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# semidef

semidefinite programming

### Calling Sequence

[x,Z,ul,info]=semidef(x0,Z0,F,blck_szs,c,options)

### Arguments

- 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

### Description

`[x,Z,ul,info]=semidef(x0,Z0,F,blck_szs,c,options)`

solves semidefinite program:

and its dual:

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

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
`-trace(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. Notice that 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.

`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

### Examples

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)

### References

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, http://www.ee.ucla.edu/~vandenbe/sp.html

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