M
Madhav V. Marathe
Researcher at University of Virginia
Publications - 356
Citations - 15017
Madhav V. Marathe is an academic researcher from University of Virginia. The author has contributed to research in topics: Approximation algorithm & Computer science. The author has an hindex of 53, co-authored 315 publications receiving 13493 citations. Previous affiliations of Madhav V. Marathe include University at Albany, SUNY & Los Alamos National Laboratory.
Papers
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Journal ArticleDOI
Modifying edges of a network to obtain short subgraphs
TL;DR: The first polynomial time approximation algorithms for the problem of developing a reduction strategy satisfying the budget constraint are presented, where the cost functions c e are allowed to be taken from a broad class of functions.
Book ChapterDOI
Topology Control Problems under Symmetric and Asymmetric Power Thresholds
TL;DR: This work considers topology control problems where the goal is to assign transmission powers to the nodes of an ad hoc network so as to induce graphs satisfying specific properties, and presents results under both symmetric and asymmetric power threshold models.
Book ChapterDOI
A Unified Approach to Approximation Schemes for NP- and PSPACE-Hard Problems for Geometric Graphs
Harry B. Hunt,Madhav V. Marathe,Venkatesh Radhakrishnan,Venkatesh Radhakrishnan,S. S. Ravi,Daniel J. Rosenkrantz,Richard Edwin Stearns +6 more
TL;DR: The authors' approximation schemes for hierarchically specified unit disk graphs along with the results in [MHSR94] are the first approximation schemes in the literature for natural PSPACE-hard optimization problems.
Journal ArticleDOI
Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches
John S. Brownstein,John S. Brownstein,Shuyu Chu,Achla Marathe,Madhav V. Marathe,Andre T. Nguyen,Andre T. Nguyen,Daniela Paolotti,Nicola Perra,Daniela Perrotta,Mauricio Santillana,Mauricio Santillana,Samarth Swarup,Michele Tizzoni,Alessandro Vespignani,Anil Vullikanti,Mandy L. Wilson,Qian Zhang +17 more
TL;DR: The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks.
Book ChapterDOI
Formal Language Constrained Path Problems
TL;DR: Given an alphabet σ, a (directed) graph G whose edges are weighted and σ-labeled, and a formal language L \(\subseteq\) σ*, this work considers the problem of finding a shortest (simple) path p in G complying with the additional constraint that l(p) ∃ L.