Concept

Problème de flot maximum

Publications associées (31)

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

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

Results on Sparse Integer Programming and Geometric Independent Sets

Jana Tabea Cslovjecsek

An integer linear program is a problem of the form max{c^T x : Ax=b, x >= 0, x integer}, where A is in Z^(n x m), b in Z^m, and c in Z^n. Solving an integer linear program is NP-hard in general, but there are several assumptions for which it becomes fixed ...

EPFL2023

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

Maximum Independent Set: Self-Training through Dynamic Programming

Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

2023

Discovering Influencers in Opinion Formation Over Social Graphs

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia, Mert Cemri

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography

Nikolaos Stergiopoulos, Georgios Rovas, Vasiliki Bikia

Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomecha ...

MDPI2023

Streaming and Matching Problems with Submodular Functions

Paritosh Garg

Submodular functions are a widely studied topic in theoretical computer science. They have found several applications both theoretical and practical in the fields of economics, combinatorial optimization and machine learning. More recently, there have also ...

EPFL2022

Spectral Hypergraph Sparsifiers of Nearly Linear Size

Mikhail Kapralov, Jakab Tardos

Graph sparsification has been studied extensively over the past two decades, culminating in spectral sparsifiers of optimal size (up to constant factors). Spectral hypergraph sparsification is a natural analogue of this problem, for which optimal bounds on ...

IEEE COMPUTER SOC2022

Space-Efficient Representations of Graphs

Jakab Tardos

With the increasing prevalence of massive datasets, it becomes important to design algorithmic techniques for dealing with scenarios where the input to be processed does not fit in the memory of a single machine. Many highly successful approaches have emer ...

EPFL2022