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

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How to solve the following convex optimization problem?

$$\min \|X\|_{*}+u\|Ax-b\|_2^2+v\|Cx\|_2^2$$ where $x$ is vec($X$), $u,v$. is the scalar. Is there any software to do this?
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24 views

Is convex or non convex function?$J(u,c)=\int K(x).u.(f(x)-c)^2dx$

I have a function such as $$J(u,c)=\int K(x).u.(f(x)-c)^2dx$$ where $f(x):\Omega \to R$; c is constant; $0 \le u \le 1$; and K(.) is gaussian kernel. My question is that : Is J convex or non-convex ...
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1answer
15 views

Inversion of a matrix in a system of linear inequalities

I would like to know if someone knows sufficient conditions on $A\in\mathbb{R}^{n\times n}$ and $b\in\mathbb{R}^{n}$ such that for all $x\in\mathbb{R}^{n}$: $$Ax\leq b \Rightarrow x\leq A^{-1}b \text{ ...
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8 views

I want to find a maximum of a function by Maple. How to restrict the variables to be integers?

For example, I want to find the maximum of $x^2+y^2$ with $0\le x,y\le 10$ in Maple. I can type $$maximize(x^2+y^2,x=0..10,y=0..10).$$ But if I restrict $x$ and $y$ to be both integers, then how can ...
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How to understand a proposition of subgradient

The question is from the following: Convex Optimization Algorithm (p.512)----- Prof. Bertsekas Let $f: R^n \rightarrow (-\infty, \infty]$ be a proper convex function. For every $x \in ...
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1answer
45 views

Calculating the distance function and projection

Let $\Omega=\{x\in\mathbb R^n\mid\langle a,x\rangle=b\}$. We define the distance function and projection as follows $$d(x;\Omega)=\inf\{||x-\omega||\mid\omega\in\Omega\}$$ ...
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17 views

A min-max problem and convex optimization problem.

Let $x^*$ a solution of the convex programming problem $$ \begin{array}{rl} \max & f_0(x)&\\ \mbox{s.t.} & g(x)\leq 0 \end{array} $$ where $f_0:\mathbb{R}^n\to \mathbb{R}$ and the ...
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19 views

On the solutions of a system of inequalities avoiding Helly's theorem

Let $a_1,b_1,\cdots,a_4,b_4\in\mathbb{R},r_1,\cdots,r_4\in(0,+\infty)$. Show that, if $\not\exists (x,y)\in\mathbb{R}^2$ such that $$ \begin{cases} (x-a_1)^2+(y-b_1)^2\le r_1\\ ...
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1answer
21 views

Supporting lines of closed Jordan curve

Given a simple closed Jordan (i.e. continuous) curve $\gamma:[a,b]\to\mathbb{R}^2,\ \gamma([a,b])=C_\gamma$, how can I prove that $D_\gamma$ (the set of all interior points of $\gamma$ toghether with ...
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14 views

Is this floor function/problem quasiconvex?

I am trying to study an optimisation problem under constraints. The point is that all my constraints are linear as well as all terms of my objective function except one. This guy : $$ \alpha^{\lfloor ...
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10 views

Analysis of Optimizatiointechniques: Regret Analysis vs. Direct convergence? [on hold]

When it comes to convergence rate analysis of optimization algorithms (like gradient descent and its family), there seems to be to be two main: Direct analysis, i.e. bound on $$ |f(x_t) - f(x^*)| ...
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1answer
10 views

Concave-Convex Decomposition of a continuous function

In this paper http://www.stat.ucla.edu/~yuille/pubs/optimize_papers/cccp_nips01.pdf they have a theorem that says that a twice continuous differentiable function (an energy function, they say) with ...
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1answer
27 views

Gradient in mirror descent

Mirror descent can be in general written as \begin{equation*} \nabla\Phi(x_{t+1})=\nabla\Phi(x_t)-\lambda_t\nabla f(x_t), \end{equation*} where $f$ is the objective function and $\Phi$ is a convex ...
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1answer
22 views

Dual residual for linearized ADMM

I am using linearized ADMM for a problem with a (non-smooth) convex loss function $f(x)$, and a hard constraint $x \in E$, where $E$ is an ellipsoid in $R^d$. I have encoded the hard constraint as $A ...
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2answers
44 views

Is it convex function?

I have a function and I don't know it is whether convex or non-convex: $$J(c,\alpha)=\int_\Omega ( \alpha c-I(x))^2u \, dx+ \|\alpha\|^2$$ where $0 \le u \le 1$, $I(x): \Omega \to R$, $c$ is constant ...
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1answer
49 views

Algorithms For Large-Scale $\ell_{\infty}$ Minimization

The general problem I want to solve is well studied: $$ \min_x \Vert Ax\Vert_\infty \;\;\; \mathrm{s.t.} \;\;\; Bx=c, $$ which is equivalent to the following linear program: $$ \min_{t,x} \, t ...
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1answer
35 views

MATLAB: minimize function using x value from previous iteration

I'm trying to develop an algorithm for a proximal point method defined as: $$ \underset{x \in \rm I\!R^n}{\arg\min} f(x) + \lambda g(x) $$ where f(x) is a convex and coercive function and also ...
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2answers
52 views

proving that $(\text{aff}\,C-\text{aff}\,C)\subset\text{aff}(C-C)$

In proof of Theorem 6.4.1, the author assumes that $\text{rge}\,A\subset\text{aff}\,C$ and for $\epsilon>0$ claims that $\epsilon^{-1}(C-\text{rge}\,A)\subset\text{aff}\,(C-C)$, that I can't verify ...
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1answer
28 views

any good way to approximate this non-convex function with convex function?

There is a non-convex constraint in my optimization problem, which is given by $\displaystyle -xy\log\left(1+\frac{z}{xy}\right)$. Obviously, it is neither convex or concave. Is there any good convex ...
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15 views

Help required in solving the lagrangian dual?

I'm trying to write the Lagrangian dual to the following problem \begin{align*} (P) \quad \min\;&\text{Trace}(CG)\\ \text{s.t.}\;&G \succcurlyeq 0\\ & G_{i,i}=I_d (i=1,..,M+1)\end{align*} ...
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1answer
111 views

proving that $\text{ri rge}\,A=\text{ri conv rge}\,A$

In Proposition 6.4.1 we want to prove that if $A:\mathbb R^n\rightrightarrows\mathbb R^n$ is maximal monotone, then $\text{cl rge}\,A$ and $\text{ri rge}\,A$ are convex. In proof we arrive to the ...
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How to get attribute weights from a tradeoff? [closed]

Assume that we are getting oranges and apples from a fruit basket, getting apples is seen more important than oranges, and the outcome of x=(0 apple, 25 oranges) is equally preferred as x=(10 apples,0 ...
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1answer
15 views

How can I show that any local minima of a concave function is at extremal point? [closed]

I would like to know some formal proof. I think this is intuitive but I cannot prove it for the general case... Thanks in advance...
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3answers
50 views

Prove $||\lambda x_1 + (1-\lambda) x_2 - y|| \leq ||x_1 - y||$

Assume we have have $3$ points $x_1, x_2$ and $y$ and $||x_1-y||=||x_2-y||$. How do we prove that the distance between $y$ and the convex combination of $x_1$ and $x_2$ is smaller than that between ...
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1answer
15 views

Analysis of iterative optimization methods using lyapunov analysis

In analysis of iterative methods, is it possible that we have to use two time-lagged version of the time-varying system to analyze its convergence? (that is, we construct the evolution of x^k, ...
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1answer
53 views

Is there any way to make the following function convex?

I need to find optimal lagrangian multiplier vectors for a quadratic programming problem subject to three quadratic equality constraints and several other linear inequality constraints. I would like ...
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77 views

Proving $x^4$ is strictly convex

I'm not sure how to prove $f(x) = x^4$ is strictly convex using just the definition of strict convexity: $$f((1-t)x+ty) < (1-t)f(x)+tf(y)$$ for $0<t<1$. Is this just an algebra slog? If so, I ...
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42 views

How to prove $f(x_1,\ldots,x_n) = \sum x_i\ln x_i - (\sum x_i )\ln(\sum x_i)$ is convex on R++ [duplicate]

How can i prove $f(x_1,\ldots,x_n) = \sum x_i\ln x_i - (\sum x_i )\ln(\sum x_i)$ is convex on R++
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2answers
47 views

How to compute primal variable based on dual variables and their multipliers

I edited this question based on information I got from comments. Assume we have an optimization problem (primal problem). we solve it's dual using some kind of primal-dual interior point solver. So, ...
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30 views

tangent cone to the set

I'm supposed to solve this problem: Let us consider the set $M=\{(x, \sin{x}):x\in\mathbb{R}\}\cup\{\big(\cos(x)-1,x\big):x\in\mathbb{R}\}$ The question is to find the tangent cone to the set $M$ in ...
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25 views

Karush-Kuhn-Tucker conditions for non-linear optimalization

I have the following problem: solve the local conditions (KKT) and find ALL optimal solutions: $$\min f(x,y)$$ subject to $$g(x,y)\le 0$$ $$x\geq0, y\in\mathbb{R}$$ I have some questions to this ...
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1answer
12 views

Gradient of squared distance to a convex set

I have the following problem: Let $f:\mathbb{R}^n \rightarrow \mathbb{R}$, $f(x)=(\operatorname{dist}(x,D))^2$ where $D$ is a convex, close set in $\mathbb{R}^n$. Prove that $f$ is convex and ...
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49 views

Proof that equation is non-convex function

I have a objective function as following $$E(\phi)=\int_{\Omega}(I(x)-m_1)^2H(\phi(x))dx+\int_{\Omega}(I(x)-m_2)^2(1-H(\phi(x)))dx+\int_{\Omega}|\nabla H(\phi(x)|dx$$ where $I$ is an image; $I: \Omega ...
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1answer
13 views

Under what hypotheses is a solution to the Lagrangian multiplier equations automatically a global minimum?

Suppose we are minimizing a function $f(x_1,...,x_n)$ under the conditions $g_1(x_1,...,x_n) = g_2(x_1,...,x_n) = 0$. Under what hypotheses is a solution to the Lagrangian multiplier equations ...
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29 views

Intuition between convex function and convex set

In my text (Luenberger), there is a proposition about convex set Prop: let $f$ be convex function on convex set $\Omega$. The set $\Gamma_c = \{{x: x\in \Omega, f(x) \leq c}\}$ is convex ...
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35 views

Optimization of sum of logs

I have an optimization problem of the form $$\operatorname*{argmax}_{\mathbf{w}} \sum_i \log(1 + \mathbf{w} \cdot \mathbf{k_i})$$ given some set of vectors, $\mathbf{ \{k_i\} }$. I have tried both ...
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1answer
48 views

How to project gradient vector to subspace defined by linear constraints

I have the following set of linear constraints: $$\begin {align}\textbf{y}^T\textbf {x} &= 0 \\ \textbf {0} &\leq\textbf {x} \leq C\cdot\textbf {1},\end {align}$$ where $\textbf {y} \in ...
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1answer
21 views

Is the normalized duality mapping symmetric?

In the area of functional analysis, nonlinear operator theory, the normalized duality mapping on a Banach space $X$ is a set valued map from $J:X\rightarrow 2^{X^*}$ given by \begin{align} ...
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20 views

Product of linear and convex function

More specific, how many maxima are there for product of these two functions: $ f(x) = ax + b $, and $ a > 0 $ $ g(x) $ is (strongly) decreasing convex function, $ \lim_{x\rightarrow\infty} g(x) = ...
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1answer
37 views

A maximization problem within the simplex

Let $\lambda_i$ be an ordered list of $N$ positive numbers, $\lambda_1<\lambda_2<\dots<\lambda_N$. I'm looking for the extrema of the function $$ f=\left(\sum_{i=1}^N p_i \lambda_i ...
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1answer
16 views

Example of convex subset (unbounded) with $\text {rec} (C) = {0}$

Example of convex subset (unbounded) with $\text {rec} (C) = {0}$ I've proved that for a bounded convex subset $C$ it always holds that $\text {rec} (C) = {0}$. However, now I'm looking for an ...
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1answer
20 views

How can a second-order cone problem be expressed as a conic problem?

I realize that a second-order cone is a cone, and thus an SOCP is a type of conic problem. However, to me it doesn't seem so apparent, looking at their equations. Could someone explain how one could ...
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1answer
21 views

Find the edges of a polyhedron P.

Given the polyhedron $P = \{v \in \mathbb R^2 \mid Av \le b\}$ with $A = \begin{bmatrix} -1 & -1 \\ 2 & -1 \\ -1 & 2 \\ 1 & 2 \end{bmatrix}$ and $b = \begin{bmatrix} 0 \\ 1 \\ 1 \\ 2 ...
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2answers
57 views

How to check if given polyhedron is empty

Consider a polyhedron specified as following set of equalities and inequalities $$ \begin{aligned} &\mathbf{A}\mathbf{x} = \mathbf{b},\\ &\mathbf{x} \geqslant \mathbf{0}. \end{aligned} $$ Are ...
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8 views

Is every optimization problem with a piece-wise affine objective function the dual of some differentiable problem?

It is well known that a problem can have a $C^1$ objective function and a convex feasible set, while the dual problem can be piece-wise $C^1$ only. So I'm wondering - if you have a piece-wise affine, ...
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1answer
22 views

Proximal Operator of $\ell_{\infty,1}$ norm of a matrix

How can I calculate the proximal operator of mixed norm $\ell_{\infty,1}$ for any general matrix, $X\in R^{m\times n}$ i.e., $X^* = \arg \min_X ||X||_{\infty,1} + \frac{1}{2\tau} ||X-Y||_F^2$ where ...
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1answer
17 views

Relaxing the elements of a matrix

I try to understand a specific part of the paper "Consistent shape maps via semidefinite programming", where a binary symmetric Input matrix $X^{in}$ is given with $X^{in} \in \{0,1\}^{nm \times nm}$ ...
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15 views

Do quasi-Newton methods check the second-order optimality condition?

I have a practical question about quasi-Newton methods. In quasi-Newton methods, Hessian matrix is approximated. It seems to be impossible for them to check the second optimality condition. In ...
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1answer
36 views

Weighted least squares with nuclear norm minimizaiton, how to optimize?

Nuclear norm minimization is very popularization and formulation is least squares term with nuclear norm term as following, $$\min\limits_{X} ...
2
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20 views

Sion Minmax theorem for integral operators

Suppose $f, g\in S=L^p([0,1],\Sigma,\mu,[0,1])$. The objective $L:S\times S\to R$ is given by $$L(f,g)= \int f (h-g) d\mu, $$ where $h\in S$ is fixed. Could we apply Sion Minmax theorem to conclude ...