0
votes
2answers
19 views

Optimization of Product of Different Objective functions (Ex.: Maximize The Product of projections of a complex vector)

Suppose We have this optimization problem which is convex $\mathbf{x}={\arg}\: \underset{\mathbf{x}}\max f_{i}\left (\mathbf{x} \right )$ But the product of different objective function is ...
1
vote
1answer
17 views

Impact of constraints on convex optimization

Let me start by saying I know almost nothing about optimization so please bear with me. Basically, I am wondering whether it is possible to solve a problem with two constraints by solving the problem ...
0
votes
1answer
15 views

Finding the maximum/minimum of a homogeneous function on $R^n$

Suppose that $f:R^n\to R$ is homogeneous. Also, suppose that the $argmin_xf(x)$ is non-empty. Is it true that if there exist $x^*\in R^n$ such that $f(x^*)=0$, then $x^*=argmin_xf(x)$?
1
vote
1answer
34 views

KKT point of a constrained optimization problem

Min$_{x}~x$ Subject to $x \geq 0$ For this problem, is $(x^{*}, \lambda^{*})=$$(0,0)$ a KKT point ? My try : I formulated corresponding Lagrangian and tried to find out the KKT point(s). ...
0
votes
1answer
32 views

Convex functions -> quasi-convex functions -> … can we weaken the assumptions?

First of all let me say that I'm new to optimization. I realized that quasi-convex functions share with convex functions some nice properties, so I wonder if we can push the weakening a little ...
-2
votes
0answers
67 views

Homework: Proving stationary point over a closed convex set.

I'm stuck on this and need some help. I know the definition of a stationary point over a set. $x \in X \implies \nabla f(x^*)^T(x-x^*) >= 0$ How do i show it? I've thought of beginning by ...
0
votes
1answer
28 views

Can SVD help to solve (inequality) constrained least squares problem?

Consider the following minimization problem: $$ ||Q u - h^{o} ||^{2} \to min \;\;\; s.t. \; u \geq 0 $$ where $Q$ is $m \times n$ matrix and $u$ is $n$-dimensional vector and $h^{0}$ is ...
0
votes
0answers
41 views

Expressing rank condition of a matrix in terms of its elements

Let $x \in \mathbb{R}^{n}$, define $X = xx^{T}$. I have an optimization problem with some linear constraints and few quadratic constraints, and I have to solve for $x$. Using $X$ as the unknown ...
3
votes
2answers
73 views

Singular Values of Matrix as Optimization Problem

Assume that $A$ is a positive semidefinite symmetric matrix. It is known that $$\max_{||y||\leq1} \quad y^TAy$$ Has an analytical solution which is the maximum eigenvalue of $A$. This isn't hard ...
0
votes
0answers
63 views

Calculation of the set for the polar tangent cone?

I have the following theorem in my book. Assume that $\tilde{x}$ is a local minimum from a minimization problem and that f(.) is differentible at $\tilde{x}$ Let $T_X(\tilde{x})$ be the tangent cone ...
-1
votes
2answers
36 views

Constraints in optimization; redundant hardness?

This is not an accurate mathematical problem, and rather a philosophical and ambitious question. As far as I know, unconstrained problems are easier than constrained problems; right? This is mostly ...
2
votes
2answers
96 views

What change of variables, if any, transforms this nonconvex problem into a convex one?

I'm looking for a convex reformulation, if any exists, of the following minimisation problem: Let $A$ be a symmetric, positive definite $n \times n$ matrix, and $b \in \mathbb{R}^n$. Minimise ...
1
vote
0answers
47 views

Some problems with a proof of the Farkas Lemma

The following is a proof of the Farkas Lemma that is creating me quite some problems. [I presented the all proof simply to point out the notation used by the author.] My problem is with the last part ...
1
vote
1answer
58 views

Minimizing a linear function on a strictly convex set.

All the theorems that I know considering the uniqueness of a solution to a minimization/maximization problem requires the strict convexity/strict concavity of the objective function. But consider the ...
0
votes
0answers
26 views

How fast Interior Point method can be when solving Quadratic Programming problems?

Given the following Quadratic Programming problem: $\;\;\;\;\; \min x^TQx+c^Tx $ s.t. $Ax=b$ $\;\;\;\;\; x\ge0$ where $x\in \mathbb{R}^n$, $Q \in \mathbb{R}^{n \times n}$ is a positive ...
1
vote
1answer
82 views

What is a convex optimisation problem? Objective function convex, domain convex or codomain convex?

My teacher in the course Mat-2.3139 did not want to answer this question because it would take too much time. So what does a convex optimisation problem actually mean? Convex objective function? ...
0
votes
1answer
41 views

(pseudo-/quasi-)convexitiy of ratio between quadratic and affine function

Let $X\subseteq\mathbb{R}^n$. I have the following function $f:X\rightarrow\mathbb{R}$: $$ f({\bf x})= \frac{c_1 + \sum_{i=1}^n a_ix_i +\sum_{i=1}^n b_ix_i^2}{c_2+\sum_{i=1}^n d_i x_i}\enspace.$$ All ...
2
votes
0answers
44 views

the objective function $\|F\|_F^2$ is quasiconvex in the optimization?why?

I have read a paper, but I can not understand one optimization thoroughly.Generally, Frobenius norm of one matrix, $\|F\|_F^2$, as the objective function is convex, so we can resolve it not using the ...
0
votes
1answer
83 views

How to get the closed form solution of a non-convex optimization problem?

I want to know if there is a closed form expression for the optimal objective function? How can I get it, if it does exist? Condition: $h,f\in \mathbb{C}^{N\times1}, \epsilon > 0 $. $\max \ \ ...
0
votes
0answers
29 views

Can I put a constraint in the optimization problem that my solution is a low pass filter?

My problem consists in finding a vector B: $$ min ||AB||_1 + ||A-AB||_2 $$ $$\text{subject to}$$ $$\text{B is a low pass filter}$$ $$\text{number of non zero elements of B is smaller than some number ...
0
votes
0answers
42 views

Solution of nonlinear matrix optimization problem

I have the optimization problem ($L,A$ are regular matrices and $A$ is Hurwitz stable): min $||L||_2$ subject to $LAL^{-1} + L^{-T}A^{T}L^{T}<0$ Can the nonlinear problem be formulated as a LMI? ...
0
votes
0answers
11 views

Variation of Optimal Solution with other Parameters

I have the following kind of optimization problem. $$\min_{f_1,f_2,\cdots\ ,f_L}\sum_{i=1}^L \mu_{i}D_i(\lambda_i,f_i,\gamma)$$ sub. to $$\sum_{i=1}^Lf_i=1-\delta\\ f_i\ge 0\quad i=1,2,\cdots \ ...
1
vote
1answer
62 views

A minimization problem

Define $$L(w,u)=\frac{1}{2}\|w-u\|^2+\beta \left\|\frac{w}{x}\right\|,~w,u\in \Bbb{R}^n$$ where $$\frac{w}{x}=\left(\frac{w_1}{x_1},\ldots, \frac{w_n}{x_n}\right)$$ $$\|x\|=\sqrt{x_1^2+\cdots+x_n^2}$$ ...
1
vote
1answer
45 views

A minimization problem [duplicate]

Define $$L(w,u)=\frac{1}{2}\|w-u\|^2+\beta \|\frac{w}{x}\|,~w,u\in R^n$$ where $$\frac{w}{x}=(\frac{w_1}{x_1},\dots, \frac{w_n}{x_n})$$ $$\|x\|=\sqrt{x_1^2+\cdots+x_n^2}$$ Given $u$, $x$ and $\beta$, ...
0
votes
1answer
463 views

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 ...
2
votes
1answer
94 views

minimizing $\sum_{i=1}^n \max(|x_i - x|, |y_i - y|)$

Suppose there are $n$ points $(x_i, y_i)$ for $i = 1,\ldots,n$. Please find another point $(x, y)$ to minimize function: $$\sum_{i=1}^n \max(|x_i - x|, |y_i - y|)$$
2
votes
2answers
335 views

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 ...
1
vote
1answer
208 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 ...
0
votes
1answer
237 views

Positive semidefinite Matrix examples query

This might be really dumb question but I've just started dealing with such matrices. I would like to know why $$\begin{bmatrix} 0 & 1 \\ 1 & x \end{bmatrix}$$ cannot be a positive semidefinite ...
0
votes
1answer
49 views

KKT formulation

How to reformulate the following problem $$min \frac{1}{2} (x_1-a_1)^2+ \frac{1}{2} (x_2-a_2)^2$$ $$s.t. \mathbf{1}^Tx=1$$ $$ ||x||_2\leq2$$ as the following system of KKT conditions: $$(1 + ...
2
votes
0answers
62 views

Nonlinear optimization of constraint parameter - subdifferential?

Disclaimer: I discovered that the FAQ suggests to post research-level to mathoverflow instead of math.stackexchange. I "moved" the question accordingly, cp. post at mathoverflow. Sorry for the ...
1
vote
3answers
144 views

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 ...
1
vote
0answers
75 views

Formulation of a problem as semidefinite programming

I would appreciate some help with this problem: $R$ is a positive semidefinite matrix $\in{R}^{n\times n}$, $A \in{R}^{n\times m}$. I need to formulate this optimization problem as semidefinite ...
2
votes
0answers
239 views

SDP relaxation of non-convex QCQP and duality gap

Short version Is there a duality gap between a QCQP problem and the SDP problem obtained through lagrangian relaxation? A paper I'm studying is using this fact, but I cannot achieve the authors' ...
0
votes
2answers
75 views

Anyone saw this interesting function before?

Say $\theta\in\Re^n$ and $\theta_i\in(0,1)$ for all $i$. Define $$ f(\theta) = \frac{1}{n}\sum_i^n\{(1-\theta_i)\log(1-\theta_i)+\theta_i\log\theta_i\} $$ It is easy to see the minimizer of ...
1
vote
1answer
56 views

Distance between a point to a $2d$ ellipse in $3d$ ambient space

Suppose we are working in the 3D Euclidean space. We are given an arbitrary point $p$ and a 2d ellipse: $$E=\{x:x^TQx\leq1,x^Tq=0\},$$ where $Q$ is a positive definite matrix and $q$ is an ...
0
votes
1answer
193 views

Lipschitz constant for optimization of multivariate function

I intend to implement an optimization algorithm which requires the computation of the Lipschitz constant. My function is a multivariate function with more than 50 variables. I am wondering whether ...
2
votes
2answers
125 views

optimality of quadratic programming problems

Suppose we have a general quadratic programming problem: \begin{align} \min_{x}\,\,&c^Tx+\frac{1}{2}x^TQx,\\ \mbox{s.t.}\,\,& Ax=b,\\ &x\geq0, \end{align} where $Q$ is positive ...
0
votes
1answer
127 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 ...
2
votes
0answers
30 views

Convex formulation of a nearly convex optimization problem

The following problem has come up in my studies of logarithmic norms. I wish to find $\mu \in \mathbb{R}$ and a positive semidefinite $B$ so as to minimize the convex function $c \mu - \log\det(B)$ ...
1
vote
2answers
237 views

Maximize the product of linear functions

Suppose $f(x,y) = \prod_{i=1}^n (a_ix+b_iy)$ where $n$ is a constant larger than 500, and $a_i>0$, $b_i>0$ are known coefficient. There is only one global maximum. What's the most efficient ...
0
votes
1answer
241 views

Convex Functions: Property Proof

Let $f\colon S\to \mathbb R$ be a $C^1$ function on a convex domain $S \subseteq \mathbb R^n$. Show that if $f$ is convex then $(\nabla f(x) - \nabla f(y)) \cdot (x-y) \ge 0$ for all $x,y \in S$. ...
1
vote
1answer
175 views

Convex Functions: Proofs

Let $f$ be a monotone nondecreasing function of a single variable which is also convex. Let $g$ be a convex function defined on a convex set $G$. Is it true that the composition of these functions ...
0
votes
1answer
466 views

is nonlinear least square a non convex optimization?

linear least-squares are convex optimization. Are nonlinear least squares also convex optimization? Can someone please give some simple examples?
2
votes
1answer
863 views

Prove the supremum of the set of affine functions is convex

Let $\langle f_i \rangle _{i \in I}$ be a family of affine functions on a convex and compact set $\Omega \subset \mathbb{R^d}$ such that $f_i = a_i.x +b_i$ for $x \in \Omega$. Prove that f, defined by ...
1
vote
0answers
49 views

why is it important to have $\max_x \min_y f(x,y)=\min_y \max_x f(x,y)$?

I am currently trying to understand the minimax theorem of Von Neumann and the improved versions of this theorem. At any case we have the property $$\max_{x\in X} \min_{y\in Y}f(x,y)=\min_{y\in Y} ...
1
vote
1answer
166 views

Max function on a closed compact convex set.

Consider a closed convex compact subset $\mathbb{S}$ of $\mathbb{R}^N$ while we denote any of its point by $x=[x_1,x_2,\ldots,x_N]^T$. Define the function \begin{align} f(x)=max(x_1,x_2,\ldots,x_N) ...
0
votes
1answer
139 views

Convex optimization problem to quadratic programming problem

Briefly, have the following problem: \begin{equation} \sum_{i = 0}^n a_i \ (max [ F_i( \bar x ), 0 ] )^2 \rightarrow min, \\\\ s.t.\\\\ A \bar x \leq b \end{equation} where $ F( \bar x ) $ is a ...
0
votes
1answer
43 views

Concave optimal value?

Let $A \in \mathbb{R}^{n \times m}$ and $B \in \mathbb{R}^{m \times n}$. Consider a compact set $C \subset \mathbb{R}^n$. For all $x \in C$ define $$ f(x) := \min_{y \in \mathbb{R}^m} \{ x^\top A y ...
0
votes
0answers
72 views

Max Quadratic Expression

Let $A \in \mathbb{R}^{n \times n}$, $A = A^\top$, $B \in \mathbb{R}^{m \times n}$, and $\mathcal{C} \subset \mathbb{R}^n$ be a compact, convex set. For $A$ not negative semidefinite, how to globally ...