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2 votes
2 answers
104 views

Inequality for matrix with rows summing to 1

Let $A$ be real matrix with $M > 1$ rows and $K > 2$ columns, and each entry $a_{m,k} \in (0,1)$, with each row summing to $1$. For all $m$ $$ \sum_{k=1}^{K} a_{m,k} = 1 $$ I want to find out if ...
Kasper's user avatar
  • 23
4 votes
1 answer
119 views

Is the Loewner maximum uniquely defined?

Given 2 (symmetric) PSD matrices $A,B$, is the following set $S_{A,B}$ non-empty? $$ S_{A,B} = \{ C: C\succeq A, C\succeq B, \text{ and }\forall D, D\succeq A, D\succeq B \implies D\succeq C \} $$ If ...
user163087's user avatar
2 votes
1 answer
1k views

$\arg\max$ in the dual norm of the nuclear norm

Given a matrix $X \in \mathbb{R}^{m \times n},$ then the spectral norm is defined by $$\left \| X\right\| := \max\limits_{i \in \{1, \dots, \min\{m,n\}\} }\sigma_i (X)$$ whereas the nuclear norm is ...
Santiago Armstrong's user avatar
1 vote
0 answers
139 views

Subgradient chain rule

Suppose $$F:\mathbb{R}^n \to \mathbb{R},\; F(x)=\mathrm{max}_\mathrm{eig}(C-\mbox{diag}(x)).$$ I am trying to find a subgradient of $F$ at $x_0$. A subgradient of $\mathrm{max}_\mathrm{eig}$ is given ...
Stephen T.'s user avatar
2 votes
2 answers
276 views

Is this parametrized semidefinite program convex?

I am considering an optimization problem of the form: \begin{equation} \begin{split} f(s) &= \min_{X} \mathrm{tr}(C(s)X) \\ &\;\;\;\;\;\;\;\;\;\;\; X \ge 0, \\ &\;\;\;\;\;\;\;\;\;\;\; \...
Marc's user avatar
  • 101