minus.m

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

function y = minus(X,Y)
%MINUS (overloaded)

% Author Johan L鰂berg
% $Id: minus.m,v 1.25 2007/03/23 09:00:56 joloef Exp $

X_is_spdvar = isa(X,'sdpvar');
Y_is_spdvar = isa(Y,'sdpvar');

% Convert block objects
if ~X_is_spdvar
    if isa(X,'blkvar')
        X = sdpvar(X);
        X_is_spdvar = isa(X,'sdpvar');
    elseif isa(X,'intval')
        X_is_spdvar = 0;
        Y.basis = intval(Y.basis);
    end
end

if ~Y_is_spdvar
    if isa(Y,'blkvar')
        Y = sdpvar(Y);
        Y_is_spdvar = isa(Y,'sdpvar');;
    elseif isa(Y,'intval')
        Y_is_spdvar = 0;
        X.basis = intval(X.basis);
    end
end

switch 2*X_is_spdvar+Y_is_spdvar
    case 1
        if isempty(X)
            try
                y = full(X - reshape(Y.basis(:,1),Y.dim(1),Y.dim(2)));
            catch
                error(lasterr);
            end
            return
        end

        y = Y;
        n_Y = Y.dim(1);
        m_Y = Y.dim(2);
        [n_X,m_X] = size(X);
        x_isscalar = (n_X*m_X==1);
        y_isscalar = (n_Y*m_Y==1);
        any_scalar = x_isscalar | y_isscalar;

        % Speeeeeeed
        if x_isscalar & y_isscalar
            y.basis = -y.basis;
            y.basis(1) = y.basis(1)+X;
            % Reset info about conic terms
            y.conicinfo = [0 0];
            return
        end

        if any_scalar | ([n_Y m_Y]==[n_X m_X])
            if y_isscalar
                y.basis = repmat(y.basis,n_X*m_X,1);
                y.dim(1) = n_X;
                y.dim(2) = m_X;
            end
            y.basis = -y.basis;
            if nnz(X)~=0
                y.basis(:,1) = y.basis(:,1)+X(:);
            end
        else
            error('Matrix dimensions must agree.');
        end
        % Reset info about conic terms
        y.conicinfo = [0 0];

    case 2

        if isempty(Y)
            try
                y = full(reshape(X.basis(:,1),X.dim(1),X.dim(2))-Y);
            catch
                error(lasterr);
            end
            return
        end
        y = X;
        n_X = X.dim(1);
        m_X = X.dim(2);
        [n_Y,m_Y] = size(Y);
        x_isscalar = (n_X*m_X==1);
        y_isscalar = (n_Y*m_Y==1);
        any_scalar = x_isscalar | y_isscalar;

        % Silly hack
        % Taking X-scalar(0) takes unnecessary time
        % and is used in most definitions of LMIs
        if (y_isscalar & (Y==0))
            return
        end

        % Speeeeeeed
        if x_isscalar & y_isscalar
            y.basis(1) = y.basis(1)-Y;
            % Reset info about conic terms
            y.conicinfo = [0 0];
            return
        end

        if any_scalar | ([n_Y m_Y]==[n_X m_X])
            if x_isscalar
                y.basis = repmat(y.basis,n_Y*m_Y,1);
                y.dim(1) = n_Y;
                y.dim(2) = m_Y;
            end
            y.basis(:,1) = y.basis(:,1)-Y(:);
        else
            error('Matrix dimensions must agree.');
        end

        % Update information about conic terms
        % This information is used in DUALIZE to
        % speed up some checks, and to facilitate some
        % advanced dualization features. It also
        % speeds up checking for symmetry in some other code
        % Ugly, but the best way at the moment
        % For a description of this field, check SDPVAR code
        % if (y.conicinfo(1)~=0) & isequal(Y,Y') & (y.conicinfo(2) ~= 2)
        %     y.conicinfo(2) = max(1,y.conicinfo(2));
        % else
        y.conicinfo = [0 0];
        % end


    case 3

        %	if (X.typeflag~=0) | (Y.typeflag~=0)
        %		error('Relational objects cannot be manipulated')
        %	end

        n_X = X.dim(1);
        m_X = X.dim(2);
        n_Y = Y.dim(1);
        m_Y = Y.dim(2);
        x_isscalar = (n_X*m_X==1);
        y_isscalar = (n_Y*m_Y==1);
        any_scalar = x_isscalar | y_isscalar;

        if ~any_scalar
            if (~((n_X==n_Y) & (m_X==m_Y)))
                error('Matrix dimensions must agree.')
            end
        end

        all_lmi_variables = uniquestripped([X.lmi_variables Y.lmi_variables]);
        y = X;
        X.basis = []; % Returns memory?
        y.lmi_variables = all_lmi_variables;

        in_X_logical = ismembc(all_lmi_variables,X.lmi_variables);
        in_Y_logical = ismembc(all_lmi_variables,Y.lmi_variables);
        in_X = find(in_X_logical);
        in_Y = find(in_Y_logical);

        if isequal(X.lmi_variables,Y.lmi_variables) & n_Y==n_X & m_Y==m_X
            y.basis = y.basis - Y.basis;
            % Super special case f(scalar)-f(scalar)
            if length(X.lmi_variables)==1
                if all(y.basis(:,2)==0)
                    y = full(reshape(y.basis(:,1),n_Y,m_Y));
                else
                    y.conicinfo = [0 0];
                end
                return
            end
        elseif max(X.lmi_variables) < min(Y.lmi_variables) &  n_Y==n_X & m_Y==m_X
            % Disjoint variables in X - Y
            y.basis = [y.basis(:,1) - Y.basis(:,1) y.basis(:,2:end) -Y.basis(:,2:end)];
        elseif max(Y.lmi_variables) < min(X.lmi_variables) &  n_Y==n_X & m_Y==m_X
            % Disjoint variables in X - Y
            y.basis = [y.basis(:,1) - Y.basis(:,1) -Y.basis(:,2:end) y.basis(:,2:end)];
        else
            [ix,jx,sx] = find(y.basis);y.basis = [];
            [iy,jy,sy] = find(Y.basis);Y.basis = [];
            mapX = [1 1+in_X];
            mapY = [1 1+in_Y];
            basis_X = sparse(ix,mapX(jx),sx,n_X*m_X,1+length(all_lmi_variables));ix=[];jx=[];sx=[];
            basis_Y = sparse(iy,mapY(jy),sy,n_Y*m_Y,1+length(all_lmi_variables));iy=[];jy=[];sy=[];

            % Fix addition of matrix+scalar
            if n_X*m_X<n_Y*m_Y
                y.dim(1) = n_Y;
                y.dim(2) = m_Y;
                basis_X = repmat(basis_X,n_Y*m_Y,1);
            end
            if n_Y*m_Y<n_X*m_X
                y.dim(1) = n_X;
                y.dim(2) = m_X;
                basis_Y = repmat(basis_Y,n_X*m_X,1);
            end

            % OK, solution is...
            y.basis = basis_X - basis_Y;
        end

        y.conicinfo = [0 0];
        if nnz(in_Y_logical & in_X_logical)>0
            y = clean(y);
        end

    otherwise
end

% Update info on KYP objects
if X_is_spdvar & Y_is_spdvar & X.typeflag==9  & Y.typeflag==9
    error('Substraction of KYP objects currently not supported')
end
if Y_is_spdvar & Y.typeflag==9
    y.extra.M = -Y.extra.M+X;
    y.extra.negated = ~Y.extra.negated;
    return
end
if X_is_spdvar & X.typeflag==9
    y.extra.M = y.extra.M-Y;
    return
end