Convex analysis is the study of properties of convex sets and convex functions. For questions about optimization of convex functions over convex sets, please use the (convex-optimization) tag.

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Geometrical interpretation of Pseudoconvexity?

I see in wolfram that a function $f$ is pseudo convex if it satisfies following, $\nabla f(x)\cdot (y-x) ≥ 0 \Rightarrow f(y) ≥ f(x) $ My question is, with this definition, how come $g(x)=x^3$ is ...
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Why is the Euclidian norm convex, if the square root function is concave?

I have some trouble figuring out if the Euclidean norm is convex. $\left\|{\boldsymbol {x}}\right\|:={\sqrt {{\boldsymbol {x}}\cdot {\boldsymbol {x}}}}$ On one side I read that all norms are convex (...
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The Set of Extremal Point Need not be closed

Exercise: Consider the Convex Hull $C$ of the points $(0,0,\pm 1)$ and the circle $\{(1+\cos\varphi,\sin\varphi,0): 0\leq \varphi \leq 2\pi\}$ in $\mathbb{R}^3$. Determine the extremal point of $C$. ...
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Convex set or not?

This question is related to my previous question Set of all positive definite matrices with off diagonal elements negative I know that the set of all positive definite matrices form a convex set. ...
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8 views

$L^p([0,1])$ stricly convex [duplicate]

Exercise: For which $p\in [1,\infty]$ is $L^p([0,1])$ strictly convex? Solution: For strict convexity we have two equivalent definition: If $x\neq 0\neq y$ and $\Vert x+y\Vert=\Vert x\Vert+\Vert ...
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$C([0,1])$ strictly convex

My question is quite simple: Is the space $C([0,1])$ strictly convex? Where strict convexity is defined as: if $\Vert y+x \Vert=\Vert y\Vert+\Vert x\Vert$ then it implies that is exists an $\lambda&...
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1answer
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Why is this matrix symmetric?

There is an example in the Convex Optimization lecture notes, Boyd. He just said in the lecture that the matrix which is underlined in red color is symmetric! How can we claim that when there is no ...
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how to calculate infimum of Augmented Lagrangian?

should any body explain that how do we calculate these step? \begin{align*} L(x,y) &= f(x) + y^T(Ax-b)\\ g(y) &= \inf_x \, L(x,y) \\ &= \inf_x \, f(x) - \langle -A^Ty, x \rangle - \langle ...
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Nesterov's bound between quadratic and strongly convex cases?

Are there some examples of simple & strongly convex functions for which the convergence bound of Nesterov’s Accelerated Gradient Method is better than Nesterov’s bound for strongly convex case $\...
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1answer
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Why isn't $\ell^p$ locally convex for $0<p<1$?

I believe we have to distinguish the finite-dimensional from the infinite dimensional case. Regardless, if $0<p<1$, $\|x\|_p := (\sum |x_i|^p)^{\frac 1 p}$ is not a norm as it fails to satisfy ...
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How to check whether a given inequality is convex? [on hold]

I have the inequality $$x_1^2+x_2^2\geqslant1$$ How do I check its convexity analytically?
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Basis of convex and concave functions

Let $g(t)$ be a positive function. I'm trying to show that the set $\{ |t|^ne^{\frac{g(t)}{t^n}}\}_{n\in \mathbb{N}}$ is linearly independent in the space of measurable functions on $\mathbb{R}$ (ie: ...
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minimum of sum of strictly convex functions

Is the following statement true? If so, how can I find a proof? Suppose that $f_1$ and $f_2$ are strictly convex functions on a convex set $X \subseteq \mathbb{R}^n$. If $f_1$ and $f_2$ have minimum,...
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Convexity of Certain Functions

Consider the set of functions: \begin{equation} f_n(t) := t^n e^{(\frac{c}{t^n})}, \end{equation} where $c$ is a non-zero real constant. I know that for $n=1$ $f_1(t)$ is convex on $(0,\infty)$ and ...
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Equality case in the Prékopa-Leindler inequality.

in the paper 'Remarks on the conjectured log-Brunn-Minkowski inequality' by C. Saraoglou, the author uses the result (Lemma A. 3.) about the equality case in the Prékopa-Leindler inequality. For the ...
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1answer
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Definitions of intrinsic core of convex set

Let $C$ be a convex subset of a vector space $V$. We consider two definitions of the intrinsic core of $C$. Definition 1. The intrinsic core of $C$ consists all points $c\in C$ such that for every $c^...
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Is the smallest ellipsoid enclosing a convex set unique?

Let $S \subset \mathbb{R}^n$ be a convex set. Assume that it is bounded. We want to find an ellipsoid $E$ of smallest volume such that $S \subset E$. Is $E$ unique?
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Prove that a nest of sets has an empty intersection

Let $f$ be a real convex function and $S$ an arbitrary closed bounded subset of the relative interior of the effective domain of $f$. Let $B$ be a closed Euclidean unit ball. The nest of sets $$(S + \...
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Definition of space by convex function [closed]

It is well know that it is possible to define a space by norm, e.g. lets say that the norm we are concentrating on is L3 norm, thus $C = \{\theta \in \Re^d \mid \| \theta \|_3 \leq 1\}$ where $d \in \...
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Helly theorem application

Let $X$ be a normed space, $dim(X)=d$, let $r>0, A$ and $\subset X$ . Show that if every $d+1$ points of $A$ are contained in a closed ball of radius $r$, then $A$ is contained in a closed ball of ...
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Convex set equals convex functions within optimization?

Can optimizing a convex function subject to convex constraints be written as optimizing the function subject to a convex set? Does the intersection of convex nonlinear ineualities necessarily describe ...
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Convexity versus Strict Convexity

Let $u,v,w\in\mathbb{R}^n$ be three points that are not collinear. We define $$ \triangle(u,v,w):=\{\alpha u+\beta v+\gamma w:\alpha+\beta+\gamma=1, \alpha,\beta,\gamma\geq 0\}, $$ $$ [u,v]=:= \{tu+(...
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Analytical, numerical and graphical approaches to solve convex optimization problems?

I'm wondering if there are analytical approaches to solve these problems(I found these problems in a book by Stephen Boyd): minimize $f_0(x_1,x_2)$ subject to $2x_1+x_2\ge1$ $x_1+3x_2\ge1$ $x_1\...
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18 views

Why are generalized inequalities defined over proper cone?

Why generalized inequality is defined over a proper cone? What property does not hold if we define it over non-convex cone? Same with `pointed'. For example, generalized inequality makes sense in a ...
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Convexity of the composite of convex function by exponential function

Let $\exp : \mathbb{R}^2 \to \mathbb{R}^2$ be the function given by $\exp(x_1,x_2) := (e^{x_1}, e^{x_2})$. Suppose that $f : \mathbb{R}^2 \to \mathbb{R}$ is a smooth (i.e. $\mathcal{C}^2$) convex ...
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Under which conditions discretization of convex\concave function is submodular?

Say, I have $f(x)$ with $x \in [0,1]$, then by discretization I mean $f(x_h)$ with $x_h \in \{0, h, 2h, \dots, 1\}$. I know about Lovasz extension, but it works in other way: given submodular ...
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Measure induced by subgradient of convex functional

I am trying to understand why the following defines a measure. Let $\phi : \mathbb{R}^d \to \mathbb{R}$ be a convex function. Define a measure $\mu$ on $\mathcal{B}(\mathbb{R}^d)$ by $$\mu(E) = \...
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random pursuit without function evaluations

Assume we want to minimize a convex function $f(x)$ with $x\in \mathbb{R}^n$. Function $f(x)$ represents cost of a system which we cannot compute directly but can observe if system is at state $x$. My ...
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Interior of a solid self dual cone

Let $X$ be a real Hilbert space with a solid self dual positive cone $K$, that is, $\mathrm{int}(K)$ is non-empty and $K^{*}=K$. If $X$ is finite dimensional, I know that the $\mathrm{int}(K)$ = $\...
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convexity of a multivariable function

I have a function of the following type: $f(x_1,x_2,...,x_n)$ Each $x_i$ has domain $[0,\infty)$. The function is continuous and differentiable in each variable (It is an expectation of several ...
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Easy interpretation of matrix multiplication with a set

I have just started learning convex optimization. I am having little bit difficulties in some notations. Currently I just encountered the following equation: $$ \boldsymbol{epi}(wf) = \left[ \begin{...
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Properties on proximal term

If the equation $x_i$-subproblem showed below is not strictly convex $\arg \min_{x_i}=f_i(x_i)+\frac{\rho}{2}\|A_ix_i+\sum_{j\neq i}A_jx_j^k-c-\frac{\lambda^k}{\rho}\|_2^2$ Why adding the proximal ...
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Optimization with box constraints - via nonlinear function

I have the following convex optimization problem over $\mathbb{R}^n$ with box constraints: $$\begin{align}\text{minimize }&\;f(x)\\ \text{subject to }&\;x \in [-1,1]^n\end{align}$$ I can see ...
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1answer
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Corollary 2.1 in Ekeland and Temam on lower semicontinuity

Why in Corollary 2.1 on page 10 (see the picture) from Ekeland and Temam book Convex Analysis and Variational Problems there is equality in (2.11), i.e why $$\forall u\in V,\quad \overline F(u)=\...
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Is there a way to measure how (non)convex a function is, maybe analogous to condition number?

Consider the functions $f(x) = \sin x$ and $g(x) = (x+1)^2 (x-1)^2$. We know that $f$ has an infinite number of local minimizers and is nonconvex on a non-compact subset of $R$. We know that $g$ has ...
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Prove that $u$ is upper semicontinuous on $\Delta(0,\rho)$.

Let $u:\Delta(0,\rho)\rightarrow \mathbb{R}$ be a function such that $u(x+iy)$ is convex in $x$ for each fixed $y$, and convex in $y$ for each fixed $x$. Prove that $u$ is subharmonic on $\Delta(0,\...
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Dual polyhedron & dual cone

From Wiki: Def. of dual of polyhedral (polytope): polyhedra are associated into pairs called duals, where the vertices of one correspond to the faces of the other. EX: 2. Def. of dual ...
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Concave optimization on closed unit ball, using penalty function

Background: I want to solve an optimization problem like $$\begin{align*}\text{minimize }&f(x)\\ \text{subject to }&\|x\| \le 1.\end{align*}$$ where $x \in \mathbb{R}^d$, $\|\cdot\|$ is the $...
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Is sum (convex combination) of quadratic function/aggregator quadratic?

We know convex combination of concave/convex functions are concave/convex. While convex combination of two quasi-convex/quasi-concave functions necessarily quasi-convex/quasi-concave. Common ...
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Poof- A function is convex iff it is convex when restricted to a any line that intersects its domain.

When I read the Convex Optimization, Boyd I noticed a statement about determining a function to be convex or not. It is: "A function is convex iff it is convex when restricted to a any line that ...
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show the quadratic function $W(x_1,x_2,\ldots,x_n)=A\sum_{i} x_i^2+ \sum_{i\neq j} x_ix_j$ is quasi-concave

I have a quadratic function $W(x_1,x_2,\ldots,x_n)=A\sum_{i} x_i^2+ \sum_{i\neq j} x_ix_j$, with $x_i$ nonnegative and $A \in[0,1)$. And w.l.o.g. we can normalize $x_i's$ to between 0 and 1. In ...
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How Do I Check Convexity Using The Actual Definition?

Suppose $f:\mathbb{R}^{+}\rightarrow\mathbb{R}$ is defined as follows: \begin{eqnarray} f(u)=\text{sgn}(\rho)\left(u^{\rho+1}-1\right),~u\geq 0, \end{eqnarray} where $\rho\in (-1,\infty)$, $\rho\neq 0$...
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The convex (bi)conjugate and the Fourier transform

In the context of convex optimization, I am looking to find a formula for the convex biconjugate of a function $f: X \rightarrow \mathbb{R}$ where $X$ is a real normed vector space, in terms of its ...
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Find minimal point of convex set

I'm having some convex set $P \subset \mathbb{R}^n_+$ and a linear-time indicator procedure $I_P(x)$ that allows for each given point $x \in \mathbb{R}^n_+$ to say whether it lies inside $P$ or not. ...
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How many times can strictly convex functions intersect?

Some time ago, I saw a post related to the number of times that two convex (and continuous) functions' graphs can meet. In general, infinitely many times: one can think, for instance, of $g(x):=x^{2}$ ...
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A set is affine if and only if its intersection with any line is affine. [duplicate]

How can we prove that the a set is affine if and only if its intersection with any line is affine? In fact, I want to know if there is such theorem that the intersection of two affine set is affine?
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My proof for “$\Gamma =\{X\in \mathbf{R}^{n\times n} \mid X \succeq 0, \text{Tr}(X)=1\}$ is compact”

This problem comes from: How to prove the compactness of the set of Hermitian positive semidefinite matrices In short, we want to prove $$\Gamma =\{X\in \mathbf{R}^{n\times n} \mid X \...
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$\{X \mid \text{trace}(X)=c\}$ is a hyperplane?

A hyperplane is a set of the form: $$\{x\in \mathbf{R}^n \ \ \mid \ \ a^Tx=b, a\in \mathbf{R}^n\}$$ This definition is quite intuitive. However, I am reading some books or paper and they ...
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Convex compact set must have extreme points

I am reading a paper and there is such description as title. Why? I have an example: $(0,1)$. This is a convex set but not closed, so I cannot find an extreme point. However if convex and compact,...
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How to give a formula of the perimeter of a $r$-neighborhood of a smooth set in $2D$?

Let $A$ be a simply connected open set in $\mathbb{R}^2$ with smooth boundary. Define $$A^r := \{x \in \mathbb{R}^2: d(x, A) \le r \},$$ where $d$ is the distance function. Let $P$ denote the ...