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semidef — semidefinite programming
[x,Z,ul,info]=semidef(x0,Z0,F,blck_szs,c,options)
x0 | : m x 1 real column vector (must be strictly primal feasible, see below) |
Z0 | : L x 1 real vector (compressed form of a strictly feasible dual matrix, see below) |
F | : L x (m+1) real matrix |
blck_szs | : p x 2 integer matrix (sizes of the blocks) defining the dimensions of the (square) diagonal blocks size(Fi(j)=blck_szs(j) j=1,...,m+1. |
c | : m x 1 real vector |
options | : row vector with five entries [nu,abstol,reltol,0,maxiters] |
ul | : row vector with two entries |
[x,Z,ul,info]=semidef(x0,Z0,F,blck_szs,c,options) solves semidefinite program:
minimize c'*x subject to F_0 + x_1*F_1 + ... + x_m*F_m >= 0 and its dual maximize -Tr F_0 Z subject to Tr F_i Z = c_i, i=1,...,m Z >= 0
exploiting block structure in the matrices F_i.
It interfaces L. Vandenberghe and S. Boyd sp.c program.
The Fi's matrices are stored columnwise in F in compressed format: if F_i^j, i=0,..,m, j=1,...,L denote the jth (symmetric) diagonal block of F_i, then
[ pack(F_0^1) pack(F_1^1) ... pack(F_m^1) ] [ pack(F_0^2) pack(F_1^2) ... pack(F_m^2) ] F= [ ... ... ... ] [ pack(F_0^L) pack(F_1^L) ... pack(F_m^L) ]
where pack(M), for symmetric M, is the vector [M(1,1);M(1,2);...;M(1,n);M(2,2);M(2,3);...;M(2,n);...;M(n,n)] (obtained by scanning columnwise the lower triangular part of M).
blck_szs gives the size of block j, ie, size(F_i^j)=blck_szs(j).
Z is a block diagonal matrix with L blocks Z^0, ..., Z^{L-1}. Z^j has size blck_szs[j] times blck_szs[j]. Every block is stored using packed storage of the lower triangular part.
The 2 vector ul contains the primal objective value c'*x and the dual objective value -Tr F_0*Z.
The entries of options are respectively: nu = a real parameter which ntrols the rate of convergence. abstol = absolute tolerance. reltol = relative tolerance (has a special meaning when negative). tv target value, only referenced if reltol < 0. iters = on entry: maximum number of iterations >= 0, on exit: the number of iterations taken.
info returns 1 if maxiters exceeded, 2 if absolute accuracy is reached, 3 if relative accuracy is reached, 4 if target value is reached, 5 if target value is not achievable; negative values indicate errors.
Convergence criterion:
(1) maxiters is exceeded (2) duality gap is less than abstol (3) primal and dual objective are both positive and duality gap is less than (reltol * dual objective) or primal and dual objective are both negative and duality gap is less than (reltol * minus the primal objective) (4) reltol is negative and primal objective is less than tv or dual objective is greater than tv
F0=[2,1,0,0; 1,2,0,0; 0,0,3,1 0,0,1,3]; F1=[1,2,0,0; 2,1,0,0; 0,0,1,3; 0,0,3,1] F2=[2,2,0,0; 2,2,0,0; 0,0,3,4; 0,0,4,4]; blck_szs=[2,2]; F01=F0(1:2,1:2);F02=F0(3:4,3:4); F11=F1(1:2,1:2);F12=F1(3:4,3:4); F21=F2(1:2,1:2);F22=F2(3:4,3:4); x0=[0;0] Z0=2*F0; Z01=Z0(1:2,1:2);Z02=Z0(3:4,3:4); FF=[[F01(:);F02(:)],[F11(:);F12(:)],[F21(:);F22(:)]] ZZ0=[[Z01(:);Z02(:)]]; c=[trace(F1*Z0);trace(F2*Z0)]; options=[10,1.d-10,1.d-10,0,50]; [x,Z,ul,info]=semidef(x0,pack(ZZ0),pack(FF),blck_szs,c,options) w=vec2list(unpack(Z,blck_szs),[blck_szs;blck_szs]);Z=sysdiag(w(1),w(2)) c'*x+trace(F0*Z) spec(F0+F1*x(1)+F2*x(2)) trace(F1*Z)-c(1) trace(F2*Z)-c(2)
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