[37] | 1 | function y = minus(X,Y) |
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| 2 | %MINUS (overloaded) |
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| 3 | |
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| 4 | % Author Johan Löfberg |
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| 5 | % $Id: minus.m,v 1.21 2006/07/26 20:17:58 joloef Exp $ |
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| 6 | |
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| 7 | X_is_spdvar = isa(X,'sdpvar'); |
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| 8 | Y_is_spdvar = isa(Y,'sdpvar'); |
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| 9 | |
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| 10 | % Convert block objects |
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| 11 | if ~X_is_spdvar |
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| 12 | if isa(X,'blkvar') |
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| 13 | X = sdpvar(X); |
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| 14 | X_is_spdvar = isa(X,'sdpvar'); |
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| 15 | end |
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| 16 | end |
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| 17 | |
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| 18 | if ~Y_is_spdvar |
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| 19 | if isa(Y,'blkvar') |
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| 20 | Y = sdpvar(Y); |
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| 21 | Y_is_spdvar = isa(Y,'sdpvar');; |
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| 22 | end |
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| 23 | end |
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| 24 | |
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| 25 | switch 2*X_is_spdvar+Y_is_spdvar |
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| 26 | case 1 |
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| 27 | if isempty(X) |
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| 28 | try |
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| 29 | y = full(X - reshape(Y.basis(:,1),Y.dim(1),Y.dim(2))); |
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| 30 | catch |
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| 31 | error(lasterr); |
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| 32 | end |
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| 33 | return |
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| 34 | end |
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| 35 | |
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| 36 | y = Y; |
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| 37 | n_Y = Y.dim(1); |
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| 38 | m_Y = Y.dim(2); |
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| 39 | [n_X,m_X] = size(X); |
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| 40 | x_isscalar = (n_X*m_X==1); |
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| 41 | y_isscalar = (n_Y*m_Y==1); |
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| 42 | any_scalar = x_isscalar | y_isscalar; |
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| 43 | |
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| 44 | % Speeeeeeed |
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| 45 | if x_isscalar & y_isscalar |
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| 46 | y.basis = -y.basis; |
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| 47 | y.basis(1) = y.basis(1)+X; |
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| 48 | % Reset info about conic terms |
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| 49 | y.conicinfo = [0 0]; |
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| 50 | return |
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| 51 | end |
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| 52 | |
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| 53 | if any_scalar | ([n_Y m_Y]==[n_X m_X]) |
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| 54 | if y_isscalar |
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| 55 | y.basis = repmat(y.basis,n_X*m_X,1); |
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| 56 | y.dim(1) = n_X; |
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| 57 | y.dim(2) = m_X; |
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| 58 | end |
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| 59 | y.basis = -y.basis; |
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| 60 | if nnz(X)~=0 |
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| 61 | y.basis(:,1) = y.basis(:,1)+X(:); |
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| 62 | end |
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| 63 | else |
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| 64 | error('Matrix dimensions must agree.'); |
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| 65 | end |
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| 66 | % Reset info about conic terms |
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| 67 | y.conicinfo = [0 0]; |
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| 68 | |
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| 69 | case 2 |
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| 70 | |
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| 71 | if isempty(Y) |
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| 72 | try |
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| 73 | y = full(reshape(X.basis(:,1),X.dim(1),X.dim(2))-Y); |
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| 74 | catch |
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| 75 | error(lasterr); |
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| 76 | end |
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| 77 | return |
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| 78 | end |
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| 79 | y = X; |
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| 80 | n_X = X.dim(1); |
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| 81 | m_X = X.dim(2); |
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| 82 | [n_Y,m_Y] = size(Y); |
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| 83 | x_isscalar = (n_X*m_X==1); |
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| 84 | y_isscalar = (n_Y*m_Y==1); |
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| 85 | any_scalar = x_isscalar | y_isscalar; |
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| 86 | |
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| 87 | % Silly hack |
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| 88 | % Taking X-scalar(0) takes unnecessary time |
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| 89 | % and is used in most definitions of LMIs |
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| 90 | if (y_isscalar & (Y==0)) |
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| 91 | return |
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| 92 | end |
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| 93 | |
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| 94 | % Speeeeeeed |
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| 95 | if x_isscalar & y_isscalar |
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| 96 | y.basis(1) = y.basis(1)-Y; |
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| 97 | % Reset info about conic terms |
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| 98 | y.conicinfo = [0 0]; |
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| 99 | return |
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| 100 | end |
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| 101 | |
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| 102 | if any_scalar | ([n_Y m_Y]==[n_X m_X]) |
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| 103 | if x_isscalar |
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| 104 | y.basis = repmat(y.basis,n_Y*m_Y,1); |
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| 105 | y.dim(1) = n_Y; |
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| 106 | y.dim(2) = m_Y; |
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| 107 | end |
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| 108 | y.basis(:,1) = y.basis(:,1)-Y(:); |
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| 109 | else |
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| 110 | error('Matrix dimensions must agree.'); |
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| 111 | end |
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| 112 | |
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| 113 | % Update information about conic terms |
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| 114 | % This information is used in DUALIZE to |
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| 115 | % speed up some checks, and to facilitate some |
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| 116 | % advanced dualization features. It also |
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| 117 | % speeds up checking for symmetry in some other code |
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| 118 | % Ugly, but the best way at the moment |
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| 119 | % For a description of this field, check SDPVAR code |
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| 120 | % if (y.conicinfo(1)~=0) & isequal(Y,Y') & (y.conicinfo(2) ~= 2) |
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| 121 | % y.conicinfo(2) = max(1,y.conicinfo(2)); |
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| 122 | % else |
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| 123 | y.conicinfo = [0 0]; |
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| 124 | % end |
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| 125 | |
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| 126 | |
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| 127 | case 3 |
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| 128 | |
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| 129 | % if (X.typeflag~=0) | (Y.typeflag~=0) |
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| 130 | % error('Relational objects cannot be manipulated') |
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| 131 | % end |
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| 132 | |
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| 133 | n_X = X.dim(1); |
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| 134 | m_X = X.dim(2); |
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| 135 | n_Y = Y.dim(1); |
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| 136 | m_Y = Y.dim(2); |
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| 137 | x_isscalar = (n_X*m_X==1); |
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| 138 | y_isscalar = (n_Y*m_Y==1); |
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| 139 | any_scalar = x_isscalar | y_isscalar; |
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| 140 | |
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| 141 | if ~any_scalar |
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| 142 | if (~((n_X==n_Y) & (m_X==m_Y))) |
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| 143 | error('Matrix dimensions must agree.') |
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| 144 | end |
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| 145 | end |
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| 146 | |
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| 147 | all_lmi_variables = uniquestripped([X.lmi_variables Y.lmi_variables]); |
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| 148 | y = X; |
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| 149 | X.basis = []; % Returns memory? |
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| 150 | y.lmi_variables = all_lmi_variables; |
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| 151 | |
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| 152 | in_X_logical = ismembc(all_lmi_variables,X.lmi_variables); |
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| 153 | in_Y_logical = ismembc(all_lmi_variables,Y.lmi_variables); |
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| 154 | in_X = find(in_X_logical); |
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| 155 | in_Y = find(in_Y_logical); |
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| 156 | |
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| 157 | if isequal(X.lmi_variables,Y.lmi_variables) & n_Y==n_X & m_Y==m_X |
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| 158 | y.basis = y.basis - Y.basis; |
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| 159 | % Super special case f(scalar)-f(scalar) |
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| 160 | if length(X.lmi_variables)==1 |
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| 161 | if all(y.basis(:,2)==0) |
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| 162 | y = full(y.basis(1)); |
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| 163 | else |
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| 164 | y.conicinfo = [0 0]; |
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| 165 | end |
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| 166 | return |
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| 167 | end |
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| 168 | else |
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| 169 | if 1 |
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| 170 | [ix,jx,sx] = find(y.basis);y.basis = []; |
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| 171 | [iy,jy,sy] = find(Y.basis);Y.basis = []; |
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| 172 | mapX = [1 1+in_X]; |
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| 173 | mapY = [1 1+in_Y]; |
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| 174 | basis_X = sparse(ix,mapX(jx),sx,n_X*m_X,1+length(all_lmi_variables));ix=[];jx=[];sx=[]; |
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| 175 | basis_Y = sparse(iy,mapY(jy),sy,n_Y*m_Y,1+length(all_lmi_variables));iy=[];jy=[];sy=[]; |
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| 176 | else |
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| 177 | % MATLAB sparse fails on this for huge problems at a certain size |
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| 178 | basis_X = spalloc(n_X*m_X,1+length(all_lmi_variables),nnz(X.basis)); |
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| 179 | basis_Y = spalloc(n_Y*m_Y,1+length(all_lmi_variables),nnz(Y.basis)); |
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| 180 | basis_X(:,[1 1+in_X])=y.basis;y.basis = []; |
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| 181 | basis_Y(:,[1 1+in_Y])=Y.basis;Y.basis = []; |
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| 182 | end |
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| 183 | |
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| 184 | % Fix addition of matrix+scalar |
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| 185 | if n_X*m_X<n_Y*m_Y |
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| 186 | y.dim(1) = n_Y; |
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| 187 | y.dim(2) = m_Y; |
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| 188 | basis_X = repmat(basis_X,n_Y*m_Y,1); |
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| 189 | end |
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| 190 | if n_Y*m_Y<n_X*m_X |
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| 191 | y.dim(1) = n_X; |
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| 192 | y.dim(2) = m_X; |
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| 193 | basis_Y = repmat(basis_Y,n_X*m_X,1); |
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| 194 | end |
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| 195 | |
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| 196 | % OK, solution is... |
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| 197 | y.basis = basis_X - basis_Y; |
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| 198 | end |
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| 199 | |
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| 200 | % Only clean if there are variables used in both |
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| 201 | %if ~all(xor(in_X_logical,in_Y_logical)) |
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| 202 | % Reset info about conic terms |
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| 203 | |
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| 204 | % if (y.conicinfo(1)~=0) & ishermitian(Y) & isempty(intersect(X.lmi_variables,Y.lmi_variables)) |
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| 205 | % y.conicinfo = [y.conicinfo(2); |
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| 206 | % else |
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| 207 | y.conicinfo = [0 0]; |
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| 208 | % end |
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| 209 | |
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| 210 | |
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| 211 | y = clean(y); |
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| 212 | %else |
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| 213 | %end |
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| 214 | |
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| 215 | otherwise |
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| 216 | end |
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| 217 | |
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| 218 | % Update info on KYP objects |
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| 219 | if X_is_spdvar & Y_is_spdvar & X.typeflag==9 & Y.typeflag==9 |
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| 220 | error('Substraction of KYP objects currently not supported') |
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| 221 | end |
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| 222 | if Y_is_spdvar & Y.typeflag==9 |
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| 223 | y.extra.M = -Y.extra.M+X; |
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| 224 | y.extra.negated = ~Y.extra.negated; |
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| 225 | return |
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| 226 | end |
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| 227 | if X_is_spdvar & X.typeflag==9 |
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| 228 | y.extra.M = y.extra.M-Y; |
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| 229 | return |
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| 230 | end |
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| 231 | |
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| 232 | |
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| 233 | |
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