M
MohammadTaghi Hajiaghayi
Researcher at University of Maryland, College Park
Publications - 404
Citations - 12400
MohammadTaghi Hajiaghayi is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Approximation algorithm & 1-planar graph. The author has an hindex of 57, co-authored 377 publications receiving 11276 citations. Previous affiliations of MohammadTaghi Hajiaghayi include Massachusetts Institute of Technology & Indian Institutes of Technology.
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Journal ArticleDOI
From Duels to Battlefields: Computing Equilibria of Blotto and Other Games
AmirMahdi Ahmadinejad,Sina Dehghani,MohammadTaghi Hajiaghayi,Brendan Lucier,Hamid Mahini,Saeed Seddighin +5 more
TL;DR: The approach provably extends the class of dueling games for which equilibria can be computed, and introduces a new dueling game, the matching duel, on which prior methods fail to be computationally feasible but upon which the authors' reduction can be applied.
Proceedings ArticleDOI
Scheduling to minimize staleness and stretch in real-time data warehouses
TL;DR: It is proved that any online nonpreemptive algorithm, no processor of which is ever voluntarily idle, incurs a staleness at most a constant factor larger than an obvious lower bound on total staleness (provided that the processors are sufficiently fast).
Posted Content
Dial a Ride from k-forest
TL;DR: It is proved that an α-approximation algorithm for the k-forest problem implies an (αṡlog2n, and the results give a different proof of a similar approximation guarantee.
Book ChapterDOI
Improved approximation algorithms for (budgeted) node-weighted steiner problems
TL;DR: A primal-dual O(logh)-approximation algorithm for a more general problem, prize-collecting node-weighted Steiner forest (PCSF), where the authors have h demands each requesting the connectivity of a pair of vertices and can be seen as a greedy algorithm which reduces the number of demands by choosing a structure with minimum cost-to-reduction ratio.
Proceedings Article
Approximating LCS in Linear Time: Beating the √n Barrier.
TL;DR: The main result is a linear time algorithm for the longest common subsequence which has an approximation factor of O(n) which beats the √ n barrier for approximating LCS in linear time.