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

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Monotonic optimal value function

Are there any theorems/sufficient conditions about when the optimal value function of a parametrized optimization problem is monotonic in the parameter? Specifically, are there simple conditions ...
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Non convex objective in SVM

In the formulation of svm.. The line underline says the norm of the vector w is a non convex constraint.. But how is this so.. Isn't norm a convex function.. Also aren't the other objectives ...
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2answers
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Finding the optimal value in an optimization problem

Given the optimization problem $$\text{minimize}\ f_0(x_1,x_2)$$ $$\text{subject to}\ 2x_1+x_2 \ge 1$$ $$x_1+3x_2 \ge 1$$ $$x_1 \ge 0, x_2 \ge 0$$ Let the objective function be $f_0(x_1,x_2) = x_1^2 ...
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Efficiency of quasiconvex optimization

Summary: Could we minimize quasiconvex objectives in polynomial time? Whenever an objective function of an optimization problem can be formed as a convex function, this is considered as victory. ...
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convex optimization with multiple nonsmooth terms

Is there a general algorithm for solving $$ \min f(x) + g(x) + h(x) $$ where all three functions are convex and proximable, $f(x)$ is smooth, and $g(x)$ and $h(x)$ are both nonsmooth? Note that if ...
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1answer
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Proving f cannot be convex

The following question I encountered in a convex optimization course and I can't seem to understand the solution.
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1answer
16 views

Proximal operator to Huber function

I want to solve the following problem: $$ \arg\min_x |x|_\mu + \frac{1}{2\sigma} |x-x^k|^2 $$ , where $$|x|_\mu = \begin{cases} \frac{|x|^2}{2\mu}, & |x|<\mu \\ |x|-\frac \mu 2 & |x|\geq ...
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Effects of degenerate basic feasible solutions in the simplex algorithm? [on hold]

Let $P$ =$\{x\in \mathbb{R}^n :Ax=b,x\geq 0\}$,where $A$ is a $d×n$ matrix of rank $d$. Suppose that all basic feasible solutions are nondegenerate. Let $x \in P$ have exactly $d$ positive entries. ...
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1answer
12 views

Hessian of function regarding convexity

Consider the function $f(x,y) = xy$ for $x,y>0$. Isn't $f$ a convex function? I computed the Hessian to be a matrix with only off diagonal entries equal to one and others zero. For any vector $z$ ...
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3answers
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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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Nonlinear Optimization

I have a nonlinear optimization problem, but constraints are ODE. Cost function is $J= x1+x1*x2+x1^2$ while constraints are, $\underline{x_i} < x < \bar{x_i}$ (for i=1,2,3) ; ...
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Convex optimization of a fractional objective function involving matrix determinants

I am interested in convex representation of the following fractional optimization problem. I have also described my approach in the following. However, as I am new to convex optimization, I am not ...
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26 views

SVD with Sparisty Penalties

I am trying to solve a problem like $$\min_{u,v} \Vert X-uv^T \Vert_F^2 + \beta \Vert v \Vert_1 \mbox{ s.t. } \Vert u \Vert_2 \le 1$$ where $X \in \mathbb{R}^{N\times N}$, $u, v \in ...
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Removing variables from convex linear program

I am solving linear program (possibly non-convex). Then we know that dual is always convex. Then I noticed that depending on objective functional I can sometimes remove particular variables from this ...
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Extension of co-coercivity in strongly convex functions

I am studying strongly convex functions and they mention if $f(x)$ is strongly convex with Lipschitz gradients $L$, which means $\parallel \nabla f(y) - \nabla f(x)\parallel \leq L\parallel x - y ...
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1answer
10 views

Weighted sum does not necessarily conserve convexity

Does anyone know a counterexample to show that a weighted sum of convex sets is not necessarily convex, unless our coefficients are positive? A weighted sum for me is defined as: $$\alpha C_1 + \beta ...
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Analytical or “simple” enough solution for the following probelm

Define: $$ B(y) = \rho \sum_{i=1}^n \left [ -\log(1-y_i) - \log(1+y_i) - y_i a_i \right ] $$ Where $\rho, a$ are parameters. I wish to solve the following optimization problem quickly, and I ...
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1answer
28 views

Inequality with three unknowns

Consider: \begin{equation} \Big(e^x-1\Big)\mathbb{1}_{(x\geq0)} \leq \lambda_1+\lambda_2e^x + \lambda_3x^2 \end{equation} where $\lambda_1$, $\lambda_2$, $\lambda_3$ are three unknown constants. By ...
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Does Slater's condition for both primal and dual imply compactness of dual solution set?

Consider a convex optimization problem (P) and its dual problem (D). If the solution set for (P) is compact and Slater's condition holds for both (P) and (D). Is the solution set for (D) compact? My ...
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30 views

Understanding ADMM: how is it applied to this particular problem?

While reading Boyd's paper on ADMM I encountered an issue. Consider the following problem: Problem. Minimize $f(u) + g(v)$ subject to $Au + Bv = c$, where $f$ and $g$ are closed, proper, convex and ...
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1answer
24 views

Continuity of Parameterized Optimal Solution

Suppose for every $y$, $f(x,y)$ is strictly convex in $x$. Further, $f(x,y)$ is continuous in $y$. Let $\mathcal X$ be compact (in my problem, $\mathcal X$ is an interval). Can anyone suggest any ...
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1answer
21 views

Will a minimizer for a sum of functions also minimize the product of these functions? [closed]

If it's not always true, when is it true, when is it false?
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1answer
35 views

Prove Jensen Inequality holds for a function

Given function $$f:\mathbb{R}^n_{+} \rightarrow \mathbb{R}, \ f(x) = \sum_{k=1}^K c_k x_1^{a_{1k}} x_2^{a_{2k}} \cdots x_n^{a_{nk}}$$ Show that for any $x, y \in \text{dom} \ f, \theta \in [0,1]$, ...
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1answer
19 views

What is the maximum of the following function?

Let $f(x,y) = \frac{xy^\alpha}{x+y},\alpha\in(0,\infty)$. How to compute $$\sup_{(x,y)\in[a,b]\times [0,c]}\frac{xy^\alpha}{x+y},$$ with $b>a>0$?
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1answer
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Convex function (vector composition rule)

I'm looking at the Boyd & Vandenberghe slides on Convex Optimization. In slide 18, it applies the rules of vector composition on an example to say that it is convex. The example given is ...
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1answer
16 views

Does optimal solution from primal problem follow from optimal solution to dual?

In a linear programming context, does the primal optimal solution yield an explicit way to find the primal dual solution? I vaguely remember something like this from an optimization class but can't ...
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1answer
43 views

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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Find the dual problem to a quadratic program

Consider the quadratic program: minimize $x_1^2 + 2x_2^2 - x_1x_2 - x_1$ subject to $x_1 + 2x_2 \leq u_1, x_1 - 4x_2 \leq u_2, 5x_1 + 5x_2 \leq 1$ Could anyone explain to me how to find the dual ...
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1answer
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How can L1-sparse representation be formulated as linear programming?

Problem Statement Show how the $L_1$-sparse reconstruction problem: $$\min_{x}{\left\lVert x\right\rVert}_1 \quad \text{subject to} \; y=Ax$$ can be reduced to a linear programming problem of form ...
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How to prove that unnormalized neg entropy is strongly convex with respect to 1-norm?

the unnormalized negative entropy of $\mathbf{x} \in \mathbb{R}^n_+$ is $$ g(\mathbf{x}) = \sum_i (x_i \log(x_i) - x_i) $$ it is stated that $g(\mathbf{x})$ is strongly convex with respect to 1-norm, ...
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Explain the dual problem to D-optimal design problem

Given the following D-optimal design problem $$ \text{minimize } \log \det (\sum_{i=1}^p x_i v_i v_i^T)^{-1}\\ \text{subject to } x \geq 0, {\bf{1}}^T x = 1 $$ Find the dual problem. I don't ...
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1answer
21 views

Is my proof correct? Convex optimization

There's a theorem that says that if $C \subset \mathbb R^n$ is a convex set, then $x^* \in C$ is the closest point in $C$ to $y \notin C$ if and only if $(y-x^*)\cdot(x-x^*)\leq 0$ for all $x \in C$. ...
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29 views

What non-convex functions be written as the $\min$ of multiple convex functions?

I am working on an optimization framework that can be used to optimize objective functions that can be written as the $\min$ of several convex functions. I was thinking about the generality of this ...
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Minimize $\|\mathbf{x-y}\|^2 $ subject to $x \in $ set $S=\{\mathbf{x} \in \mathbb{R}^n \;\;\;\mid \;\;\; \|\mathbf{x-x_c}\|^2\leq r^2 \}$

We are given the set $S=\{\mathbf{x} \in \mathbb{R}^n \;\;\;\mid \;\;\; \|\mathbf{x-x_c}\|^2\leq r^2 \}$ and a point $\mathbf{y} \in \mathbb{R}^n$. Our goal is to find point $\mathbf{\hat{x}}$ ...
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1answer
44 views

About a definition of “rank” of a matrix.

I am familiar with the definition of rank of a matrix as either (1) the maximal number of linearly independent rows or columns or (2) as the dimension of the image of the matrix. Another ...
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1answer
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Log transformations of function domain and inequalities

If I know that for some function $f$, the following is true for $x, y \geq 0$: $f(\log (x^a y^b)) \leq f(\log x)^a f(\log y)^b$ Can I make the claim that $f(x^a y^b) \leq f(x)^a f(y)^b$ If I ...
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1answer
45 views

Probability that max cos(φ)x + sin(φ)y according to uniform distribution = (8,5)

max $x_2$ subject to $x_1 - 2x_2 \le 0$ $2x_1 - 3x_2 \le 2$ $x_1 - x_2 \le 3$ $-x_1 + 2x_2 \le 2$ $-2x_1 + x_2 \le 0$ Optimal solution: (8, 5) --> $x_2 = 5$ Now assume that the objective is ...
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1answer
129 views

Gradient of the dual function for a nonlinear program

I'm attempting to find a proof for a property from Floudas' Nonlinear and Mixed-Integer Optimization book. Consider a nonlinear optimization problem of the form \begin{align} \min_{{\bf x}}&\quad ...
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Convex problem with linear constraints

I wish to solve the following nonlinear program: $$\min_{\substack{x_i\ge 0\\x_1\le x_1+x_3\le x_2}}h_1 x_1+h_2x_2+h_3 x_3+k_1(\tau-x_1)^++k_2(\tau-x_2)^++k_3(x_2-x_1-x_3)^+$$ I have the KKT ...
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Prove that $f(x,y) = x/(y^2+1)$ is convex

Suppose $f(x,y) = x/(y^2+1)$. I was trying to prove that this function is convex. So I took partial double-derivative and constructed the Hessian for this function. Here the Hessian is a 2 by 2 matrix ...
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1answer
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Tracking a vehicle moving with uniform velocity?

Suppose there are three cell towers at three positions $P_1$, $P_2$ and $P_3$. A vehicle is moving at uniform speed along a straight line. Three towers are pinging the vehicle at certain ...
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1answer
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How to show the Hessian matrix of such functions are positive semi-definite?

Let $f:R\to R$, $g:R^n\to R$. Thus $f\circ g:R^n\to R$. Now suppose $f$ is non-decreasing and convex while $g$ is convex. In additon, $f,g$ are of $C^2$. I want to show that their composition is ...
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why Quasiconvex function is not concave?

A quasiconvex function is a function whose all sublevel set are convex. I am curious to know whether a quasiconvex function is a concave function.
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Explicit solution for minimization over unit box with total budget constraint

I am trying to solve question 4.8, part (e) from Convex Optimization by Boyd. The problem is to find an explicit solution for the minimization problem: Minimize $\textbf{c}^T \textbf{x}$ subject to ...
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1answer
34 views

Gradient of Least Squares function

I have trouble understand the gradient of equation 3.12 with respect to $W$. Tn is a scalar output variable, $\phi(x)$ and $W$ are $N \times 1$ dimensional. According to the book, the gradient ...
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Dual program is wrong. Authors claim is right.

In a well respected book, I found the following. The authors claim that it is correct. But I think it is wrong. This is the primal Linear Problem: $$ \begin{array}{cccc} ...
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Showing λu + (1 − λ)v is an optimal solution

$$\max \quad c \cdot x \\ \mathrm{s.t.} \ Ax \leq b\\ x\geq 0 \\$$ There are two optimal solutions to the LP $u$ and $v$. How do I show that for $\lambda \in [0,1]$, $\lambda u + (1-\lambda)v$ is ...
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Do corner points optimise a linear function over a bounded convex region?

This proof says if $Z_P \ne Z_Q$, then $Z$ is maximised (or minimised, I guess) at one of the endpoints -- of what exactly? $\overline{PQ}$? So the maximum value of $Z$ occurs at either $P$ or $Q$? ...
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Do we need nonnegativity in this proof on convexity of a feasible region in an LP problem?

Is the $\color{red}{\text{non-negativity constraint (see red box)}}$ used at all in the proof? If so, where? If not, does the proof then hold for a standard LP problem without the non-negativity ...
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Moreau Decomposition with Bregman Distance

I am working with a non-Euclidean proximity operator defined by a Bregman distance function $D(\cdot, \cdot)$: $$ \operatorname{prox}_f(x) = \operatorname*{argmin}_u \{ f(u) + D(u, x) \} $$ Is ...