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.
Papers
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Journal ArticleDOI
Prize-collecting steiner network problems
TL;DR: A novel linear programming relaxation for the Prize-Collecting Steiner Network problem is presented, and by rounding it, the first constant-factor approximation algorithm for submodular and monotone nondecreasing penalty functions is obtained.
Patent
Minimizing staleness in real-time data warehouses
TL;DR: In this article, data tables in data warehouses are updated to minimize staleness and stretch of the data tables, and aggregated update requests may be batched in order to minimize the stretch of data tables.
Posted Content
Improved Approximation Algorithms for (Budgeted) Node-weighted Steiner Problems
TL;DR: In this article, a primal-dual O(log h)-approximation algorithm for the Steiner Steiner forest problem was proposed, which is almost tight for the natural linear programming relaxation used by Moss and Rabani.
Book ChapterDOI
Disjoint-path facility location: theory and practice
Lee Breslau,Ilias Diakonikolas,Nick Duffield,Yu Gu,MohammadTaghi Hajiaghayi,David S. Johnson,Howard Karloff,Mauricio G. C. Resende,Subhabrata Sen +8 more
TL;DR: This paper proposes three algorithms that build solutions and determine lower bounds on the optimum solution, and evaluates them on several large real ISP topologies and on synthetic networks designed to reflect real-world LAN/WAN network structure.
Journal ArticleDOI
Minimizing Movement: Fixed-Parameter Tractability
TL;DR: In this article, the authors study an extensive class of movement minimization problems that arise from many practical scenarios but so far have little theoretical study and show that many movement problems of interest can be solved efficiently.