2019 - IEEE John von Neumann Medal “For contributions to the field of algorithms, including foundational new methods in optimization, approximation algorithms, and algorithmic game theory.”
2013 - Fellow of the American Mathematical Society
2013 - Member of the National Academy of Sciences
2009 - SIAM Fellow For the design and analysis of graph and network algorithms.
2007 - Member of the National Academy of Engineering For contributions to the design and analysis of efficient algorithms for network problems.
2006 - Dantzig Prize, by the Society for Industrial and Applied Mathematics (SIAM) and the Mathematical Optimization Society (MOS)
2005 - Fellow of the Institute for Operations Research and the Management Sciences (INFORMS)
2001 - Fellow of the American Academy of Arts and Sciences
1999 - Fellow of John Simon Guggenheim Memorial Foundation
1998 - ACM Fellow For fundamental contributions in the design and analysis of algorithms, combinatorial optimization, network flows, and approximation algorithms.
1991 - Fellow of Alfred P. Sloan Foundation
Her primary scientific interests are in Approximation algorithm, Mathematical optimization, Algorithm, Network planning and design and Theoretical computer science. She interconnects Discrete mathematics, Social network analysis, Optimization problem and Centrality in the investigation of issues within Approximation algorithm. Éva Tardos studies Linear programming which is a part of Mathematical optimization.
Her Algorithm research includes elements of Facility location problem, Lagrangian relaxation, Polynomial and Total cost. Her Network planning and design study combines topics from a wide range of disciplines, such as Shapley value, Game theory, Technical report and Nash equilibrium. Her Theoretical computer science research integrates issues from Algorithmic game theory, Algorithm engineering, Algorithmics, Game complexity and Computational geometry.
Éva Tardos focuses on Approximation algorithm, Mathematical optimization, Combinatorics, Mathematical economics and Price of anarchy. In her research on the topic of Approximation algorithm, Submodular set function is strongly related with Optimization problem. Her study looks at the relationship between Mathematical optimization and topics such as Algorithm, which overlap with Simple, Maximum flow problem and Polynomial.
Her Combinatorics study combines topics in areas such as Discrete mathematics, Linear programming and Combinatorial optimization. Her research investigates the connection between Mathematical economics and topics such as Common value auction that intersect with issues in Greedy algorithm. While the research belongs to areas of Algorithmic game theory, Éva Tardos spends her time largely on the problem of Algorithmics, intersecting her research to questions surrounding Theoretical computer science and Game complexity.
Her main research concerns Mathematical economics, Price of anarchy, Mathematical optimization, Regret and Common value auction. Her studies deal with areas such as Generalized second-price auction and Market clearing as well as Mathematical economics. Éva Tardos interconnects Algorithm and Algorithmic game theory in the investigation of issues within Price of anarchy.
Éva Tardos works in the field of Mathematical optimization, namely Submodular set function. Her Submodular set function research incorporates themes from Social network analysis, Viral marketing, Social network, Centrality and Heuristics. As part of one scientific family, Éva Tardos deals mainly with the area of Common value auction, narrowing it down to issues related to the Greedy algorithm, and often Approximation algorithm, Inefficiency and Cardinality.
Éva Tardos mainly focuses on Mathematical economics, Nash equilibrium, Price of anarchy, Generalized second-price auction and Repeated game. Her Mathematical economics research includes themes of Class, Vickrey auction and Common value auction. Her Nash equilibrium study deals with Bounding overwatch intersecting with Potential game, Congestion game and Network planning and design.
Within one scientific family, she focuses on topics pertaining to Regret under Repeated game, and may sometimes address concerns connected to Discrete mathematics. In her articles, she combines various disciplines, including High probability and Mathematical optimization. Her Mathematical optimization course of study focuses on Payment and Mechanism design.
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Maximizing the Spread of Influence through a Social Network
David Kempe;Jon M. Kleinberg;Éva Tardos.
Theory of Computing (2015)
Jon Kleinberg;Eva Tardos.
How bad is selfish routing
Tim Roughgarden;Éva Tardos.
Journal of the ACM (2002)
Algorithmic Game Theory: Computing in Games
Noam Nisan;Tim Roughgarden;Eva Tardos;Vijay V. Vazirani.
Algorithmic Game Theory: Quantifying the Inefficiency of Equilibria
Noam Nisan;Tim Roughgarden;Eva Tardos;Vijay V. Vazirani.
Approximation algorithms for scheduling unrelated parallel machines
J. K. Lenstra;D. B. Shmoys;É. Tardos.
Mathematical Programming (1990)
Influential nodes in a diffusion model for social networks
David Kempe;Jon Kleinberg;Éva Tardos.
international colloquium on automata languages and programming (2005)
The Price of Stability for Network Design with Fair Cost Allocation
Elliot Anshelevich;Anirban Dasgupta;Jon Kleinberg;Éva Tardos.
SIAM Journal on Computing (2008)
Fast approximation algorithms for fractional packing and covering problems
Serge A. Plotkin;David B. Shmoys;Éva Tardos.
Mathematics of Operations Research (1995)
An approximation algorithm for the generalized assignment problem
David B. Shmoys;Éva Tardos.
Mathematical Programming (1993)
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