evalVariables,used_variables,nonlinearvariables,linearvariables] = detectHiddenNonlinear(used_variables,options,nonlinearvariables,linearvariables,eIN)

%evalVariables = yalmip('evalVariables');
evalVariables = eIN;
old_used_variables = used_variables;
goon = 1;
if ~isempty(evalVariables)
    while goon
        % Which used_variables are representing general functions
     %   evalVariables = yalmip('evalVariables');
     evalVariables = eIN;
        usedEvalVariables = find(ismember(used_variables,evalVariables));
        evalMap =  yalmip('extstruct',used_variables(usedEvalVariables));
        if ~isa(evalMap,'cell')
            evalMap = {evalMap};
        end
        % Find all variables used in the arguments of these functions
        hidden = [];
        for i = 1:length(evalMap)
            n = length(evalMap{i}.arg{1});
            if isequal(getbase(evalMap{i}.arg{1}),[zeros(n,1) eye(n)])% & is(evalMap{i}.arg{1},'linear')
                for j = 1:length(evalMap{i}.arg)-1
                    % The last argument is the help variable z in the
                    % transformation from f(ax+b) to f(z),z==ax+b. We should not
                    % use this transformation if the argument already is unitary
                    hidden = [hidden getvariables(evalMap{i}.arg{j})];
                end
            else
                for j = 1:length(evalMap{i}.arg)
                    % The last argument is the help variable z in the
                    % transformation from f(ax+b) to f(z),z==ax+b. We should not
                    % use this transformation if the argument already is unitary
                    hidden = [hidden getvariables(evalMap{i}.arg{j})];
                end
            end
        end
        used_variables = union(used_variables,hidden);

        % The problem is that linear terms might be missing in problems with only
        % nonlinear expressions
        [monomtable,variabletype] = yalmip('monomtable');
        if (options.relax==1)|(options.relax==3)
            monomtable = [];
            nonlinearvariables = [];
            linearvariables = used_variables;
        else
            nonlinearvariables = find(variabletype);
            linearvariables = used_variables(find(variabletype(used_variables)==0));
        end
        needednonlinear = nonlinearvariables(ismembc(nonlinearvariables,used_variables));
        linearinnonlinear = find(sum(abs(monomtable(needednonlinear,:)),1));
        missinglinear = setdiff(linearinnonlinear(:),linearvariables);
        used_variables = uniquestripped([used_variables(:);missinglinear(:)]);


        usedEvalVariables = find(ismember(used_variables,evalVariables));
        evalMap =  yalmip('extstruct',used_variables(usedEvalVariables));
        if ~isa(evalMap,'cell')
            evalMap = {evalMap};
        end
        evalVariables = usedEvalVariables;

        for i = 1:length(evalMap)
            n = length(evalMap{i}.arg{1});
            if isequal(getbase(evalMap{i}.arg{1}),[zeros(n,1) eye(n)])
                index = ismember(used_variables,getvariables(evalMap{i}.arg{1}));
                evalMap{i}.variableIndex = find(index);
            else
                index = ismember(used_variables,getvariables(evalMap{i}.arg{end}));
                evalMap{i}.variableIndex = find(index);
            end
        end
        goon = ~isequal(used_variables,old_used_variables);
        old_used_variables = used_variables;
    end
else
    evalMap = [];
end


function evalVariables = determineEvaluationBased(operators)
evalVariables = [];
for i = 1:length(operators)
    if strcmpi(operators{i}.properties.model,'callback')
        evalVariables = [evalVariables operators{i}.properties.models];
    end
end