0
votes
0answers
17 views

Convex cones and positively homogenous and subadditive functional

Let $V$ be a linear topological space, $K \subsetneq V$ a convex, closed cone with $0 \in K$ and $k \in K \setminus (-K)$. Show that the functional $\varphi : V \to \mathbb R \cup \{ \pm \infty \}$ ...
0
votes
1answer
29 views

Min of concave symmetric function on a convex set

Consider the convex set $$C=\left\{ \mathbf{x}\in \mathbb{R}^N :0\le x_1\le x_2\le\dots\le x_i\le x_{i+1}\le \ldots\le x_N\le \frac 1{N-1}\text{ and } \sum_{k=1}^{N}x_k=1\right\}$$ I need to minimize ...
0
votes
0answers
35 views

Why is pointwise maximum a convex function?

It seems like if you have a family of function $$g = \{a(x), \: b(x), \: c(x), \:d(x)\}$$ $$\text{given} \:\: f(x):= max(g),$$ $$\text{if} \: f(1) = a(1), \: f(2) = b(2), \: f(3) = c(3), \: f(4) = ...
0
votes
1answer
24 views

Why does convexity of a function required the following

What is the significance of the following condition $$\forall x_1, x_2 \in dom(f) , \forall \theta \in [0, 1], f(\theta x - (1-\theta)y) \leq \theta f(x) + (1-\theta)f(y)$$ and why isn't the ...
0
votes
2answers
39 views

Dual of a Semi Definite Programming Problem

How do I write the dual of the following semi definite programming problem? \begin{align} \max_{\lambda,y_i}~&\lambda \\ &\sum_{i=1}^{L}y_i\mathbf{C}_i-\lambda\mathbf{I}\geq 0 \\ ...
0
votes
1answer
17 views

How do I justify that a second order cone is an intersection of half space

I am studying convex optimization right now, and the text book claims that a second order cone is a collection of intersections of half space $$K_n = \bigcap_{ u:\|u\|_2 \leq 1} \{(x,y) \in R^{n+1 } ...
0
votes
0answers
22 views

Exact Line Search in Projected Gradient Descent

How is exact line search adapted for projected gradient descent in convex optimization? One way I think of is that unconstrained exact line search is run, and the new point is projected into the ...
1
vote
1answer
29 views

Convex Sets and extreme supports

Let the set $S$ in $R^n$ consists of the origin $0$ and $n$ lineary independent vectors $T_1, \ldots, T_n$. Show that $C(S)$, the convex hull of of $S$, is the intersection of its extreme supports, ...
1
vote
1answer
16 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 ...
4
votes
0answers
136 views

Properties of the square norm in Banach spaces

Let $X$ be a Banach space with its dual $X^*$. Consider the mapping $f: X\rightarrow \mathbb{R}$ given by $$ f(x)=\frac{1}{2}\|x\|^2. $$ We have know that when $X$ is a real Hilbert space ($X=X^*$) ...
0
votes
1answer
37 views

A Simple Algorithm for Imposing Semi-definite Constraints

What is the simplest algorithm to implement, to impose semi-definite constraints? $\min_{X\succeq 0} f(X) $, where $X$ is an $n \times n$ symmetric matrix, and $f$ is a general smooth convex ...
0
votes
2answers
25 views

A statement for convex sets

The following statement is true or false? Given a convex set $S$ then for any $y \in S$ and $\theta\in[0,1], \theta \in \mathbb R$ there exist $y_1,y_2 \in S, y_1 \ne y, y_2 \ne y$ such that ...
0
votes
0answers
40 views

Online convex programming: Projection followed by normalization

I have the following projected gradient descent online linear programming problem which has been well studied in www.cs.cmu.edu/~maz/publications/techconvex.pdf‎ $\mathbf{y}_{t+1}=\mathbf{w}_t - ...
0
votes
1answer
23 views

How can I prove this problem is quasiconvex?

I'm doing a convex optimization problem. It requires me to fit a rational function to an exponential function. I assumed the original problem would be a quasiconvex optimization problem and based on ...
0
votes
2answers
56 views

Solution in general for a seemingly simple problem

Let $\mathbb{S}$ be a closed, bounded, convex set in $\mathbb{R}^N$. Let $\mathbb{x}=[x_1,\dots,x_N]$ be any arbitrary vector in $\mathbb{S}$. Then what can we comment on the problem \begin{align} ...
0
votes
1answer
44 views

How can the sum of two closed cones be not closed?

Can there be two closed cones $K_1$ and $K_2$ in $\mathbb{R}^3$ such that $K_1+K_2$ need not be closed?
0
votes
1answer
9 views

Conic hull of a proper function

Suppose $f$ is a proper function pn $\mathbb{R}^{n}$with $f(0)>0$.Now consider $$ g(x) = \text{inf}\{t: (t,x) \in \text{cl(cone(epi(}f)))\} $$ Can I always say that $\exists y \in \mathbb{R}^{n} : ...
0
votes
0answers
33 views

KKT conditions for nonsmooth convex problems

What are the KKT conditions for a non-smooth convex function? Is the vanishing gradient of Lagrangian, replaced by $0$ in sub-differential of the Lagrangian, and all other things remain the same? I ...
0
votes
1answer
55 views

Explain the convexity by looking the hessian matrix of a function

The hessian matrix of a function is given by, $$ H = \begin{bmatrix} a & b & c \\[0.3em] b & b & 0\\[0.3em] c & 0 & c \end{bmatrix} $$ where, ...
1
vote
1answer
58 views

Proof of the Moreau decomposition property of proximal operators?

Given the prox operator i.e. $ prox_h (x) = arg min_u (h(u) + 1/2 ||u-x||^2_2) $ the Moreau decomposition property says that $ x = prox_h (x) + prox_{h^*} (x) $ where $h^*$ is the conjugate of ...
0
votes
1answer
31 views

Express a function as difference of convex functions (DC)

is there a way to express the function $$1-\exp \Big( \frac{-\max(0,x)^2}{\alpha} \Big)$$ as the difference of two convex functions (DC)? Thanks
0
votes
1answer
34 views

Relax equality into inequality in convex problem

Let $\mathbf{x}, \mathbf{z}, \underline{\mathbf{x}}, \overline{\mathbf{x}} \in \mathbb{R}^{I}$, where the first two are variables and the last two are given data. I have the following problem: ...
1
vote
1answer
33 views

Minimizing difference and individual variables in convex problem

Let's say I have the following optimization problem: $$ \begin{align*} \min_{\mathbf{x},\mathbf{y}} & \sum_i x_i-y_i \\ \mathrm{s.t.} & \{\mathbf{x},\mathbf{y}\} \in ...
0
votes
1answer
24 views

Prove that the intersection of convex sets is convex using the following three points…

I want to prove each point, then, use points (1) and (2) to prove (3). $C_{1} = \lbrace x \in \mathbb{R}^{n} \mid h(x) = 0 \rbrace $ is convex iff $h(x)$ is affine in $C_{1}$ $C_{2} = \lbrace x ...
1
vote
2answers
45 views

Why is this weighted least squares cost function a function of weights?

Here is a picture from my book regarding weighted least squares: Totally lost here, so I extracted the main nested issues confusing me: First Question: I know that in any LSE we want to minimize ...
2
votes
1answer
37 views

“Support function of a set” and supremum question.

I have already learned about what a supremum means from wikipedia and from another answer here. However I am not quite sure what 'supremum over a set of functions' means exactly. As an example, my ...
0
votes
2answers
50 views

Inequality involving max is confusing me.

I am trying to understand one line in a derivation here. Simply put, the statement is that: $$ max\{\theta \ f_1(x) + (1-\theta) \ f_1(y) \ , \ \theta \ f_2(x) + (1-\theta) \ f_2(y)\} \leq \theta \ ...
1
vote
1answer
45 views

Examples of affine functions and convex sets

I'm just learning about convexity and affineness, and I've read over some similar questions asked here, but those were more about general properties. I need some help applying those properties to a ...
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 ...
0
votes
2answers
48 views

Is the geometric-to-arithmetic function convex or concave?

Consider a vector $\mathbf{x} \in \mathbb{R}_{++}^N$. Also consider two functions, $g(\mathbf{x}): \mathbb{R}^N \rightarrow \mathbb{R}$, and $a(\mathbf{x}): \mathbb{R}^N \rightarrow \mathbb{R}$, ...
0
votes
3answers
66 views

Concave function divided by a convex function. What is the result?

Let us say that I have a function $f(x)$ that we know is a concave. And let us also say that we have another function $g(x)$ that is a convex. If I make a new function, $h(x) = \frac{f(x)}{g(x)}$, ...
1
vote
1answer
31 views

How to verify correctness of a Fenchel conjugate derivation

Suppose I derived Fenchel conjugate of a function. My goal is to check if my solution is right. Suppose the steps are not available any more and only the final solution is present. Is there any ...
0
votes
0answers
21 views

Show that the Rosenbrock function is strictly convex for a specific region

So we know that the Rosenbrock function is a test function of sorts, but can anyone prove that a specific region is strictly convex? Rosenbrock eqn: $ f(x_{1},x_{2}) = 100(x_{2} - x_{1}^{2})^{2} + ...
1
vote
1answer
73 views

Tangent Cone is a cone?

I first give two definitions. Def1: A set $S$ is a cone if $x \in S, \lambda \geq 0 \implies \lambda x \in S$. Def2: Let $S$ be any set (we may assume $\mathbb{R}^n$ with the usual Euclidean ...
0
votes
2answers
29 views

is convex hull of intersection equal to intersection of convex hull

is $convexhull(S_1\cap S_2)=convexhull(S_1)\cap convexhull(S_2)$ where $S_1$ and $S_2$ are finite sets.
0
votes
0answers
25 views

Isomorphisms of Convex Cones

A convex cone $C$ is a subsets $C \subseteq V$ of a vector space which is closed under positive linear combinations, i.e. for $\lambda, \mu > 0$ and $u,v \in C$ it is $\lambda u + \mu v \in C$. An ...
0
votes
1answer
79 views

Can a Lipschitz continuous function be strictly convex?

Let $\varphi:\mathbb R^n\to\mathbb R$, and suppose for all $x,y\in\mathbb R^n$, $$\|\varphi(x)-\varphi(y)\|\leq L\|x-y\|$$ for Lipschitz constant $L$. Is it possible for such a function to satisfy ...
1
vote
0answers
20 views

Dual of the mixed $\ell_1/\ell_2$ norm?

The mixed $\ell_1/\ell_2$ norm $\Omega_{12} $ is defined as $\Omega_{12}(x) = \sum_g ||x_g||_2$ where $x_g$ are disjoint subsets of the elements of the vector $x$. This is used in machine learning ...
1
vote
0answers
23 views

can we prove that a certain supremum of affine functions is frechet differentiable or at least continuous?

Let $X$ be a Hilbert space. Let $A\colon \operatorname{dom} A\to X$ be linear operator with closed graph (not necessarily bounded). Define $$ g\colon \operatorname{dom}A\to \mathbb{R}:x\mapsto ...
0
votes
1answer
66 views

Composition of convex function and affine function

Let $g: E^{m} \rightarrow E^{1}$ be a convex function, and let $h: E^{n} \rightarrow E^{m} $ be an affine function of the form $h(x)=Ax+b$, where $A$ is an $m \times n$ matrix and $b$ is an $m \times ...
-1
votes
1answer
69 views

Prove or disprove the concavity of the function [closed]

Prove or disprove the concavity of $f$ over the following two domains. $$f(x_1,x_2)=10-2(x_2-x^{2}_{1})^{2}$$ defined either over $$S_1=\{(x_1,x_2) : -1\leq x_1 \leq 1, -1 \leq x_2 \leq ...
0
votes
1answer
66 views

convexity of matrix “soft-max” (log trace of matrix exponential)

In convex optimization it is often convenient to use the following smooth approximation to $\max\{x_1, \ldots, x_n\}$: $$ f_\lambda(x_1, \ldots, x_n) = \frac{1}{\lambda}\log \sum_{i = 1}^n{e^{\lambda ...
0
votes
0answers
13 views

Uniqueness of the solution to a quadratic opt problem

Consider a positive definite matrix $\boldsymbol H$, the known vectors ${\boldsymbol b}$ and ${\boldsymbol a}_i$. Now the minimization problem is casted with respect to the vector ${\boldsymbol x} $ ...
8
votes
1answer
155 views

Stochastic gradient descent for convex optimization

What happens if a convex objective is optimized by stochastic gradient descent? Is a global solution achieved?
0
votes
2answers
32 views

Show that the set of points that are nearer $a$ than $b$ with respect to $\lVert \cdot \rVert_2$ is convex

I am trying to show the above statement: Show that the set of points that are nearer $a$ than $b$ in the sense of Euclidean $\lVert\cdot\rVert_2$ are convex. My attempt This follows from the ...
1
vote
1answer
63 views

The convex conjugate of a quadratic form with positive semi definite matrix

I want to find the convex conjugate (Legendre transform) of a quadratic for $1/2x^{t}Qx$ when $Q$ is positive semi-definite. If Q is non-singular, the solution is easy - the gradient is $y-Qx=0$ so ...
0
votes
1answer
57 views

Cyclic monotonicity of sub-differential domain and convex property

I am looking for hints/proof's overview/reference about this proposition : Let $S\subset \mathbb{R}^d\times\mathbb{R}^d$. There exist a convex function $\phi$ such that $S\subset \partial\phi$ ...
1
vote
3answers
57 views

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 $\mathbb R^2_{++}=[0,+\infty)\times [0,+\infty)$.
1
vote
2answers
66 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 ...
0
votes
1answer
40 views

Finding the Expansion of a Separable Convex Optimization Problem

Hi there is a convex optimization problem in this paper which I am trying to implement in mosek. The author specifies that they also implement it using the separable optimization method. Specifically ...