Questions tagged [nonlinear-optimization]

Nonlinear objectives, nonlinear constraints, non-convex objective, non-convex feasible region.

Filter by
Sorted by
Tagged with
0
votes
1answer
74 views

How to derive Slime Mould Equations

As shown in https://en.wikiversity.org/wiki/Slime_Mould_Algorithm, we have the Slime Mould equations for Approach food and Wrap food. I do not know how to derive these equations. Please help me or ...
3
votes
1answer
174 views

Minimiser of a certain functional

Let $f_i \in L^1 ([0, 1])$ be a sequence of functions equibounded in $L^1$ norm - that is, there exists some $M > 0$ such that $\|f_i\|_{L^1} < M$. Define the functional $F: L^1([0, 1]) \to \...
1
vote
1answer
97 views

Was a quotient of two norms considered as a constraint to a convex optimization problem before?

I want to solve the optimization problem $$ \text{minimize }g(x) \quad \text{subject to} \quad \Vert x\Vert_{\infty}/\Vert x\Vert_{2} \le s $$ for $x\in\mathbb{R}^d$ and $s\in(0,\infty)$. The function ...
3
votes
1answer
50 views

Numerical scheme for convex optimization

Given $(e_n)_{-N\le n\le N}\in\mathbb R^{2N+1}$ and $-1<x<1$, solve \begin{eqnarray} &&\max_{(q_n)_{-N\le n\le N}\in\mathbb R^{2N+1}_+}~ \sum_{n=-N}^N (e_n-\log(q_n))q_n \\ \mbox{s.t.} &...
0
votes
0answers
26 views

Breaking up an infinite-dimensional optimization problem into a sequence of finite-dimensional problems

My question is a bit vague. I have an infinite-dimensional convex optimization problem and I can solve constrained versions of the problem by restricting the domain of the objective function to a ...
0
votes
0answers
42 views

Chain rule for Clarke's subdifferential

My question is on the validity of the chain rule for Clarke's subdifferential for functions into spaces of dimension larger than $1$. In Clarke's original work (Optimization and nonsmooth analysis) as ...
0
votes
0answers
42 views

Nested, successive minimization solved by asympotic minimization?

I am curious about the general relation between nested, successive minimization (M1) and asymptotic minimization (M2) as defined in the following. What one wants is to implicitly minimize a sequence ...
3
votes
0answers
97 views

An inequality for three iid random variables with a log-concave density

It was previously shown that $$H\ge cG,\tag{1}$$ where $c:=1/14334$, $$G:=E|X-Y|,\quad H:=E|X-Y|-\tfrac12\,E|X+Y-2Z|,$$ and $X,Y,Z$ are independent random variables with the same log-concave density. ...
2
votes
0answers
97 views

Existence and uniqueness of solution of a nonlinear system

I need a proof of the following result to calculate a Nash equilibrium in the Showcase Showdown game. For all $n>1$, the system of equations $$\left\{ \begin{aligned} (1+e^{x}(-1+x))^{n-2}&=\...
0
votes
0answers
31 views

Lipschitz solutions to linear complementarity problems (LCP)

Let $M\in\mathbb{R}^{n\times n}$. For $q\in\mathbb{R}^n$, define the set: $$S_M(q)=\{y\in\mathbb{R}^n|y\ge 0,q+My\ge 0, y^\top (q+My)=0\}.$$ This is the set of solutions to the LCP $(q,M)$. We say $...
0
votes
0answers
44 views

Iterative minimization of a functional $f=f((x(\xi^1,\xi^2, …), \frac{\partial x}{\partial \xi^1 }, \frac{\partial x}{\partial \xi^2 }, …)$

Consider the minimization of a nonlinear functional $f$ of a field $x$ and its partial derivatives, $$ f=f\left(x(\xi^1,\xi^2,\dots,\xi^n), \frac{\partial x}{\partial \xi^1 }, \frac{\partial x}{\...
2
votes
0answers
44 views

Continuum of Lagrange multipliers, duality gap, and minimax theorem

Suppose I have a linear optimization problem involving random variables on some (infinite) probability space $\Omega$. For example, need to maximize expectation $E[Q]$ of random variable $Q$ subject ...
5
votes
2answers
616 views

What is Young measure?

I read about Young measures from the book, Weak convergence methods for nonlinear partial differential equations by L.C. Evans. He introduces the concept by the following theorem: Theorem. Assume ...
1
vote
0answers
40 views

Minimize smooth function $(x,y) \to f(x,y)$ subject to $x \perp y$

Let $V$ be a finite-dimensional real vector space (e.g space of $m \times n$ real matrices equiped with Hilbert-Schmidt inner product $(A,B) \to \mathrm{tr}(AB^\top)$, and let $f:V^2 \to \mathbb R$, $(...
2
votes
0answers
105 views

Optimization of functionals with constraints

I have a minimization problem as follows: $\min\left( \int_0^1\int_0^1\beta(t)\beta(s)G_1(t, s)dtds\right)^{1/2}+\left( \int_0^1\int_0^1\beta(t)\beta(s)G_2(t, s)dtds\right)^{1/2} $ $\texttt{s.t.}\;\;\;...
1
vote
2answers
79 views

Variant of Parthasarathy's minimax theorem

Does there exist a variant of Parthasarathy's minimax theorem [1] that relaxes the assumption that the spaces $X$ and $Y$ are $[0,1]$? [1] https://en.wikipedia.org/wiki/Parthasarathy%27s_theorem
3
votes
1answer
203 views

Maximizing the distance sum of some points inside a circle

Consider $n$ points $\{p_i\}_{i=1}^n$ located inside or on a circle with radius $r$ in the plane. The question is: how to place the $n$ points so that the sum of inter-point distances, $$J=\sum_{i=1}^...
3
votes
1answer
254 views

Eigenvectors of a tensor in $\mathbb{C}^2 \otimes \mathbb{C}^2 \otimes \mathbb{C}^2$

I want to find the critical point of tensor $f=a_0b_0c_0 + a_1b_1c_1$ in $\mathbb{C}^2 \otimes \mathbb{C}^2 \otimes \mathbb{C}^2$, and I followed this construction: First, I take the following partial ...
1
vote
0answers
22 views

Are such assumptions of functions similar to strong convexity reasonable in convex optimization?

For $\mu$-strongly convex function $f:\mathbb{R}^d\to\mathbb{R}$, the following property holds: for any given $x,y\in\mathbb{R}^d$, we have $$ (\nabla f(x) - \nabla f(y))^\top(x-y) \ge \mu \|x-y\|^2.$$...
2
votes
0answers
105 views

Condition under which the Clarke's subdifferential is locally Lipschitzian

Given a locally Lipschitz continuous function $f: \mathbb{R}^{n} \rightarrow \mathbb{R}$ and closed set $$\Omega =\left\lbrace x \in\mathbb{R}^n \ |\ f(x) \leq 0 \right\rbrace$$ such that f is semi-...
4
votes
1answer
69 views

Do Pareto critical points of a multicriteria optimization problem form an attractor of the dynamical system induced by a descent algorithm?

Let $d\in\mathbb N$, $k\in\mathbb N$ and $f:\mathbb R^d\to\mathbb R^k$ be differentiable. Say that $v\in\mathbb R^d$ is a descent direction at $x\in\mathbb R^d$ if ${\rm D}f(x)v<0$ (component-wise) ...
1
vote
1answer
96 views

Calculus of variations for double sum with Lagrange multiplier

This cropped up in a research question I'm tackling. I wish to solve the following optimization problem: $$ \text{minimize}\ \sum_{i=1}^\infty f_i \sum_{j=1}^i \sqrt{f_j} \quad\text{subject to}\ \sum_{...
0
votes
1answer
107 views

What is the computational complexity of the calculation of $ \Psi(x) $?

What is the computational complexity of the calculation of $ \Psi(x) $ described below: Let $\left\{ f_i : \{0,1,\dots,m\} \to \mathbb{R} \right\}_{i=1}^n$. For each $x \in \{0,1,\dots,m\}$ we ...
2
votes
1answer
67 views

Does this non-negative function, with No stationary points, have only descend directions close to a constraint set?

Suppose $P: \mathbb{R}^n \rightarrow \mathbb{R}_{\ge 0} $ is differentiable map, with $P(x) = 0 \ \forall x \in \mathcal{X}$ and $P(x) > 0 \ \forall x \in \mathcal{X}^c$. Further, suppose $P$ has ...
1
vote
1answer
166 views

How to solve a system of nonlinear equation, with y known and x or its coefficients unknown? [closed]

While solving a complex problem I have ended up with this simplified problem: There are eight straight lines in the plane. They are notated as follows: \begin{gather} \tag{1} \label{1} y=k_1 x+b_1\\ y=...
0
votes
0answers
25 views

Norm of vector components optimization of linear matrix combination

Given complex matrices $A_1, A_2, \dots, A_k\in\mathbb{C}^{m \times n}$, $B \in\mathbb{R}^{m \times n}$, the objective is to find a vector $x \in \mathbb{C}^k$ such that: $\max {||x_i||}$ , $i\in 1,2.....
0
votes
1answer
118 views

What to call a function that is negative on a set

Let $Y$ be a nonempty region in $\mathbb{R}^n$. I am designing an algorithm which given a point $x_0$ outside $Y$ in a finite number of steps lead to a point $x_n∈ Y$. The way I do it is that I have a ...
2
votes
0answers
62 views

Monotone rearrangements of function, constrained optimization in $\mathcal{L}^p$

By $\mathcal{S}$ let us denote the set of such step functions $f:[0,1]\to [0,1]$ that additionally satisfy: $$\forall_{ x>\frac{1}{2}} \ \ \lambda\Big(f=x\Big) \ = \ x\cdot \Big[\lambda\Big(f=x\Big)...
4
votes
0answers
74 views

Minimizing the largest eigenvalue of matrix product

Let $A\in\mathbb{C}^{m\times n}$, $B\in\mathbb{C}^{n\times k}$, $C\in\mathbb{C}^{k\times m}$ be given complex matrices. The objective of the optimization problem is \begin{equation} \mathop {\arg \min ...
3
votes
2answers
220 views

On some inequality (upper bound) on a function of two variables

There is a problem (of physical origin) which needs an analytical solution or a hint. Let us consider the following real-valued function of two variables $y (t,a) = 4 \left(1 + \frac{t}{x(t,a)}\right)...
0
votes
0answers
32 views

Numerically finding matrix approximation by lower-dimensional “pseudo-similar” matrix

Consider an $N\times N$ (real or complex) matrix $A$, and some $n<N$. Is there a good numerical algorithm that finds the set consisting of an $n\times n$ matrix $B$, an $n\times N$ matrix $I$, and ...
2
votes
0answers
60 views

Global minimum of sum of a non-convex and convex function, where minima of the non-convex function can be found

I'm interested in finding $\arg\min_{x \in X} (f(x) + \lVert x\rVert_2^2)$ where $X$ is a $[0,1]^n$, $f$ is Lipschitz but non-convex and we already have a procedure to find some $x^* \in \arg\min_{x\...
1
vote
1answer
110 views

Example of a differentiable function optimization where derivative free methods are used

While preparing a workshop on the derivative free methods, and fminsearch in MATLAB, I found an example function where fminsearch converges better and in less iterations than fmincon with calculated ...
0
votes
1answer
74 views

Gradient-descent “type” Methods for non-convex and non-smooth functions

Most (stochastic) "gradient descent" type algorithms (such as Nesterov-accelerated gradient-descent or ADAM) seem to be well-defined only for functions which are either: lower semi-...
1
vote
0answers
57 views

Taut string algorithm and TV-minimization equivalence

Given real numbers $y_i's$, consider the following convex optimization problem: $$ \min_{x_i's} \sum_{i=1}^N(y_i-x_i)^2 + \lambda\sum_{i=1}^{N-1}|x_{i+1}-x_{i}|. $$ The paper A Direct Algorithm for 1D ...
-1
votes
1answer
58 views

Existence of continuous selection for metric projection

Let $(X,d)$ be a separable complete geodesic metric space and let $K$ be a compact (non-empty) subset of $X$. Without assuming things like linearity, the convexity of $K$, and locally convexity, ...
0
votes
0answers
39 views

A parametrized saddle point problem with linear constraints

I am struggling to find any potential algorithm for solving a saddle point problem. More precisely let $\mathcal{P}=\{ \mathbf{x}\in \mathbb{R}^{d}; \mathbf{A}\mathbf{x}=\mathbf{b}, \mathbf{x} \geq 0\}...
2
votes
1answer
94 views

Normal cones and KKT conditions

I'm trying to understand a statement from the book "Perturbation Analysis of Optimization Problems", by Bonnans and Shapiro. Let me start by providing some context. In page 148, the authors ...
3
votes
1answer
103 views

KKT conditions of problem with variational inequality constraint

I have an optimization problem with a variational inequality constraint: $$ \begin{equation} \begin{array}{ll} \min_x & f(x) \\ \mathrm{s.t.} & g_i(x) \leq 0, \quad i=1,\ldots,m \\ & h_j(...
0
votes
0answers
20 views

Distributed optimization - expectation of a product

I've been trying to find distributed optimization algorithms for solving a problem of the form: $$ \min_x \mathbb{E}\left[f_1(x) \cdot f_2(x) \cdot \ldots \cdot f_N(x)\right], $$ where each agent only ...
4
votes
1answer
127 views

Nonlinear system of integral equations

I have encountered a system of nonlinear integral equations in my work. They take the form $$\int_{0}^{1} \frac{1}{g(y)}e^{f(x)/g(y)}(x+f(x)/g(y)-f(x))dy=0$$ $$\int_{0}^{1}\frac{f(x)}{g(y)^2} e^{f(x)/...
2
votes
0answers
54 views

Minimizing a certain norm of the identity operator on $\mathbb R^2$

$\newcommand\R{\mathbb R}\newcommand\Q{\mathcal Q}$For mutually orthogonal vectors unit vectors $a=[a_1,\dots,a_n]^T$ and $b=[b_1,\dots,b_n]^T$ in $\R^n=\R^{n\times1}$ (so that $n\ge2$) and for all $x=...
5
votes
1answer
218 views

On a certain norm of the identity operator on $\mathbb R^2$

$\newcommand\R{\mathbb R}\newcommand\Q{\mathcal Q}$For mutually orthogonal vectors unit vectors $a=[a_1,\dots,a_n]^T$ and $b=[b_1,\dots,b_n]^T$ in $\R^n=\R^{n\times1}$ (so that $n\ge2$) and for all $x=...
1
vote
2answers
117 views

Robust estimation of $Ax=b$

Problem setting : $ \underset{x}{\text{min}} \|Ax-b\|$, where $A \in \mathcal{R}^{m \times n}, m\gg n $, full rank. L1 loss is used for robust estimation using IRLS. The corresponding equation to ...
2
votes
0answers
45 views

Which algorithm to optimize this problem?

I do need to find coefficients of a parametric model given observations, and I was wondering which algorithm I should use. The problem is as follows. I have a set of values $\mathbf x_i = (x_{i,1},\...
0
votes
0answers
30 views

How to derive Lipschitz constant of Moreau envelope of a Lipschitz function

This question is from a lecture note of convex optimization. Q: Prove: If $f$ is $L$-Lipschitz, then its Moreau envelope $f_{\mu}$ is also $L$-Lipschitz. ($L = \mu^{-1}$) [NOT my homework] $$f_{\mu}(x)...
2
votes
0answers
39 views

A question about strong slopes (nonsmooth analysis)

Context. I'm reading the manuscrip "Nonlinear Error Bounds via a Change of Function" by Dominique Azé and Jean-Noël Corvellec (J Optim Theory Appl 2016), and I'm having a hard time ...
0
votes
0answers
49 views

Convex optimization under asymmetric loss in infinite dimensional space

The following problem is common in financial economics $$ \min_{m \in L^2} \mathbb{E}[ \phi(y(\theta)-m)] \quad \text{s.t. } \mathbb{E}[ mx ]= q $$ That is, given a random variable $y(\theta)$ ($\...
1
vote
0answers
21 views

Maximizing the volume of the intersection of a fixed ball with a cube with varying width and location

Given a ball $B$ and a linear subspace $L$ in $\mathbb{R}^n$, what is the maximum value of $\frac{vol(B \cap C)}{vol(C)}$ where $C$ is a cube of the form $x + [0, h]^n$ for $x \in L$ and $h \in \...
0
votes
0answers
27 views

Optimality of a simple solution of a linear fractional minimax problem

Consider the following linear fractional optimization problem \begin{align} \max_\mathbf{x}&\quad \min_{n=1,\ldots,N}\frac{x_n}{\alpha+\sum_{m}\beta_m^{(n)}x_m}\\ \text{subject to}&\quad\...

1
2 3 4 5
10