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See the recommended documentation of this function
Solve semidefinite problems.
x=semidef(x0,Z0,F,blocksizes,c,options) [x,Z]=semidef(...) [x,Z,ul]=semidef(...) [x,Z,ul,info]=semidef(...)
m-by-1 real column vector (must be strictly primal feasible, see below)
L-by-1 real vector (compressed form of a strictly feasible dual matrix, see below)
L-by-(m+1) real matrix
p-by-2 integer matrix (sizes of the blocks) defining the dimensions of the (square) diagonal blocks
m-by-1 real vector
a 1-by-5 matrix of doubles
a 1-by-2 matrix of doubles.
semidef solves the semidefinite program:
and its dual:
exploiting block structure in the matrices
Tr is the trace operator, i.e. the sum of the
diagonal entries of the matrix.
The problem data are the vector
block-diagonal symmetric matrices
Moreover, we assume that the matrices
L diagonal blocks.
Fi's matrices are stored columnwise in
F in compressed format: if
i=0,..,m, j=1,...,L denote the jth (symmetric) diagonal block of
where, for each symmetric block
M, the vector
the compressed representation of
M, that is
obtained by scanning rowwise the upper triangular part of
For example, the matrix
is stored as
[1; 2; 3; 4; 5; 6; 7; 8; 9; 10]
In order to create the matrix
F, the user can combine the
pack function, as described in the example below.
blocksizes gives the size of block
Z is a block diagonal matrix with L blocks
Z^j has size
.Every block is stored using packed storage of
the lower triangular part.
The 1-by-2 matrix of doubles
ul contains the primal objective
c'*x and the dual objective value
The entries of
options are respectively:
nu: a real parameter which controls the rate of convergence.
abstol: absolute tolerance. The absolute tolerance cannot be lower than 1.0e-8, that is, the absolute tolerance used in the algorithm is the maximum of the user-defined tolerance and the constant tolerance 1.0e-8.
reltol: relative tolerance (has a special meaning when negative).
tv: the target value, only referenced if
reltol < 0.
maxiters: the maximum number of iterations, a positive integer value.
On output, the
info variable contains the status of the execution.
info=1if maxiters exceeded,
info=2if absolute accuracy is reached,
info=3if relative accuracy is reached,
info=4if target value is reached,
info=5if target value is not achievable;
negative values indicate errors.
Convergence criterion is based on the following conditions that is, the algorithm stops if one of the following conditions is true:
maxiters is exceeded
duality gap is less than abstol
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)
reltol is negative and primal objective is less than tv or dual objective is greater than
// 1. Define the initial guess x0=[0;0]; // // 2. Create a compressed representation of F // Define 3 symmetric block-diagonal matrices: F0, F1, F2 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] // Define the size of the two blocks: // the first block has size 2, // the second block has size 2. blocksizes=[2,2]; // Extract the two blocks of the matrices. 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); // Create 3 column vectors, containing nonzero entries // in F0, F1, F2. F0nnz = list2vec(list(F01,F02)); F1nnz = list2vec(list(F11,F12)); F2nnz = list2vec(list(F21,F22)); // Create a 16-by-3 matrix, representing the // nonzero entries of the 3 matrices F0, F1, F2. FF=[F0nnz,F1nnz,F2nnz] // Compress FF CFF = pack(FF,blocksizes); // // 3. Create a compressed representation of Z // Create the matrix Z0 Z0=2*F0; // Extract the two blocks of the matrix Z01=Z0(1:2,1:2); Z02=Z0(3:4,3:4); // Create 2 column vectors, containing nonzero entries // in Z0. ZZ0 = [Z01(:);Z02(:)]; // Compress ZZO CZZ0 = pack(ZZ0,blocksizes); // // 4. Create the linear vector c c=[trace(F1*Z0);trace(F2*Z0)]; // // 5. Define the algorithm options options=[10,1.d-10,1.d-10,0,50]; // 6. Solve the problem [x,CZ,ul,info]=semidef(x0,CZZ0,CFF,blocksizes,c,options) // // 7. Check the output // Unpack CZ Z=unpack(CZ,blocksizes); w=vec2list(Z,[blocksizes;blocksizes]); Z=sysdiag(w(1),w(2)) c'*x+trace(F0*Z) // Check that the eigenvalues of the matrix are positive spec(F0+F1*x(1)+F2*x(2)) trace(F1*Z)-c(1) trace(F2*Z)-c(2)
This function is based on L. Vandenberghe and S. Boyd sp.c program.
L. Vandenberghe and S. Boyd, " Semidefinite Programming," Informations Systems Laboratory, Stanford University, 1994.
Ju. E. Nesterov and M. J. Todd, "Self-Scaled Cones and Interior-Point Methods in Nonlinear Programming," Working Paper, CORE, Catholic University of Louvain, Louvain-la-Neuve, Belgium, April 1994.
SP: Software for Semidefinite Programming, User's Guide, Beta Version, November 1994, L. Vandenberghe and S. Boyd, 1994 http://www.ee.ucla.edu/~vandenbe/sp.html
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