# Tagged Questions

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

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### Is this the correct proximal operator?

I'm supposed implementing certain optimization algorithms (ISTA, FISTA) to minimize: $$\frac12 ||Ax-(Ax_0+z)||_2^2 + \lambda ||x||_1.$$ $A$ is a matrix, $x$ is a vector, $z$ is some noise filled with ...
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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 ...
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### Impact of convexity under different changes of variables for different parts of optimization

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 ...
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### Feasible Region of QCQP and Semidefinite Programs

I am trying to visualize the feasible region of a Quadratically Constrained Quadratic Program (QCQP) which is expected to be non convex (actually is a set of ellipses in $\mathbb{R}^2$) and the ...
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### Solve the closed form solution for argmax of $x^Ty - x^T\ln(x)$

Let $y \in \mathbb{R}^n, \ln(x) = \begin{bmatrix} \ln(x_1) \\ \vdots \\ \ln(x_n) \end{bmatrix} \in \mathbb{R}^n$ Show that $$x^* = \text{argmax}_{x \in \mathcal{D}} \quad x^Ty - x^T\ln(x)$$ ...
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### Optimization of the function of two variables

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 $g(.,y)$ ...
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### Sum of convex and decreasing function

I have a sum of decreasing function and a convex function over some domain. Can I say that the sum is also a convex function (i.e. there exists a unique minimum)?
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### Solving $I^* = \arg\min_{I'} \left( \|\phi_\ell(I) - \phi_\ell(I')\|_2^2 + R(I') \right)$ with gradient descent

I am trying to create the results from this a paper that is trying to understand the types of features a convolutional neural network is learning to recognize. I don't think understanding ...
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### How do you prove that $\{ Ax \mid x \geq 0 \}$ is closed?

Let $A$ be a real $m \times n$ matrix. How do you prove that $\{ Ax \mid x \geq 0, x \in \mathbb R^n \}$ is closed (as in, contains all its limit points)? The inequality $x \geq 0$ is ...
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### Unique critical point and psd implies pd and hence strict relative maximum

Let $f(x)$ be of class $C^{(2)}$ on an open set A, $x_0\in A\subseteq R^n$ a critical point. In addition, the hessian matrix of f(x) at $x_0$, $H(x_0)=\{f_{ij}\}|_{x=x_0}$, is negative semi-definite. ...
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Gradient descent reduces the value of the objective function in each iteration. This is repeated until convergence happens. The question is if the norm of gradient has to decrease as well in every ...
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### Benders decomposition Master Problem

I am currently working on implementation of Bender's Decomposition for MIP. I am looking at the simplest model \begin{split} \min_{x,y} &\; c^Tx + f(y)\\ s.t. & \; Ax + Dy \ge ...
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### Interpolating polynomial such that it is convex in specified region

The problem I have is that I have data at two points $x_1,x_2$ and $x_2>x_1>0$. At these two points, I know that the function $f$ has values $f(x_1)$ and $f(x_2)$ respectively. It is also ...
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### Recommend a optimization book with more coding examples?

I am interested in continuous optimization problems. However, I feel it is very difficult for me to understand the classic books such as Convex Optimization or Numerical Optmization. My problem with ...
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### Optimization of a function of two variables.

Suppose we have to minimize a function $f(\mathbf x,\mathbf y)$ where $\mathbf x$, $\mathbf y$ are vectors in Euclidean space. The function is convex in $\mathbf y$ when $\mathbf x$ is kept constant ...
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### Using gradient descent and Newton's method combined

I have this function $f(\mathrm{X})$ where $\mathrm{X=A+B+C}$ where $\mathrm{A}$ is a diagonal element with variable $a$ on its diagonal. $\mathrm{B}$ is another diagonal matrix with variable $b$ on ...
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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 ...