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Optimization with convex constraints and convex objectives; notions related to convex optimization such as sub-gradients, normal cones, separating hyperplanes

13 votes
0 answers
696 views

Minimizing total variation under constraint

For $p\in[0,1]$, we write $\mathrm{Ber}(p)$ to denote the Bernoulli measure on $\{0,1\}$; that is, $\mathrm{Ber}(p)(0)=1-p$, $\mathrm{Ber}(p)(1)=p$. For $n\in\mathbb{N}$ and $p=(p_1,\ldots,p_n)\in[0,1 …
5 votes
0 answers
252 views

Generalized convexity

Let $X$ be a vector space. The positive-homogeneous function $\|\cdot\|$ is said to be a quasinorm if $\|x+y\|\le K(\|x\|+\|y\|)$, for some $K\ge1$; it is a norm if $K=1$. Question: 1. (terminology) i …
2 votes
0 answers
125 views

Variant of Sion's minimax theorem

Sion's minimax theorem assumes that $f:X\times Y\to\mathbb{R}$ is being minimized w.r.t. $x$ and maximized w.r.t. $y$, where at least one of $X,Y$ is compact (additional (quasi)convexity and semi-cont …