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Concept

Théorème flot-max/coupe-min

Publications associées (26)

The Strong Integral Input-to-State Stability Property in Dynamical Flow Networks

Nils Gustav Nilsson

Dynamical flow networks serve as macroscopic models for, e.g., transportation networks, queuing networks, and distribution networks. While the flow dynamics in such networks follow the conservation of mass on the links, the outflow from each link is often ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Equilibria in Network Constrained Energy Markets

Leonardo Massai

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

Elsevier2023

Better Trees for Santa Claus

Etienne Michel François Bamas, Lars Rohwedder

We revisit the problem max-min degree arborescence, which was introduced by Bateni et al. [STOC'09] as a central special case of the general Santa Claus problem, which constitutes a notorious open question in approximation algorithms. In the former problem ...

Assoc Computing Machinery2023

Flow Time Scheduling and Prefix Beck-Fiala

Ola Nils Anders Svensson, Lars Rohwedder

We relate discrepancy theory with the classic scheduling problems of minimizing max flow time and total flow time on unrelated machines. Specifically, we give a general reduction that allows us to transfer discrepancy bounds in the prefix Beck-Fiala (bound ...

ASSOC COMPUTING MACHINERY2022

From coating flow patterns to porous body wake dynamics via multiscale models

Pier Giuseppe Ledda

Multiscale phenomena are involved in countless problems in fluid mechanics. Coating flows are known to exhibit a broad variety of patterns, such as wine tears in a glass and dripping of fresh paint applied on a wall. Coating flows are typically modeled und ...

EPFL2022

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Ahmet Alacaoglu

Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...

EPFL2021

Estimating an extreme Bayesian network via scalings

Mario Krali

A recursive max-linear vector models causal dependence between its components by expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph and some exogenous innovation. Motivated by extreme value theory, inno ...

ELSEVIER INC2021

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Volkan Cevher, Shaul Nadav Hallak

This paper develops a methodology for regret minimization with stochastic first-order oracle feedback in online, constrained, non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, and ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

CFD simulations of a Y-junction for the implementation of hydraulic short-circuit operating mode

François Avellan, Cécile Münch-Alligné, Daniel Biner

In the framework of the XFLEX HYDRO H2020 European Project, one of the demonstrators focuses on the implementation of the hydraulic short-circuit on the pump storage power plant of Grand-Maison owned by Electricite De France (EDF). The Grand-Maison power p ...

IOP Publishing2021

Local approximation of the Maximum Cut in regular graphs

Etienne Michel François Bamas

This paper is devoted to the distributed complexity of finding an approximation of the maximum cut (MAXCUT) in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communica ...

ELSEVIER2020

Memory Efficient Max Flow for Multi-Label Submodular MRFs

Mathieu Salzmann

Multi-label submodular Markov Random Fields (MRFs) have been shown to be solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable X-i is represented by l nodes (where l is the number of labels) arranged in a c ...

2019

An Optimal Space Lower Bound for Approximating MAX-CUT

Mikhail Kapralov

We consider the problem of estimating the value of MAX-CUT in a graph in the streaming model of computation. At one extreme, there is a trivial 2-approximation for this problem that uses only O(log n) space, namely, count the number of edges and output hal ...

ASSOC COMPUTING MACHINERY2019

Numerical Approximation Of Orthogonal Maps

Marco Picasso, Alexandre Caboussat, Dimitrios Gourzoulidis

Orthogonal maps are the solutions of the mathematical model of paper-folding, also called the origami problem. They consist of a system of first-order fully nonlinear equations involving the gradient of the solution. The Dirichlet problem for orthogonal ma ...

2019

Local Approximation of the Maximum Cut in Regular Graphs

Etienne Michel François Bamas

This paper is devoted to the distributed complexity of finding an approximation of the maximum cut in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communication and ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Ola Nils Anders Svensson

A sparsifier of a graph G (Bencztir and Karger; Spielman and Teng) is a sparse weighted subgraph (G) over tilde that approximately retains the same cut structure of G. For general graphs, non-trivial sparsification is possible only by using weighted graphs ...

IEEE COMPUTER SOC2019

Efficiently Learning Fourier Sparse Set Functions

Andreas Krause, Mikhail Kapralov, Amir Zandieh

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Artificial floods in residual flow reaches – How do they affect hydraulic habitats

Severin Stähly

Floodplains in hydropower-controlled rivers, where a constant residual flow discharge is imposed, often lack sediment supply and periodic inundation due to the absence of natural flood events. In this study we compare two residual flow systems. The first r ...

2019

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

Damian Mateusz Straszak

Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most s ...

EPFL2018

Testing Graph Clusterability: Algorithms and Lower Bounds

Mikhail Kapralov, Aidasadat Mousavifar, Ashish Hari Chiplunkar

We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generali ...

IEEE COMPUTER SOC2018

GEOTEXTILE

Lyesse Laloui, Dimitrios Terzis

The present invention concerns a geosynthetic element (1) for a geotechnical engineering application. The geosynthetic element (1) comprises: bacteria carriers (3) for carrying bacteria arranged to be propagated from the geosynthetic element (1) to a surro ...

2018

Publications associées (26)