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Optimal Transport has received much attention in Machine Learning as it allows to compare probability distributions by exploiting the geometry of the underlying space. However, in its original formulation, solving this problem suffers from…

Machine Learning · Computer Science 2023-11-27 Clément Bonet

A surface M is called p-minimal if one of the coordinate functions is p-harmonic in the inner metric. We show that in the twodimensional case the Gaussian map of such surfaces is quasiconformal. In the case when the surface is a tube we…

Differential Geometry · Mathematics 2018-04-20 Vladimir G. Tkachev

Optimization is a critical tool for addressing a broad range of human and technical problems. However, the paradox of advanced optimization techniques is that they have maximum utility for problems in which the relationship between the…

Computational Engineering, Finance, and Science · Computer Science 2024-03-04 Hazhir Aliahmadi , Ruben Perez , Greg van Anders

We consider the problem of optimizing the product of the distances from a given point in a triangle to each vertex. There are two possible cases in general. For isosceles triangles, we explicitly show exactly when both cases occur.

Metric Geometry · Mathematics 2026-05-14 Tommy Murphy , Kevin Tran

We consider the problem of minimizing a function, which is the sum of a linear function and a composition of a strongly convex function with a linear transformation, over a compact polyhedral set. Jaggi and Lacoste-Julien [14] showed that…

Optimization and Control · Mathematics 2015-04-21 Amir Beck , Shimrit Shtern

We present a powerful and easy-to-implement algorithm for solving constrained optimization problems that involve $L_1$/total-variation regularization terms, and both equality and inequality constraints. We discuss the relationship of our…

Optimization and Control · Mathematics 2015-05-22 Musa Maharramov , Stewart A. Levin

Several optimization schemes have been known for convex optimization problems. However, numerical algorithms for solving nonconvex optimization problems are still underdeveloped. A progress to go beyond convexity was made by considering the…

Optimization and Control · Mathematics 2015-06-29 Nguyen Thai An , Nguyen Mau Nam

We consider the following variant of the Monge-Kantorovich transportation problem. Let S be a finite set of point sites in d dimensions. A bounded set C in d-dimensional space is to be distributed among the sites p in S such that (i) each p…

Metric Geometry · Mathematics 2015-02-18 Darius Geiß , Rolf Klein , Rainer Penninger , Günter Rote

We consider the multi-objective optimization problem of choosing the bottom left block-entry of a block lower triangular matrix to minimize the ranks of all block sub-matrices. We provide a proof that there exists a simultaneous…

Optimization and Control · Mathematics 2021-06-22 Ethan N. Epperly , Nithin Govindarajan , Shivkumar Chandrasekaran

In this paper, we introduce the notions of proximally completeness, proximally closedness and proximally continuity and utilize the same to prove a result on existence and uniqueness of best proximity points in the setting of metric space…

Functional Analysis · Mathematics 2024-05-07 Aftab Alam

We establish new results concerning projectors on max-plus spaces, as well as separating half-spaces, and derive an explicit formula for the distance in Hilbert's projective metric between a point and a half-space over the max-plus…

Metric Geometry · Mathematics 2011-11-08 Marianne Akian , Stephane Gaubert , Viorel Nitica , Ivan Singer

We obtain the ratio of semiclassical partition functions for the extension under mixed flux of the minimal surfaces subtending a circumference and a line in Euclidean $AdS_{3}\times S^{3}\times T^{4}$. We reduce the problem to the…

High Energy Physics - Theory · Physics 2020-03-11 Rafael Hernandez , Juan Miguel Nieto , Roberto Ruiz

An extension of the Gauss-Newton algorithm is proposed to find local minimizers of penalized nonlinear least squares problems, under generalized Lipschitz assumptions. Convergence results of local type are obtained, as well as an estimate…

Optimization and Control · Mathematics 2011-03-03 Saverio Salzo , Silvia Villa

We provide a simple proof of the radial symmetry of any nonnegative minimizer for a general class of quasi-linear minimization problems

Functional Analysis · Mathematics 2010-04-21 H. Hjaiej , M. Squassina

In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables that they require to contribute similarly to the final result. The output allows…

Machine Learning · Computer Science 2024-07-17 Roberto Esposito , Mattia Cerrato , Marco Locatelli

The paper explores the differential inclusion of a special form. It is supposed that the support function of the set in the right-hand side of an inclusion may contain the sum of the maximum and the minimum of the finite number of…

Optimization and Control · Mathematics 2025-02-05 Alexander Fominyh

Let $G$ be a graph with maximum degree $\Delta(G)$ and maximum multiplicity $\mu(G)$. Vizing and Gupta, independently, proved in the 1960s that the chromatic index of $G$ is at most $\Delta(G)+\mu(G)$. The distance between two edges $e$ and…

Combinatorics · Mathematics 2022-04-05 Yan Cao , Guantao Chen , Guangming Jing , Xuli Qi , Songling Shan

The problem of minimizing convex functionals of probability distributions is solved under the assumption that the density of every distribution is bounded from above and below. A system of sufficient and necessary first-order optimality…

Information Theory · Computer Science 2018-12-05 Michael Fauss , Abdelhak M. Zoubir

Given a road network modelled as a planar straight-line graph $G=(V,E)$ with $|V|=n$, let $(u,v)\in V\times V$, the shortest path (distance) between $u,v$ is denoted as $\delta_G(u,v)$. Let $\delta(G)=\max_{(u,v)}\delta_G(u,v)$, for…

Computational Geometry · Computer Science 2024-05-01 Zhidan Feng , Henning Fernau , Binhai Zhu

We study the min-max optimization problem where each function contributing to the max operation is strongly-convex and smooth with bounded gradient in the search domain. By smoothing the max operator, we show the ability to achieve an…

Optimization and Control · Mathematics 2019-05-31 Hakan Gokcesu , Kaan Gokcesu , Suleyman Serdar Kozat
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