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This article develops a duality principle for a class of optimization problems in $\mathbb{R}^n$. The results are obtained based on standard tools of convex analysis and on a well known result of Toland for D.C. optimization. Global…

Optimization and Control · Mathematics 2019-04-02 Fabio Botelho

Many problems in geometric optics or convex geometry can be recast as optimal transport problems: this includes the far-field reflector problem, Alexandrov's curvature prescription problem, etc. A popular way to solve these problems…

Numerical Analysis · Mathematics 2017-03-08 Jun Kitagawa , Quentin Mérigot , Boris Thibert

Novel nonlinear damping control is proposed for the second-order systems. The proportional output feedback is combined with the damping term which is quadratic to the output derivative and inverse to the set-point distance. The global…

Systems and Control · Electrical Eng. & Systems 2020-11-30 Michael Ruderman

We present a new approach to solve a Schr\"odinger Equation autonomous at infinity, by identifying the relation between the arrangement of the spectrum of the concerned operator and the behavior of the nonlinearity at zero and at infinity.…

Analysis of PDEs · Mathematics 2019-09-23 Mayra Soares , Liliane A. Maia

In this work we study a modification of the Monge-Kantorovich problem taking into account path dependence and interaction effects between particles. We prove existence of solutions under mild conditions on the data, and after imposing…

Analysis of PDEs · Mathematics 2022-04-19 Rene Cabrera

Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertainty set or are governed by any probability distribution from…

Optimization and Control · Mathematics 2023-07-21 Jianzhe Zhen , Daniel Kuhn , Wolfram Wiesemann

In this work, we construct a novel numerical method for solving the multi-marginal optimal transport problems with Coulomb cost. This type of optimal transport problems arises in quantum physics and plays an important role in understanding…

Optimization and Control · Mathematics 2023-06-16 Yukuan Hu , Huajie Chen , Xin Liu

We consider nonlinear Schrodinger equations with either local or nonlocal nonlinearities. In addition, we include periodic potentials as used, for example, in matter wave experiments in optical lattices. By considering the corresponding…

Mathematical Physics · Physics 2012-06-08 Rémi Carles , Christof Sparber

On a smooth, closed Riemannian manifold, we study the question of proportionality of components, also called synchronization, of vector-valued solutions to nonlinear elliptic Schr\"odinger systems with constant coefficients. In particular,…

Analysis of PDEs · Mathematics 2024-02-23 Saikat Mazumdar , Jérôme Vétois

The main purpose of this research note is to show that the triality theory can always be used to identify both global minimizer and the biggest local maximizer in global optimization. An open problem left on the double-min duality is solved…

Mathematical Physics · Physics 2011-10-04 Daniel Morales-Silva , David Yang Gao

We propose and study a system of Schr\"odinger's problems and functional equations in probability theory. More precisely, we consider a system of variational problems of relative entropies for probability measures on a Euclidean space with…

Probability · Mathematics 2025-06-17 Toshio Mikami , Jin Feng

We consider the modified Monge-Kantorovich problem with additional restriction: admissible transport plans must vanish on some fixed functional subspace. Different choice of the subspace leads to different additional properties optimal…

Functional Analysis · Mathematics 2014-04-22 Danila Zaev

Motivated by the grid search method and Bayesian optimization, we introduce the concept of contractibility and its applications in model-based optimization. First, a basic framework of contraction methods is established to construct a…

Optimization and Control · Mathematics 2021-08-24 Xiaopeng Luo , Xin Xu

The majorization-minimization (MM) principle is an extremely general framework for deriving optimization algorithms. It includes the expectation-maximization (EM) algorithm, proximal gradient algorithm, concave-convex procedure, quadratic…

Optimization and Control · Mathematics 2021-06-08 Kenneth Lange , Joong-Ho Won , Alfonso Landeros , Hua Zhou

Turnpike properties have been established long time ago in finite-dimensional optimal control problems arising in econometry. They refer to the fact that, under quite general assumptions, the optimal solutions of a given optimal control…

Optimization and Control · Mathematics 2014-02-14 Emmanuel Trélat , Enrique Zuazua

We present a novel method for mixed-integer optimization problems with multivariate and Lipschitz continuous nonlinearities. In particular, we do not assume that the nonlinear constraints are explicitly given but that we can only evaluate…

Optimization and Control · Mathematics 2023-03-22 Julia Grübel , Richard Krug , Martin Schmidt , Winnifried Wollner

Optimal mass transport, also known as the earth mover's problem, is an optimization problem with important applications in various disciplines, including economics, probability theory, fluid dynamics, cosmology and geophysics to cite a few.…

Numerical Analysis · Mathematics 2022-06-28 Said Kerrache , Yasushi Nakauchi

We investigate qualitative properties of positive singular solutions of some elliptic systems in bounded and unbounded domains. We deduce symmetry and monotonicity properties via the moving plane procedure. Moreover, in the unbounded case,…

Analysis of PDEs · Mathematics 2019-07-16 Francesco Esposito

A variant of the classical optimal transportation problem is: among all joint measures with fixed marginals and which are dominated by a given density, find the optimal one. Existence and uniqueness of solutions to this variant were…

Optimization and Control · Mathematics 2018-01-23 Jonathan Korman , Robert J. McCann

We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the…

Machine Learning · Statistics 2025-03-31 Michael Unser , Alexis Goujon , Stanislas Ducotterd
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