# 6.4 Semidefinite Optimization¶

Semidefinite optimization is a generalization of conic quadratic optimization, allowing the use of matrix variables belonging to the convex cone of positive semidefinite matrices

$\PSD^r = \left\lbrace X \in \Symm^r: z^T X z \geq 0, \quad \forall z \in \real^r \right\rbrace,$

where $$\Symm^r$$ is the set of $$r \times r$$ real-valued symmetric matrices.

MOSEK can solve semidefinite optimization problems of the form

$\begin{split}\begin{array}{lccccll} \mbox{minimize} & & & \sum_{j=0}^{n-1} c_j x_j + \sum_{j=0}^{p-1} \left\langle \barC_j, \barX_j \right\rangle + c^f & & &\\ \mbox{subject to} & l_i^c & \leq & \sum_{j=0}^{n-1} a_{ij} x_j + \sum_{j=0}^{p-1} \left\langle \barA_{ij}, \barX_j \right\rangle & \leq & u_i^c, & i = 0, \ldots, m-1,\\ & l_j^x & \leq & x_j & \leq & u_j^x, & j = 0, \ldots, n-1,\\ & & & x \in \K, \barX_j \in \PSD^{r_j}, & & & j = 0, \ldots, p-1 \end{array}\end{split}$

where the problem has $$p$$ symmetric positive semidefinite variables $$\barX_j\in \PSD^{r_j}$$ of dimension $$r_j$$ with symmetric coefficient matrices $$\barC_j\in \Symm^{r_j}$$ and $$\barA_{i,j}\in \Symm^{r_j}$$. We use standard notation for the matrix inner product, i.e., for $$A,B\in \real^{m\times n}$$ we have

$\left\langle A,B \right\rangle := \sum_{i=0}^{m-1} \sum_{j=0}^{n-1} A_{ij} B_{ij}.$

## 6.4.1 Example SDO1¶

We consider the simple optimization problem with semidefinite and conic quadratic constraints:

(1)$\begin{split}\begin{array} {llcc} \mbox{minimize} & \left\langle \left[ \begin{array} {ccc} 2 & 1 & 0 \\ 1 & 2 & 1 \\ 0 & 1 & 2 \end{array} \right], \barX \right\rangle + x_0 & & \\ \mbox{subject to} & \left\langle \left[ \begin{array} {ccc} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right], \barX \right\rangle + x_0 & = & 1, \\ & \left\langle \left[ \begin{array}{ccc} 1 & 1 & 1 \\ 1 & 1 & 1 \\ 1 & 1 & 1 \end{array} \right], \barX \right\rangle + x_1 + x_2 & = & 1/2, \\ & x_0 \geq \sqrt{{x_1}^2 + {x_2}^2}, & \barX \succeq 0, & \end{array}\end{split}$

The problem description contains a 3-dimensional symmetric semidefinite variable which can be written explicitly as:

$\begin{split}\barX = \left[ \begin{array} {ccc} \barX_{00} & \barX_{10} & \barX_{20} \\ \barX_{10} & \barX_{11} & \barX_{21} \\ \barX_{20} & \barX_{21} & \barX_{22} \end{array} \right] \in \PSD^3,\end{split}$

and a conic quadratic variable $$(x_0, x_1, x_2) \in \Q^3$$. The objective is to minimize

$2(\barX_{00} + \barX_{10} + \barX_{11} + \barX_{21} + \barX_{22}) + x_0,$

subject to the two linear constraints

$\begin{split}\begin{array}{ccc} \barX_{00} + \barX_{11} + \barX_{22} + x_0 & = & 1, \\ \barX_{00} + \barX_{11} + \barX_{22} + 2(\barX_{10} + \barX_{20} + \barX_{21}) + x_1 + x_2 & = & 1/2. \end{array}\end{split}$

Listing 7 demonstrates how to solve this problem using MOSEK.

Listing 7 Code implementing problem (1). Click here to download.
function sdo1()
[r, res] = mosekopt('symbcon');

prob.c         = [1, 0, 0];

prob.bardim    = [3];
prob.barc.subj = [1, 1, 1, 1, 1];
prob.barc.subk = [1, 2, 2, 3, 3];
prob.barc.subl = [1, 1, 2, 2, 3];
prob.barc.val  = [2.0, 1.0, 2.0, 1.0, 2.0];

prob.blc = [1, 0.5];
prob.buc = [1, 0.5];

% It is a good practice to provide the correct
% dimmension of A as the last two arguments
% because it facilitates better error checking.
prob.a         = sparse([1, 2, 2], [1, 2, 3], [1, 1, 1], 2, 3);
prob.bara.subi = [1, 1, 1, 2, 2, 2, 2, 2, 2];
prob.bara.subj = [1, 1, 1, 1, 1, 1, 1, 1, 1];
prob.bara.subk = [1, 2, 3, 1, 2, 3, 2, 3, 3];
prob.bara.subl = [1, 2, 3, 1, 1, 1, 2, 2, 3];
prob.bara.val  = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0];


The solution $$x$$ is returned in res.sol.itr.xx and the numerical values of $$\barX_j$$ are returned in res.sol.barx; the lower triangular part of each $$\barX_j$$ is stacked column-by-column into an array, and each array is then concatenated forming a single array res.sol.itr.barx representing $$\barX_1, \ldots, \barX_p$$. Similarly, the dual semidefinite variables $$\barS_j$$ are recovered through res.sol.itr.bars.