[37] | 1 | function solution = savesdpafile(varargin) |
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| 2 | %SAVESDPAFILE Saves a problem definition in the SDPA format |
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| 3 | % |
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| 4 | % SAVESDPAFILE(F,h,'filename') Saves the problem min(h(x)), F(x)>0 to the file filename |
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| 5 | % SAVESDPAFILE(F,h) A "Save As" - box will be opened |
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| 6 | |
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| 7 | % Author Johan Löfberg |
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| 8 | % $Id: savesdpafile.m,v 1.5 2005/09/27 10:21:32 joloef Exp $ |
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| 9 | |
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| 10 | F = varargin{1}; |
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| 11 | h = varargin{2}; |
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| 12 | nvars = yalmip('nvars'); |
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| 13 | |
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| 14 | % Expand nonlinear operators |
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| 15 | [F,failure,cause] = expandmodel(F,h,sdpsettings); |
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| 16 | if failure % Convexity propgation failed |
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| 17 | interfacedata = []; |
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| 18 | recoverdata = []; |
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| 19 | solver = ''; |
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| 20 | diagnostic.solvertime = 0; |
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| 21 | diagnostic.problem = 14; |
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| 22 | diagnostic.info = yalmiperror(14,cause); |
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| 23 | return |
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| 24 | end |
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| 25 | |
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| 26 | % Get the SP format |
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| 27 | [F_struc,K] = lmi2sedumistruct(F,[]); |
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| 28 | |
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| 29 | % Convert the objective |
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| 30 | if isempty(h) |
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| 31 | c=zeros(nvars,1); |
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| 32 | else |
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| 33 | [n,m]=size(h); |
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| 34 | if ~((n==1) & (m==1)) |
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| 35 | error('Scalar expression to minimize please.'); |
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| 36 | else |
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| 37 | lmi_variables = getvariables(h); |
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| 38 | c = zeros(nvars,1); |
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| 39 | for i=1:length(lmi_variables) |
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| 40 | c(lmi_variables(i))=getbasematrix(h,lmi_variables(i)); |
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| 41 | end; |
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| 42 | end |
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| 43 | end |
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| 44 | |
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| 45 | % Which sdpvar variables are actually in the problem |
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| 46 | used_variables_LMI = find(any(F_struc(:,2:end),1)); |
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| 47 | used_variables_obj = find(any(c',1)); |
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| 48 | used_variables = uniquestripped([used_variables_LMI used_variables_obj]); |
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| 49 | |
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| 50 | % Check for unbounded variables |
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| 51 | unbounded_variables = setdiff(used_variables_obj,used_variables_LMI); |
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| 52 | if ~isempty(unbounded_variables) |
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| 53 | % Remove unbounded variable from problem |
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| 54 | used_variables = setdiff(used_variables,unbounded_variables); |
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| 55 | end |
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| 56 | |
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| 57 | % Pick out the necessary rows |
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| 58 | if length(used_variables)<nvars |
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| 59 | c = c(used_variables); |
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| 60 | F_struc = sparse(F_struc(:,[1 1+[used_variables]])); |
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| 61 | end |
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| 62 | |
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| 63 | if K.f>0 |
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| 64 | % Extract the inequalities |
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| 65 | A_equ = F_struc(1:K.f,2:end); |
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| 66 | b_equ = -F_struc(1:K.f,1); |
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| 67 | [Q,R] = qr(A_equ'); |
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| 68 | n = max(find(any(R'))); |
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| 69 | Q1 = Q(:,1:n); |
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| 70 | Q2 = Q(:,n+1:end); |
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| 71 | R = R(1:n,:); |
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| 72 | x_equ = Q1*(R'\b_equ); |
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| 73 | % Exit if no consistent solution exist |
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| 74 | if (norm(A_equ*x_equ-b_equ)>sqrt(eps)) |
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| 75 | error('Linear constraints inconsistent.'); |
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| 76 | return |
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| 77 | end |
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| 78 | % So we dont need these rows anymore |
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| 79 | F_struc = F_struc(K.f+1:end,:); |
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| 80 | K.f = 0; |
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| 81 | % OK, we found a new basis |
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| 82 | H = Q2; |
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| 83 | % objective in new basis |
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| 84 | c = H'*c; |
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| 85 | % LMI in new basis |
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| 86 | F_struc = [F_struc*[1;x_equ] F_struc(:,2:end)*H]; |
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| 87 | end |
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| 88 | |
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| 89 | % Is a filename supplied |
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| 90 | if nargin<3 |
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| 91 | [filename, pathname] = uiputfile('*.dat-s', 'Save SDPA sparse format file'); |
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| 92 | if isa(filename,'double') |
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| 93 | return % User canceled |
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| 94 | else |
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| 95 | % Did the user change the extension |
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| 96 | if isempty(findstr(filename,'.')) |
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| 97 | filename = [pathname filename '.dat-s']; |
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| 98 | else |
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| 99 | filename = [pathname filename]; |
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| 100 | end |
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| 101 | end |
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| 102 | else |
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| 103 | filename = varargin{3}; |
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| 104 | end |
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| 105 | |
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| 106 | % Save to file |
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| 107 | createsdplibfile(F_struc, K, c, filename); |
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