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

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Conversion into linear program

I have an optimisation problem with decision variables that are multiplied with another (a weighted average is calculated). I'd like to convert it into a linear program. I found this link that ...
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Does analytical solution exist for this convex euclidean affine projection problem with non-negativity constraints?

I've come across this convex optimization problem in my research where I need to project a matrix $X_0$ onto a non-negative affine space constraint and box constraints. Concretely, $X \in ...
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Difficulty in understanding a solution: Constraint minimization of sum of Non-symmetric matrices

I am trying to understand why there is significance difference in the performance of two proposed solutions. Original question (Constraint minimization of sum of Non-symmetric matrices) ...
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59 views

Constraint minimization of sum of Non-symmetric matrices

I am trying to find closed form solution to following problem \begin{equation} \begin{array}{c} \text{min} \hspace{4mm} \big(\lambda_1\left( \mathbf{y}^T V^{(1)}\mathbf{x} \right)^2 + ...
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Optimizing linear function with non-linear constrants

I got stuck with the following optimization problem: \begin{equation*} \begin{aligned} & \underset{x_1,\ldots,x_n}{\text{max}} & & \mathbf{c}^T\mathbf{x} \\ & \text{subject to} & ...
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convexity of inverse of a matrix

I know that the function $f(X)$ which maps matrix $X$ to $Tr((X)^{-1})$ is convex for symmetric positive definite $X$. This has also been answered in Is the trace of inverse matrix convex? for ...
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1answer
367 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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1answer
26 views

how to find the solution of this cost function?

I have the following cost function. $J = \sum_{i=1}^N a\, Trace(W^TX_iW) - b\, Trace(W^TY_iW)$ Where $X_i$ and $Y_i$ are symmetric matrices, $a$ and $b$ are scalars. How can I find W?
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371 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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49 views

Show both relaxations of boolean LP give equal lower bounds

Given the boolean LP: $$\text{Minimize}\;\; c^Tx$$ $$\text{Subject to}\;\; Ax \leq b$$ $$\hspace{57mm} x_i(1-x_i)=0\;\; i=1,...,n$$ Show that the LP relaxation: $$\text{Minimize}\;\; c^Tx$$ ...
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Determining corners of this convex set

Let $N \geq 2$ be an integer. Let $P:= \{ (a_1, \ldots, a_N) \in [0, 1]^N : \sum_n a_n = 2 \}$. Is $P$ the convex hull of $P \cap \{0, 1\}^N$? Edit: This is apparently true, see the beginning of ...
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Fenchel Conjugate of a norm squared

I was wondering if the fenchel conjugate of the $\frac{1}{2}||u||^2$, is the $\frac{1}{2}||u||_*^2$, where $||.||_*$ is the dual norm of $||.||$. This seems to be true for the $\ell_2$ norm. However, ...
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1answer
20 views

How do I set a lower bound to the solution's norm in a QP problem

I know that LASSO-regularization can be used to scale into an $L_1$ upper bound for a solution. But what if I want the norm to be within a specific range $[a,b]$? ie. I also want to set a lower bound? ...
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23 views

Log concavity/convexity of a determinant

I was wondering if anyone would be able to help me determine whether the following quantity is log concave or not with respect to $\alpha$? $$\left[\det(\textbf Y^\top \textbf P \textbf G \textbf ...
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How to solve cardinality (number of non-zero elements) problem through convex optimization

So here is the problem: you wanna minimize the number of non-zero elements of a vector x through the following optimization : \begin{array}{ll} \text{minimize} & \mathop{\textrm{Card(x)}} \\ ...
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22 views

Proof - extreme point of a convex set

everybody! I am wondering how to prove the following theorem: Let $S \subset \mathbf{R}^{n}$ be a non-empty closed convex set. Then $S$ has at least one extreme point iff $S$ does not contain any ...
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1answer
27 views

strong convexity of loss function in multi-dimensional (high-dimensional) space

My question is based on this paper (see the last 10 rows in page 7). It seems this is a general claim: In machine learning or statistic, the loss function $l(W^TX, y)$ (a linear predictor) can never ...
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3answers
29 views

Questions about coerciveness and convexity

I just have a few yes/no questions, and would really appreciate if you could correct me where I am wrong, and for what fundamental flaw I have. 1. Would the set of coercive functions a linear space? ...
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1answer
17 views

Equality Constraints in Quadratic Programming

Now I am new to the world of primal-dual algorithms and I want to understand the SOCP-Code of Lobo/Vandenberghe/Boyd (primal dual interior point method). Currently I am working through Goldfarb and ...
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2answers
20 views

How to find ellipsoid bounding the intersection of an ellipsoid and half-space?

How does one prove that the bounding ellipsoid $E(A', a')$ of the intersection of an ellipsoid $E(A,a) = [ x | (x-a)^TA^{-1}(x-a) ]$ and half-space $H = [x | c^Tx \le c^Ta ]$ is given by the ...
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How to show $ \sup \inf g(x,y) \leq \inf \sup g(x,y)$?

Came across this little practice exercise, and I couldn't properly convince myself of this relation: Let $X,Y \subset \mathbb{R}^n$ and $g:X\times Y \rightarrow \mathbb{R}$. Show that $$\sup_{y \in ...
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Maximin problem as LP?

Consider the following setting. Let $A\in \mathbb{R}^{3 \times m}$ and $B\in \mathbb{R}^{m\times 3}$ be two matrices such that each of their columns must add up to a given $c\in \mathbb{R}$. Denote by ...
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Coerciveness and Positive definiteness relation?

Let $A ∈ \mathbb{R}^{n×n}$ be a symmetric matrix. How can I demonstrate that A is positive definite iff the function $q(x) := x^TAx$ is coercive . I know the eigenvalues of A have to be positive for ...
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1answer
28 views

Why does the Weierstrass theorem fail if a set is not compact?

By Weierstrass theorem I mean that if $f:\mathbb{R}^n \to \mathbb{R}$ is continuous and $C \subset \mathbb{R}^n$ is compact, then the theorem asserts that a solution $x^*$ of $$ \text{min} _{x\in ...
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Union of 2 convex sets

Let $f : \mathbb{R}^n→ \mathbb{R}_∞$ be convex over the sets A, B which are also convex. $A ∩ B = ∅$ and $A ∪ B$ is convex. Then is $f$ is convex on $A ∪ B$? Why or why not? I am confused ...
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41 views

Uniqueness of constrained maximum

I have the following constrained maximisation problem $$\begin{array}{ll} \text{maximize}_{x,y,c,d} & q f(c) +(1-q) f(d) \\ \text{subject to} & x+y\leq 1 \\ & cq\leq y \\ & d ...
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1answer
26 views

Proving convexity using the Hessian

Suppose I have $f: \mathbb{R}^n \to \mathbb{R}_\infty$ which is twice continuously differentiable, on some convex set C, which is open. How can I prove that $f$ is convex over C, iff the hessian ...
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1answer
17 views

Problems with vector vector derivative in optimization

I have a loss function of the followoing form: $L(\mathbf{a}) = \|\mathbf{b} - \mathbf{a}\|_2^2$ Where, $\mathbf{a}$ and $\mathbf{b}$ are vectors of dimension $d\times 1$. I need to calculate ...
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Is f(x)=-log(x) a closed function?

I am reading Convex optimization written by Stephen Boyd. In page 640, there is an example said \begin{equation} f(x)=-log(x) \end{equation} is a closed function. But this function seems does not ...
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Coerciveness of a function - help

I'm trying to show that $$f(x_1,x_2,x_3) = e^{x_1^2 + x_2^2} + (x_1^2 + x_2^2 + 3x_2)^{500}$$ is not coercive, but am struggling to see anything. Any help is appreciated!
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Which one(are) true?

My Effort: a,b are true. As f convex means for any 2 points in the curve , curve lies below the line. Thinking geometrically I think c is incorrect as if f negative |f| is positive. I' ll be glad if ...
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61 views

Laplacian Regularization with Sparse group Lasso

I have an optimization problem that is of the form: $\{\textbf{A}\} = argmin \{tr(\textbf{A}^\top L \textbf{A}) + \lambda_1||\textbf{A}||_1 + \lambda_2||\textbf{A}||_{2,1}\}$ where $\textbf{A}$ is a ...
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Showing convexity of a function with the restriction over an arbitrary line proof

Let $f : \mathbb{R}^n → \mathbb{R}_∞$ be a function and let $C ⊂ dom f$ be a convex set. $$**Part I**$$ Prove that $f$ is a convex function if and only if $f$ is convex over every line $L_{v,x_0}$ ...
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Convexity over a line given a convex interval [duplicate]

Let $f : \mathbb{R}^n \to \mathbb{R}_∞$ be a function. I want to prove that $f$ is convex over the line $L_{v,x_0}$ iff $\psi : \mathbb{R} \to \mathbb{R}_∞$ $\psi(t) := f (x_0 + tv)$, is convex ...
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Subsets being cones

I am trying to self-study convex optimization and still trying to get into the gist of it. There is a question in my text as follows: Let $V$ be the set of sequences whose terms are contained in ...
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1answer
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Subdifferential is closed, convex and bounded

If $f: \mathbb{R}^n \to \mathbb{R} \cup \{+\infty\}$ is convex, how can I show that $\partial f(x_0)$ (sub differential) is closed and convex, and also bounded (bounded when f over the entire domain)
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Is the support function always unique for a convex set?

Given an arbitrary set $A ⊂ \mathbb{R}^n$ , the support function associated with the set $A$ $ σ_A : \mathbb{R}^n \to \mathbb{R} ∪ \{+\infty\}$ is defined as $\sigma_A(x):= \sup_{z \in A} \langle ...
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How to find a hyperplane

Let $A, B ⊂ \mathbb{R}^n$ be two nonempty sets such that $A ∩ B = ∅$. $H(A, B) := \{(w, d) ∈ \mathbb{R}^{n+1} : \sup_{x\in A} \langle w,x\rangle ≤ d ≤ \inf_{y \in B} \langle w, y\rangle \}$ How do I ...
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2answers
19 views

Showing coercivity of a function

I am well attuned to the definition for a function to be coerce, which is that $\lim_{\|x\| \to \infty}f(x) = \infty$ ie the values of $f$ go to infinity as the norm goes to infinity. So Ex.1 ...
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does this convex set have a specific name?

Let $x_1,\dots,x_N$ be points of $\mathbb{R}^n$. Define the following set: $\mathcal{A} = \left\{\sum_{j=1}^N a_j x_j : -1 \le a_j \le 1, \, \, \forall j=1,...,N\right\}$. It is an easy exercise to ...
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What is the time complexity of conjugate gradient method

I have been trying to figure our the time complexity of conjugate gradient method I have to solve a system of linear equations given by $$ Ax=b $$ where A is sparse and positive definite symmetrix ...
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1answer
27 views

Support function of a set is convex

Given an arbitrary set $A ⊂ \mathbb{R}^n$ , the support function associated with the set $A$ $ σ_A : \mathbb{R}^n \to \mathbb{R} ∪ \{+\infty\}$ is defined as $\sigma_A(x):= \sup_{z \in A} \langle ...
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1answer
16 views

Convexity of a subset is convex?

Let $V$ be the set of sequences whose terms are contained in $\mathbb{R}^n . V$ is the set of functions $x(·) : N → \mathbb{R}^n $ which we denote as $\{x_n\}_n \subset \mathbb{R}^n$. $V$ is a vector ...
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1answer
29 views

The set of separating hyperplanes is a convex cone

Let $A, B ⊂ \mathbb{R}^n$ be two nonempty sets such that $A ∩ B = ∅$. $H(A, B) := \{(w, d) ∈ \mathbb{R}^{n+1} : \sup_{x\in A} \langle w,x\rangle ≤ d ≤ \inf_{y \in B} \langle w, y\rangle \}$ I can't ...
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1answer
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Showing the intersection/union of a cone is a cone

Defining a set $C \subset \mathbb{R}^n$ as a cone if for ever $x \in C$ and $\alpha \geq 0$ we have $\alpha x \in C$. ie they are closed under scalar multiplication. How can I show that the ...
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48 views

When the closure of a convex set contains a ball

Suppose $C$ is a convex set in $\mathbb{R}^n$ whose closure contains the open ball $B(x,r)$. Is it true that $C$ contains $B(x,r)$? Motivation: I am asking this because something like this seems to ...
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Eliminating variables from an SOCP

Given an SOCP problem $$ \begin{array}{ll} \text{minimize}&w^Tx\\ \text{subject to} &\|A_i x + b_i\|_2 \le c_i^T x + d_i ~~~~~~~ 1 \le i < N\\ \end{array} $$ where $x$ is partitioned into ...
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1answer
29 views

Showing affinity of a function - proof help

Let $V$ be the set of sequences whose terms are contained in $\mathbb{R}^n . V$ is the set of functions $x(·) : N → \mathbb{R}^n $ which we denote as $\{x_n\}_n \subset \mathbb{R}^n$. $V$ is a vector ...
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32 views

Positive semidefiniteness of a matrix -to examine convexity- What mistake have I made?

$f(x) = \begin{cases} e^{x_1x_2} & x_1, x_2 \geq 0 \\ +\infty & otherwise \\ \end{cases}$ The Hessian of this matrix is: $H = \begin{pmatrix} x_2^2e^{x_1x_2} & ...
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1answer
40 views

Help with this convex set proof

Take $C ⊂ \mathbb{R}^n$ a convex set. Fix $x_0 ∈ C$ and a nonzero vector $v ∈ \mathbb{R}^n$ . Define the set $I(x_0,v) := \{t ∈ R : x_0 + tv ∈ C \}$. Prove that $I_(x_0,v)$ is a convex subset of ...