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

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Optimization of a function over probability distributions

I'm trying to solve certain optimization problems dealing with probability distributions. Consider the space of probability distributions $\{ 1, ..., N\} \to [0, 1]$ I have a function $f : (\{ 1, ...
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How to write lagrangian terms related to only one variable in a semidefinite constraint?

I have a semidefinite problem as follows(which is nonconvex) \begin{alignat}{3} &\min_{x_{un}} \min_{t,H,w} &&t+f( w)\cr &\text{s.t. } &&\begin{bmatrix} K\odot H ...
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Finite Subdifferential

Can the set of subgradient vectors (Subdifferential) have finite number of vectors? I know it can be either empty, have one vector, or have infinite number of vectors
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L1 regularized unconstrained optimization problem

I am encountering an unconstrained minimization problem. The problem is of the form $$\min_x \frac{\|x-a\|_2^2}{2}+\lambda\|x\|_1$$ where $x,a \in R^n$ and $x$ is the optimization variable. ...
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Variational inequality and saddle point problem

I refer to the following paper: (fix the wrong link) https://papers.nips.cc/paper/5723-adaptive-primal-dual-splitting-methods-for-statistical-learning-and-image-processing.pdf In that paper, we ...
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Minima of non-strictly convex function [on hold]

I wonder if there is anything that can be said about minimizers of convex, but not strictly convex problems, with regards to initial points provided to an optimization algorithm? Not sure if this is ...
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1answer
629 views

Dual cone of a L1 norm cone?

I am listening to convex optimization lectures and I hear that dual cone of a $L1$ norm cone is a $L-\infty$ norm cone. Can anybody please explain how? I understand that every point in the dual cone ...
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Is there an efficient way to evaluate the proximal operator of $f(x) = \|x\|_2 + I_{\geq 0}(x)$?

Is there an efficient way to evaluate the proximal operator of the function $f:\mathbb R^n \to \mathbb R \cup \{ \infty \}$ defined by \begin{equation} f(x) = \| x \|_2 + I_{\geq 0}(x), \end{equation} ...
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References on probability theory, stochastic processes, Monte Carlo and convex optimisation, with similar writing style to Terence Tao

I learned a lot from prof Tao's notes and books because unlike many authors, he seems to prefer writing more words, explanations and intuitions rather than just mathematical formulae. His approach is ...
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806 views

Maximization of sum of two functions

Is there any relationship similar to the following. Let $X$ be the maximum of functions $f_1(x)+f_2(x)$. Let $X_1$ be a maximum of $f_1(x)$ and let $X_2$ be a maximum of $f_2(x)$. Is there any ...
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Why this problem is unbounded, even though it's dual is feasible?

In this paper, An Efficient Inexact ABCD Method for Least Squares Semidefinite Programming,page 2, there is a problem called, (P): \begin{align} P: &\min_{X,s} && ‎ \frac{1}{2} ‎\Vert ...
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$\max\{c^Tx:Ax\le b,x\ge 0\}=+\infty$ iif it exists $j\in\{1,…,n\}$ such that $\max \{x_j:Ax\le b, x\ge 0\}=+\infty$

Show a vector $\vec c$ exists such that $\max\{c^Tx:Ax\le b,x\ge 0\}=+\infty$ if and only if it exists $j\in\{1,...,n\}$ such that $\max \{x_j:Ax\le b, x\ge 0\}=+\infty$ I'm only asking for a ...
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9 views

Convert this problem with 2-norm cost to an SOCP?

I am solving a non-convex problem via sequential convex optimization. Here is a minimal example of my problem at iteration $i$: $$ \begin{align*} \min_{\Delta t,F_i}&\left( \Delta ...
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61 views

Maximum concyclic points

Given n points, find an algorithm to get a circle having maximum points.
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1answer
26 views

On Boyd et al.'s convergence analysis of ADMM: Why do we need the convexity assumption?

Please refer to Boyd et al.'s convergence analysis of ADMM (Chapter 3 and Appendix A). My question is: Why do we need $f$ and $g$ to be convex? I don't see the need of this assumption. If the ...
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2answers
587 views

Convex Optimization of quadratic function with inequality constraints

How would I solve the following problem? $$\min_{x\in\mathbb{R}^n} x^T A x$$ subject to the constraints $$x_i\geq 1,\,i=1,\dots,n,$$ where A is positive semidefinite and symmetric. Is it possible to ...
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Why is the Barycenter operation in Hadamard spaces Lipschitz continuous?

I am looking into exercise 9.2.22 of "A course in metric geometry" by Burago-Burago-Ivanov. For a Hadamard space $H$ (a complete simply connected metric space of nonpositive curvature in the ...
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1answer
31 views

Converting generic linear problems into their dual

I'm revising how to do dual problems in linear algebra. I'm very weak in Linear programing but I struggle to cope with the topic during lectures and assignements. I have to convert the following ...
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+100

Optimization of approximate functions using varying objective function

Let $g(\theta;x)$ and $f(\theta;x)$ be two convex functions such that $g$ asymptotically approximates $f$: $g(\theta;x)\approx f(\theta;x)$, specifically: $$ |g(\theta;x)-f(\theta;x)| \leq ...
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Positivity of the last component of non negative least squares based on active set method

I have followed the instructions given in Lawson and Hanson book for non-negative least squares using active set method. I am having a trouble in justifying one of the statements they have made about ...
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53 views

Solving a linear program thanks to complementary slackness theorem

Using the complementary slackness theorem, say if the following basis optimal: $$x_1*=0=x_5*,x_2*=4/3,x_3*=2/3,x_4*=5/3$$ \begin{cases} \max & 7x_1 ...
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Can a linear program be optimal if its basis is infeasible?

I want to know thanks to the dual theorem wether the following basis is or isn't optimal. That is to say looking for the slack variables. As far as the third line doesn't respect the constraints: ...
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Which coefficient to start with in the dictionary method?

I used to start with the variable with the biggest coefficient in the goal function (in the case of max). yet I read an article that behaving like this may lead to loop. It is rather preferred to do ...
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Subgradients of non-convex functions

In these notes (section 2.3), it is stated that: A point $x^*$ is a minimizer of a function $f$ (not necessarily convex) if and only if $f$ is subdifferentiable at $x^*$ and $0 \in\partial ...
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How to deal with an $xy\le 1$ constraint?

I have to solve the following optimization problem: $$ \begin{align*} \min_{x,y} &\{-x-y\} \\ \text{such that} \\ y &\ge 3 \\ y &\le 30 \\ x &\ge 0 \\ xy &\le 1 \\ \end{align*} $$ ...
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1answer
20 views

Convexify $x\le a+by^2$

I have the following non-convex constraint: $$ x\le a+by^2\quad\text{where}\quad a,b>0,\,y\in[0,y_{max}]\text{ and }a\approx by_{max}^2 $$ On a drawing, it looks something like this: The above ...
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18 views

Coordinate descent with equality and inequality constraints

I have an intuitive understanding of why the simple method of coordinate descent does not work with linearly coupled constraints such as; $$\min_x\sum_if_i(x_i)$$ $$s.t.$$ $$Ax=b$$ If we try to ...
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8 views

How to compute norm bound error in robust approximation

I am reading convex optimization, and I am little confused about the following two prolems in norm bound error of robust approximation. How to compute $\{\|\bar{A}X-b+Ux\| | \|U\|\le a\}$ ? For the ...
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reference for convex function results

Here is a simple property of a concave function from $\mathbb{R}$ to $\mathbb{R}$, Given $x,x'\in \mathbb{R}$ with $x'>x$, if $\exists \kappa'\in (0,1)$ such that ...
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Sequential convex second order cone programming [closed]

I am trying to solve an optimization problem where the objective function is convex, the inequalities are second order cones but I also have nonlinear equality constraints. The convex energy can be ...
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Weights in goal programming

I'm not quite convinced about assigning weights in goal programming. Here is an example formulation problem. What I tried: Let $x_j$ be the number of minutes for ad $j = R, T$ We want to ...
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Please help… is this a convex function?

Kindly help me. What can we say about the function $f$ shown in below? is it convex or non-convex over the variables $x_1, x_2,.., x_{n+1}, y_1,y_2$? \begin{align} f(x_1, x_2,.., x_{n+1}, ...
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$\max 2x_1 +x_2$ unbounded or unfeasible with the constraint $sx_1 +tx_2\le-1$

\begin{cases} \max & 2x_1 &{}+x_2\\ & sx_1 &{}+tx_2&\le-1\\ & x_1,x_2&&\ge 0 \end{cases} Find out when this program is not feasible, bounded Feasibility It ...
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Use of binary variables in LP problems

I can't figure out how to write the following condition to an LP. I have four nonnegative variables: $X_A$, $X_B$, $X_C$, and $X_D$. The condition which should be satisfied is this: If $X_A$ and ...
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When does a variable goes out with the revised Simplex method?

Let be the following linear program. \begin{cases} \max & 3x_1& +x_2\\ &x_1&-x_2 &\le -1\\ &-x_1 &-x_2&\le -3\\ &2x_1 &+x_2 &\le4\\ x_1,x_2\ge 0 ...
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min-max problem

Hello to everybody: I'm trying to prove that : Let $A$ be the incidence matrix of a clutter (simple hypergraph) $C$. Prove that the vertex covering number and the matching number of $C$ satisfy: ...
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Impact of removing active constraints in convex optimization

In active set methods for non negative least squares, we remove variables from the passive set to active set if the least squares solution gives negative values on those variables. What's the impact ...
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1answer
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Chebyshev's approximation understanding

I am reading Boyd's book on convex optimization. Could you assisst me in understanding what this expression means: $$\text{minimize} \ \ \text{max}_{i=1,...,k}|a_i^Tx-b_i|$$ This is what I think ...
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Some intuition on the support function of a convex set

I have some doubts on the interpretation and properties of the support function of a convex subset of $\mathbb{R}^d$. (1)Let $K$ be a convex set in $\mathbb{R}^d$. (2) The support function of $K$ is ...
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Residual norm of active set method in non negative least squares

I am having trouble with understanding one of the statements in active set methods done for non negative least squares given in Book written by Lawson. The quadratic problem can be written as ...
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2answers
71 views

Can you prove that this function is convex? $\sqrt{2x_1^2+3x_2^2+x_3^2+4x_1x_2+7} + (x_1^2+x_2^2+x_3^2+1)^2$.

My analysis: The second term can be proven to be convex as follows. It is basically a composition of norm with an affine transformation to the power of four: $(x_1^2+x_2^2+x_3^2+1)^2 = \|(x_1^2, ...
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Is the minimum point of a strictly convex function stable?

This is a problem I figured out after seeing the definition of minimum stable point and I think the following tense is true: Let $f(x)$ be a strictly convex function whose minimum values is ...
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Pseudo-Boolean functions restricted to integers

The Pseudo-Boolean functions are of the following form. $$ f : \mathbb{B}^n \to \mathbb{R} $$ I would like to know if there is a special sub-category of $$ f : \mathbb{B}^n \to \mathbb{Z} $$ with ...
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Variant of conjugate function: $V(s) = \underset{x}\max \{\langle s,x-x_0\rangle-\beta f(x)\}$

Consider one variant of conjugate function: $$V(s) = \underset{x}\max \{\langle s,x-x_0\rangle-\beta f(x)\}$$ You can think $s$ as a linear functional. If I do the following steps: ...
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How to form a dual problem in convex optimization (in a broad view)

After reading some papers, this problem confuses me. There are different forms of dual problem to the primal problem: $$\underset{x}\min \ \ f(x)$$ where $f(x)$ is a convex function. By ...
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535 views

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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1answer
30 views

characteristic cone of polyhedral

Let $$Q=\{x ∶ Ax ≤ b \}≠∅$$ If $Q = P + C$, where $P$ is a polytope and $C$ is a polyhedral cone, prove that $$\{y|Ay ≤ 0\} = \{y|x + y ∈ Q, ∀ x ∈ Q\}$$ The cone $C = \{y|Ay ≤ 0\}$ is ...
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$\big\langle\nabla f(x)-\nabla f(y),\,(x-y)\big\rangle\ge m\,\left\| x-y\right\|^2,\;m>0\;$ for strictly and strong convex function

Prove that $\,\big\langle\nabla f(x) - \nabla f(y),\, (x-y)\big\rangle \geq m\,\left\lVert x-y\right\rVert^2, \;\,m > 0\,$ for strong convex function $f: \mathbb R^n \to\mathbb R$. Is it true ...
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What aspects of convex optimization are used in artificial intelligence, if any?

I work on convex optimization with Stephen Boyd's book. As an example, support vector machines are mentioned as an application of separating hyperplanes theorem. I am wondering if there is any other ...
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Is there a way to determine if the Convex Hull of two polyhedra is going to be huge?

So in this post: Faster Algorithms for Convex Hulls I was interested in determining if a convex hull of two $n$ dimensional polyhedra can be computed quickly, and the answer was in general: no, ...