# Tagged Questions

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

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### Proving a Set Convex

I am trying to prove a set is convex. This is a problem from Boyd's Convex Optimization text. The set is $\{\hat{x} + tv | \alpha t^2 + \beta t + \gamma \leq 0\}$. This is actually an intersection of ...
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### Is there a way to measure how (non)convex a function is, maybe analogous to condition number?

Consider the functions $f(x) = \sin x$ and $g(x) = (x+1)^2 (x-1)^2$. We know that $f$ has an infinite number of local minimizers and is nonconvex on a non-compact subset of $R$. We know that $g$ has ...
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### Reference for elementary result in optimization

Let $U(\mathbf{z})$ be a convex, twice differentiable function, and $F(\mathbf{z},\mathbf{q})$ be convex and twice differentiable separately in $\mathbf{z}$ and $\mathbf{q}$. Consider the problem of ...
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### Variational characterization of nuclear norm

The nuclear norm $||\cdot||_{*}$ of a matrix is defined as the sum of its singular values. Working from the result at the bottom of this blog post, we have, for a matrix $\mathbf{X}$ and its ...
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### Constrained nonlinear optimization

I am wondering what is the easiest/best way to find the values of $x_i$ that maximize the expression $\sum_{i=1}^N a_i \ln (x_i)$ under the constraints $\sum_{i=1}^Nx_i = 1$ and $0\leq x_i \leq 1$ ...
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### Maximum overlap of two convex bodies

Let $\Delta_n=\big\{(x_1,x_2,\ldots,x_n): \sum_i x_i \leq 1, x_i \geq 0\big\}$ denote an n-dimensional simplex. I am trying to find an $y \in \mathbb{R}^n$ such that the $y-$shifted negative simplex ...
Given a $n$-dimensional convex polytope defined by $A x\leq b$ and $A_{eq} x = b_{eq}$, is there an efficient way to determine the average coordinates of all vertices without enumerating them? (As if ...