I am struggling to solve an optimization problem of the following form:

$$\begin{array}{ll} \underset{A}{\text{maximize}} & \log \det (A) \\ \text{subject to} & a^T A^{-1} a \le b\end{array}$$

Is there any solution for it?

  • $\begingroup$ Does $b$ have negative entries? $\endgroup$ – fedja Aug 25 '20 at 13:46
  • $\begingroup$ @fedja Isn't $b$ a scalar? $\endgroup$ – Rodrigo de Azevedo Aug 25 '20 at 13:50
  • $\begingroup$ @RodrigodeAzevedo Ah, yes. Is it negative then? $\endgroup$ – fedja Aug 25 '20 at 13:50
  • $\begingroup$ @fedja Since $A \succ 0$ (I assume), for $b < 0$ the feasible region is empty. $\endgroup$ – Rodrigo de Azevedo Aug 25 '20 at 13:52
  • $\begingroup$ @RodrigodeAzevedo And for $b>0$ you can multiply $A$ by a huge number and drive the determinant up without any bound. Actually, it is strange either way: WLOG $a=(1,0,\dots,0)$. Then you can take $A=diag(1/b,M,\dots,M)$ if $b>0$ and $diag(1/b,-M,M,\dots,M)$ if $b<0$. So, unless we are in dimension $1$, the answer is always $+\infty$. $\endgroup$ – fedja Aug 25 '20 at 13:56

I will presume you want $A$ to be constrained to be symmetric (hermitian) psd. In that case, this is a convex optimization problem which is a Linear Semidefinite Programming problem (SDP) a.k.a. Linear Matrix Inequality (LMI).

A convex optimization modeling tool, such as CVX, can formulate this as a standard Linear SDP and call a solver to solve it.

Here is the code for CVX.

variable A(n,n) hermitian semidefinite
subject to
matrix_frac(a,A) <= b
  • $\begingroup$ Thanks a lot. A is a Hermitian matrix. $\endgroup$ – user164237 Aug 25 '20 at 11:24
  • $\begingroup$ If A can be complex, you can change the variable statment to "variable A(n,n) hermitian semidefinite, as I have now incorporated in the answer. $\endgroup$ – Mark L. Stone Aug 25 '20 at 12:12
  • $\begingroup$ Thanks a lot for your help Mark. $\endgroup$ – user164237 Aug 25 '20 at 22:17
  • $\begingroup$ Sorry I am new here. How can I do it? Is it enough to click on that checkmark? $\endgroup$ – user164237 Aug 26 '20 at 11:18

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