# Tagged Questions

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

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### Why is this matrix symmetric?

There is an example in the Convex Optimization lecture notes, Boyd. He just said in the lecture that the matrix which is underlined in red color is symmetric! How can we claim that when there is no ...
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### how to calculate infimum of Augmented Lagrangian?

should any body explain that how do we calculate these step? \begin{align*} L(x,y) &= f(x) + y^T(Ax-b)\\ g(y) &= \inf_x \, L(x,y) \\ &= \inf_x \, f(x) - \langle -A^Ty, x \rangle - \langle ...
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### Under which conditions discretization of convex\concave function is submodular?

Say, I have $f(x)$ with $x \in [0,1]$, then by discretization I mean $f(x_h)$ with $x_h \in \{0, h, 2h, \dots, 1\}$. I know about Lovasz extension, but it works in other way: given submodular ...
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### Optimization with L_infinity norm regularization

I'm trying to solve an optimization problem of the form $$\text{minimize } \; f(x) + \|x\|_\infty$$ where $x$ ranges over all of $\mathbb{R}^n$ and $f:\mathbb{R}^n \to \mathbb{R}$ is a nice, smooth, ...
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### Optimization with box constraints - via nonlinear function

I have the following convex optimization problem over $\mathbb{R}^n$ with box constraints: \begin{align}\text{minimize }&\;f(x)\\ \text{subject to }&\;x \in [-1,1]^n\end{align} I can see ...
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### Is Frank Wolfe a descent algorithm?

A colleague was explaining to me that the Frank-Wolfe algorithm is a descent algorithm (i.e. its objective value decreases monotonically at each iteration). However, when I tried simulating it, my ...
Let $$\min_x f(x)$$ such that $$C(x) \le 0$$ where $C$, and $f$ each are convex under respective changes of variables. How does that impact the optimization? If standard algorithms are sensitive ...
I have two functions $f(x,y)$ and $g(x,y)$. I want to minimize the sum of these functions w.r.t $x,y \in (0,1)$. I know that for fixed values of $x$, $f(.,y)$ is a decreasing function while $g(.,y)$ ...