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

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Normalize gradient

I want to minimize a function $f \, : \, \mathbb{R}^{N} \, \longrightarrow \, \mathbb{R}$ (with $N \in \mathbb{N}^{\ast}$. In my problem, $N = 315$). I know that $f$ is differentiable on ...
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Complexity analysis of convex optimization problem

I am studying an optimization problem \begin{equation} \mathbf{x}^*=\text{argmax}\quad\sum_{d=1}^{D}\log(\mathbf{a}_d^T\mathbf{x}+b)+\mathbf{c}_d^T\mathbf{x}+f_d\\ \text{subject to}\quad ...
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Strong duality: When does the optimal primal variable coincide with the primal variable giving the dual function.

I'm considering the inequality-constrained optimization problem of finding $$ x^{\star} = \arg \min_{x} f(x) \;\; \text{s.t.} \;\; h(x) \le 0 $$ which is assumed to have a unique minimizer. The ...
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The importance of the full-row-rank assumption for the simplex method

Consider a linear programming model in the usual form ready for applying the simplex method. I understand that having the constraint equations' coefficient matrix $A$ be of full row rank means not ...
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1answer
292 views

Lagrange dual of a sum of convex functions

Given a set of convex functions $f_i(x)$ and convex sets $X_i$ in $\mathbb R^n$ I need to find the Lagrange dual problem for the problem $\min \sum{f_i(x)} , x \in X_i \forall i$. There is of course ...
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637 views

Solving optimization problems using derivatives and critical points

I have a homework question which I have completed 2/3 of; however I am stuck on the last part of the question. The question is: A drug used to treat cancer is effective at low doses with an ...
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48 views

Is the optimizer of a strongly-convex cost function bounded?

Let f(x) be strongly-convex. Can its minimizer be unbounded? I suspect not. Can we obtain a bound on it in relation to the strong-convexity constant? I believe an equivalent formulation of this ...
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1answer
89 views

Rewrite constrained optimization objective

I wanted to ask, under which conditions can one rewrite the optimization objective $\min_x f(x)\;\;\;s.t.\;\;\;g(x) \leq s$ as $\min_x g(x)\;\;\;s.t.\;\;\;f(x) \leq t$ I have particular interest ...
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Does convexity of a function guarantee tractability of finding its minimum?

Formulating a problem as a convex optimization problem usually implicitly considered to imply being able to find global minimizer of the objective. My question is that if it is true or not. ...
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1answer
189 views

minimization problem on differential equations - optimal control

I am trying to minimize an time-integral of a linear function with respect to differential equations. The problem is formally defined as follows: Given $\lambda< \mu_1, \mu_2$ fixed ...
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On the duality gap for quasiconvex optimisation problems

This stack exchange question got me thinking about quasiconvex analysis. Given a compact,convex subset $X\subset \mathbb{R}^n$ and a quasiconvex function $f:X\rightarrow \mathbb{R}$ Define the ...
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How to show that a function is piecewise linear

Let z(t) = min $(c+t d)^T x$ s.t $Ax <= b$ Show that Z(t) is a concave, piecewise linear function of t. I'm really not sure how to even start proving this, I would really ...
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443 views

sufficient condition for KKT problems

For the Karush-Kuhn Tucker optimsation problem, Wikipedia notes that: "The necessary conditions are sufficient for optimality if the objective function f and the inequality constraints g_j are ...
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347 views

Global optimum of sum of convex functions

Take two real differentiable convex functions, $f_1$ and $f_2$, defined on the unit interval $[0; 1]$. I want to find the global optimum of: $\min_{x \in [0;1]} af_1(x)+bf_2(x)$, for given $a, b \in ...
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Trace of a quadratic function, Convexity

Is trace($XX^T$) a convex function of matrix $X$? A linear function of a quadratic should be convex, but I could not prove by definition.
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201 views

How to calculate the maximal ellipsoid in a given polyhedron

I am faced with the problem of finding the ellipsoid $B$ ($B$ is a symmetric positive definite matrix) of maximal volume within a convex set $C$ given as a set of linear inequalities $C=\{x| a_i^T x ...
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997 views

Convex minimization over the Unit Simplex

I have a simple (few variables), continuous, twice differentiable convex function that I wish to minimize over the unit simplex. In other words, $\min. f(\mathbf{x})$, $\text{s.t. } \mathbf{0} \preceq ...
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Convexity of a trace of matrices with respect to diagonal elements

Can we prove that $\mbox{trace}({\bf A} ({\bf P}+{\bf Q})^{-1} {\bf A}^T)$ is a jointly convex function of positive variables $[q_1,q_i,...,q_N]$, where ${\bf Q}=\mbox{diag}(q_1,...,q_N)$, ...
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Is this set convex ?2

Is this set convex for every arbitrary $\alpha\in \mathbb R$? $$\Big\{(x_1,x_2)\in \mathbb R^2_{++} \,\Big|\, x_1x_2\geq \alpha\Big\}$$ Where $\mathbb R^2_{++}=[0,+\infty)\times [0,+\infty)$.
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Derivation of soft thresholding operator

I was going through the derivation of soft threholding at http://dl.dropboxusercontent.com/u/22893361/papers/Soft%20Threshold%20Proof.pdf. It says the three unique solutions for $\operatorname{arg ...
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What is the dual of this optimization problem?

Consider the points $x_1, \ldots, x_N \in \mathbb{R}^n$, and a (locally bounded, convex) function $f: \mathbb{R}^n \rightarrow \mathbb{R}$. I am looking for the dual of the following optimization ...
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195 views

Is this necessarily true for a convex function?

$$\sum_{s \in V} \lambda_sf(s) \geq f(\sum_{s \in V} s\lambda_s)$$ $V$ is a set of points and corresponding to each point in $V$, there is a $\lambda_s$ which is a scalar so $\sum_{s \in V} ...
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Every concave function that is nonnegative on its domain is log-concave?

This is a statment from Wiki. I'm not sure why this is true: If: $f(\theta x+(1-\theta y) \geq \theta f( x) + (1-\theta)f(y)$ And $f(\cdot) \geq 0$ then: $$f(\theta x+(1-\theta y) \geq f( ...
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Is this optimization problem solvable?

I have the following optimization problem: $$ \min_\mathbf{x}\|\mathbf{a+Bx}\|^2 ~~\text{s.t}~~ \|\mathbf{y+Ax}\|_\infty \leq \beta\|\mathbf{y}\|_\infty ~~,~~ \|\mathbf{x}\|^2 \leq \alpha^2$$ where ...
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2answers
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Is this a polyhedron?

Is $S$ a polyhedron? $$S=\{x\in\mathbb{R}^n|\|x-x_0\|\le\|x-x_1\|\}$$ where $x_0, x_1$ are given. $S$ is the set of points that are closer to $x_0$ than to $x_1$. I was thinking the ...
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138 views

Verifying the convexity of some function

Convex function: We will say that $f:X\rightarrow R$ is convex function if for every $\lambda\in [0,1]$ and for every $x,y\in X$ ($X$ is convex space) $f(\lambda x+(1-\lambda)y)\leq\lambda ...
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324 views

Is the following problem convex?

I think the following problem is convex (due to the results of some simulations), but I am not sure: $min_x||e^{(Ax)}-b||^2_2$ s.t. x>0 where $A$ is m x n, $x$ is n x 1, and b is m x 1. $A,x,b$ are ...
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1answer
338 views

Simple resource for Lagrangian constrained optimization?

Just had an optimization lecture. I understand unconstrained methods like Newton and Gradient descent just fine, as well as the ideas that give rise to them. I don't really understand the ideas that ...
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356 views

Maximizing the number of non-crossing lines between a number of points

Suppose I have a number of points in 2-dimensional space. I want to draw as many lines between the points as possible such that no two lines cross. Hoping for a polynomial time algorithm, I ...
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Proof of Convexity

Is the function $Trace(AX^TBX)$ a convex function in $X$ or not ? Here, $X$ is a rectangular matrix and $A,B$ are square, symmetric, p.s.d matrices. The entries in $X,A,B$ are real valued.
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Convert any convex optimization problem to a linear objective

Wikipedia claims that: Any convex optimization problem can be transformed into minimizing (or maximizing) a linear function over a convex set by converting to the epigraph form. Is there a ...
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1answer
57 views

Consider $(f\Box g) (z)=\inf_{x+y=z}(f(x)+f(y))$

Consider $$(f\Box g) (z)=\inf_{x+y=z}(f(x)+g(y))$$ Find two convex functions $f$ and $g$ from $X$ to $\mathbb{R}$ such that the infimum given above is never a minimum. Can anyone help with finding ...
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1answer
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Is the expectation of log-concave function still log-concave?

I know the expectation preserves the concavity (or convexity), but I was wondering is it still true that the expectation of log-concave function still log-concave; to be more precise, Let ...
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1answer
69 views

linear approximation with respect to L1 norm

I am trying to solve this problem: Find the best $L^1$ linear approximation of $e^x$ on [0,1] i.e. minimize $\int_0^1|e^x-\alpha-\beta x| dx$ any hints how to proceed
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Proving convexity of a function whose Hessian is positive semidefinite over a convex set

C is a convex set in R^n and f:R^n --> R is twice continuously differentiable over C. The Hessian of f is positive semidefinite over C, and I want to show that f is therefore a convex function. I ...
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Origin of Slater's condition

I've been looking all over the internet to answer this question: Slater's condition is a commonly used to certify that strong duality holds in a convex optimization problem. Although used in many ...
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554 views

Compressive sensing with non square matrices

I'm implementing the algorithm in the following paper: "Compressive sensing for wideband cognitive radios" http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04218361 However I've run into a ...
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How can I solve Lagrange multiplier equation with multi constraints?

This site is really awesome. :) I hope that we can share our ideas through this site! I have an equation as below, $$ min \ \ w^HRw \ \ subject \ \ to \ \ w^HR_aw=J_a, \ w^HR_bw=J_b$$ If there is ...
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1answer
435 views

Sion's minimax theorem

Sion's minimax theorem is stated as: Let $X$ be a compact convex subset of a linear topological space and $Y$ a convex subset of a linear topological space. Let $f$ be a real-valued function on ...
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240 views

straightforward way to determine if this set is convex?

straightforward way to determine if this set is convex? $Z=\left\{x\in\mathbb{R}^2:3x_1^4-x_1x_2+x_2^4\le x_2,x_1>2,x_2>2\right\}$ I know I can try by manipulation of linear combination of two ...
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2answers
1k views

LP relaxation for ILP\IP (integer linear programming)

I am familiar with LP relaxation for ILP (or IP). Assume we concern with integer minimization problem, which we formalize using ILP; we then relax the ILP into LP and we say that the LP provides a ...
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92 views

Rewriting a matrix as a polynomial

If a finite dimensional matrix $A$ is positive semidefinite, how can we write $A$ as a polynomial in $A^{2}$? Thanks.
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995 views

time complexity of a convex quadratically constrained quadratic program (QCQP) problem

Could someone tell me the time complexity of a convex quadratically constrained quadratic program (QCQP) problem? And any references? Thank you very much.
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Convexity of a non linear optimization problem

I have a non linear optimization problem, namely: $$\min {\sqrt{(x-u)^2 + (y-v)^2 + (z-w)^2)}}$$ How can i show that the above function is convex. Doing via Hessian is a difficult task.
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Are posynomial functions convex?

I know that you can transform a posynomial function into an exponential function, which is convex. Does this imply that all posynomial functions are convex?
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58 views

Convexity of variance

How can I prove the following function is convex? Is variance concave or convex? $$\psi (x)=\left(x_1-\frac{x_1+x_2+x_3}{3}\right)^2+ \left(x_2-\frac{x_1+x_2+x_3}{3}\right)^2+ ...
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1answer
70 views

How to replace piecewise objective function in convex optimization problem?

Suppose I have a minimization problem \begin{equation} \begin{aligned} & \min\limits_{x} & & g(x)+f(x) \end{aligned} \end{equation} \begin{equation} f(x)= \begin{cases} 1, ...
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1answer
120 views

Why the gradient of $\log{\det{X}}$ is $X^{-1}$, and where did trace tr() go??

I'm studying Boyd's "Convex Optimization" and encountered a problem in page 642. According to the definition, the derivative $Df(x)$ has the form: $$f(x)+Df(x)(z-x)$$ And when $f$ is ...
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206 views

Proof of convergence for the proximal point algorithm

I'm trying to come up with a super simple proof of convergence on the proximal point algorithm, which uses the iteration scheme $x^{i+1} = \mathbf{prox}_{tf}(x^i)$ where $f$ is a closed, convex ...
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172 views

Why a convex cone cannot have more than one extreme point?

The way I define an extreme point is : A point which cannot be defined as a convex combination of two distinct points. I'm not able to extend this and show why a convex cone cannot have more than ...