Syamantak Das

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Online algorithms are usually analyzed using the notion of competitive ratio which compares the solution obtained by the algorithm to that obtained by an online adversary for the worst possible input sequence. Often this measure turns out to be too pessimistic, and one popular approach especially for scheduling problems has been that of “resource(More)
We consider the online scheduling problem to minimize the weighted `p-norm of flow-time of jobs. We study this problem under the rejection model introduced by Choudhury et al. (SODA 2015) – here the online algorithm is allowed to not serve an ε-fraction of the requests. We consider the restricted assignments setting where each job can go to a specified(More)
We describe a primal-dual framework for the design and analysis of online convex optimization algorithms for drifting regret. Existing literature shows (nearly) optimal drifting regret bounds only for the l2 and the l1-norms. Our work provides a connection between these algorithms and the Online Mirror Descent (OMD) updates; one key insight that results(More)
We study the classical scheduling problem of assigning jobs to machines in order to minimize the makespan. It is well-studied and admits an EPTAS on identical machines and a (2− 1/m)approximation algorithm on unrelated machines. In this paper we study a variation in which the input jobs are partitioned into bags and no two jobs from the same bag are allowed(More)
The Group Steiner Tree (GST) problem is a classical problem in combinatorial optimization and theoretical computer science. In the Edge-Weighted Group Steiner Tree (EWGST) problem, we are given an undirected graph G = (V,E) on n vertices with edge costs c : E → R≥0, a source vertex s and a collection of subsets of vertices, called groups, S1, . . . , Sk ⊆ V(More)
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