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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Book ChapterDOI
Prize-collecting steiner networks via iterative rounding
TL;DR: It is shown as an structural result that in each iteration of the iterative rounding approach, there is an LP variable in a basic feasible solution which is at least one-third-integral resulting a 3-approximation algorithm for this problem.
Proceedings ArticleDOI
Stochastic matching with few queries: (1-ε) approximation
TL;DR: An algorithm that constructs a matching on a stochastic graph, which among some other important properties, guarantees that each vertex is matched independently from the vertices that are sufficiently far is analyzed and bypassed a previously known barrier towards achieving (1−є) approximation based on existence of dense Ruzsa-Szemerédi graphs.
Book ChapterDOI
Directed subset feedback vertex set is fixed-parameter tractable
TL;DR: This paper generalizes the result of Chen et al. (STOC '08) by showing that Subset Feedback Vertex Set in directed graphs can be solved in time, i.e., FPT parameterized by size k of the solution, and completes the picture for feedback vertex set problems and their subset versions in undirected and directed graphs.
Proceedings Article
Approximate maximum matching in random streams
TL;DR: There exists a single-pass deterministic semi-streaming algorithm that finds a $\frac{6}{11} (\approx 0.545)$ approximation of the maximum matching in general graphs, improving upon the state-of-art result $0.506$ approximation by Gamlath et al.
Book ChapterDOI
Approximation Algorithms for Connected Maximum Cut and Related Problems
TL;DR: This work presents the first non-trivial \(\Omega(\frac{1}{\log n})\) approximation algorithm for the connected maximum cut problem in general graphs using novel techniques and obtains a poly-logarithmic approximation algorithm.