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Jan 4, 2019 at 15:12 comment added Fabian Wirth This answer assumes that $A$ is symmetric. But this is not assumed in the original question. For $A$ symmetric the answer is correct, but this is not the interesting case.
Sep 29, 2018 at 16:31 comment added Ludwig If $A$ is diagonalizable and has strictly negative eigenvalues, then it is not true in general that $A+A^\top \le 0$. Take for instance $A=\begin{bmatrix}-\frac{1}{2} & 2\\ 0 & -\frac{1}{2}\end{bmatrix}$, $A+A^\top$ has eigenvalues $\{-3,+1\}$.
Sep 29, 2018 at 16:28 comment added hänsel but $U(A+A^*)U^* = UAU^*+(UAU^*)^*=UAU^* + UA^* U^* \le 0$
Sep 29, 2018 at 16:24 comment added Ludwig Your proof is correct when $A+A^\top\le 0$. However when $A+A^\top\not\le 0$, taking $X=\frac{1}{2}I$ violates the constraint $AX+XA^\top\le 0$ and so it does not represent an admissible solution.
Sep 29, 2018 at 16:21 comment added hänsel my proof is correct, so always $X=1/2$ is the solution. search the flaw in your comment.
Sep 29, 2018 at 16:11 comment added Ludwig Ok, so your claim is that the solution is invariant under unitary similarity transformations. I agree. Further, I would say that the optimum is $X=\frac{1}{2}I$ in the case $A+A^\top\le 0$ (not just $A$ diagonal). Otherwise, the explicit form of the optimum $X$ seems tricky (cf. the explicit example in my edited OP).
Sep 29, 2018 at 16:08 comment added hänsel Indeed, then optimum is $U X U^*$ as explained in my answer. $X=1/2$ for $A$ diagonal.
Sep 29, 2018 at 16:05 comment added Ludwig Then I don't think your claim is true. See the edit in my OP.
Sep 29, 2018 at 15:58 comment added hänsel yes. strangly you could even always take exactly this $X$.
Sep 29, 2018 at 15:55 comment added Ludwig Thanks for your answer. However, I think I'm missing something. Are you claiming that the optimal $X$ is always of the form $X=\frac{1}{2}I$?
Sep 29, 2018 at 15:52 history edited hänsel CC BY-SA 4.0
added 14 characters in body
Sep 29, 2018 at 15:07 history answered hänsel CC BY-SA 4.0