Publication

A tutorial on the use of graphs in discrete tomography

Publications associées (30)

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

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

Algorithms For Clustering Problems : Theoretical Guarantees and Empirical Evaluations

Ashkan Norouzi Fard

Clustering is a classic topic in combinatorial optimization and plays a central role in many areas, including data science and machine learning. In this thesis, we first focus on the dynamic facility location problem (i.e., the facility location problem in ...

EPFL2018

When Stuck, Flip a Coin : New Algorithms for Large-Scale Tasks

Slobodan Mitrovic

Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question: How can we design efficient algorithms for large-scale computation? In this thesis, we focus on devising a general strategy to addr ...

EPFL2018

Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

EPFL2018

Novel Algorithms For Clustering

James Peter Newling

Clustering is a method for discovering structure in data, widely used across many scientific disciplines. The two main clustering problems this dissertation considers are K-means and K-medoids. These are NP-hard problems in the number of samples and cluste ...

EPFL2018

Strengths and Limitations of Linear Programming Relaxations

Abbas Bazzi

Many of the currently best-known approximation algorithms for NP-hard optimization problems are based on Linear Programming (LP) and Semi-definite Programming (SDP) relaxations. Given its power, this class of algorithms seems to contain the most favourable ...

EPFL2017

Optimal Software Patching Plan for PMUs

Jean-Yves Le Boudec, Teklemariam Tsegay Tesfay, Ola Nils Anders Svensson

Phasor measurement units (PMUs) deployed to monitor the state of an electrical grid need to be patched from time to time to prevent attacks that exploit vulnerabilities in the software. Applying some of these patches requires a PMU reboot, which takes the ...

2017

Distributed Rate Allocation in Switch-Based Multiparty Videoconferencing System

Pascal Frossard, Sergio Mena, Stefano D'Aronco

Multiparty videoconferences, or more generally multiparty video calls, are gaining a lot of popularity as they offer a rich communication experience. These applications have, however, large requirements in terms of both network and computational resources ...

2017

Combinatorial Algorithm for Restricted Max-Min Fair Allocation

Ola Nils Anders Svensson, Christos Kalaitzis, Chidambaram Annamalai

We study the basic allocation problem of assigning resources to players to maximize fairness. This is one of the few natural problems that enjoys the intriguing status of having a better estimation algorithm than approximation algorithm. Indeed, a certain ...

Association for Computing Machinery2017