# Tagged Questions

Convex Optimization is a special case of mathematical optimization. It includes Linear Programming and least-squares.

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### Optimization of relative entropy

Wondering if my following question is an application of information theory: Lets say we have a factory and ship boxes of stuff outside. If a competitor stands outside my factory, observes the stream ...
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### If this problem is not unbounded, what's wrong in this dual derivation?

In a paper with 100 citation, Robust Support Vector Machine Training via Convex outlier Ablation, a convex relaxation is used. In this paper, a form of robust svm proposed: \begin{align} \min_{0\leq \...
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### Is minmax equivalent to maxmin?

More precisely, problem $1$ is as follows: \begin{eqnarray} &\max_{1{\le}i{\le}N}\min_{[\gamma_m^i]}\left[\lambda_i - \sum_{m=1}^M\phi_m\gamma_m^iF_m^i\right] \\ &\mbox{subject to} \sum_{i=1}^...
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### Is $K =\{ S: \exists \text{ positive diagonal} D, D^TSD \;\text{diagonally dominant}\}$ convex?

I am doing some convex cone optimization and wonder whether the following set $K_1$ is convex or not. Assume the following matrices are all in $\mathbb{R}^{n\times n}$ and symmetric. Let the set of ...
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### Theoretically understanding the algorithmic solution to this Quadratically Constrained Quadratic Optimization Problem

I have a simple QCQP problem to solve: $\min_{t} x(t)^{T}Ax(t)$ subject to constraints $x(t)^{T}Ax(t) > 1$ where A is a positive definite matrix and $x(t) \in \mathbb{R}^2$ is some time ...
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### Duality between $Ax<b$ and another system, but Gordan's just misses

I am trying to show that $$Ax < b$$ Is feasible iff $$A^T y =0 , b^Ty + s = 0, (y,s) \ge 0, (y,s) \ne 0$$ Is infeasible. Work So Far Now when I try to hit this with Gordan's Lemma I seem ...
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### Is it possible to use regularization to minimize the (expected) number of non-zero digits in a number?

This question may be slightly related to this question on length of the representation of a number in a certain basis. Introduction / Background In image and video coding, particularly the ...
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### Can a subgradient always be found in polynomial time?

Given a convex function, under what conditions can we find a subgradient in polynomial time? There are easy examples such as $f$ being an supremum of a finite number of differentiable functions, but ...
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### Example 2.13 Conditional Probabilities, Convex Optimization by Stephen Boyd

I am reading Stephen Boyd's Convex Optimization. I can't understand the example below: what is the "convex set of joint probabilities" in this example? what is the convex set $C$ here?
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### How to deal with a convex constraint

I want to deal with a convex constraint \begin{align} F(P)=P^{H}AP_{0}+P_{0}^{H}AP-P_{0}^{H}AP_{0}\succeq 0 \end{align} where $(\cdot)^{H}$ represents Hermitian transpose, $A$ is a positive definite ...
This SDP problem with the variable $X \in \mbox{S}^n$ where $\rho \gt 0$ $$\max \mbox{Tr}(AX) - \rho \mbox{1}\lvert X \rvert \mbox{1} \\ \mathrm{subject\; to}\;\mbox{Tr}(X)=1, \\ X \succeq 0$$ ...
I think that given any two vertices, say $x$ and $y$, of a convex polyhedron, if the line segment between $x$ and $y$ is the intersection of the polyhedron with a half-space, then $xy$ forms an edge. ...