Aravindan Vijayaraghavan

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In the Densest k-Subgraph problem, given a graph G and a parameter k, one needs to find a subgraph of G induced on k vertices that contains the largest number of edges. There is a significant gap between the best known upper and lower bounds for this problem. It is NP-hard, and does not have a PTAS unless NP has subexponential time algorithms. On the other(More)
In the Densest k-Subgraph problem, given a graph G and a parameter k, one needs to find a subgraph of G induced on k vertices that contains the largest number of edges. There is a significant gap between the best known upper and lower bounds for this problem. It is NP-hard, and does not have a PTAS unless NP has subexponential time algorithms. On the other(More)
Low rank decomposition of tensors is a powerful tool for learning generative models. The uniqueness results that hold for tensors give them a significant advantage over matrices. However, tensors pose serious algorithmic challenges; in particular, much of the matrix algebra toolkit fails to generalize to tensors. Efficient decomposition in the overcomplete(More)
Quadratic Programming (QP) is the well-studied problem of maximizing over {−1, 1} values the quadratic form ∑ i6=j aijxixj . QP captures many known combinatorial optimization problems, and assuming the unique games conjecture, semidefinite programming techniques give optimal approximation algorithms. We extend this body of work by initiating the study of(More)
We study the <i>k</i>-route cut problem: given an undirected edge-weighted graph <i>G</i> &equals; (<i>V</i>, <i>E</i>), a collection {(<i>s</i><sub>1</sub>, <i>t</i><sub>1</sub>), (<i>s</i><sub>2</sub>, <i>t</i><sub>2</sub>), &#8230;, (<i>s<sub>r</sub></i>, <i>t<sub>r</sub></i>)} of source-sink pairs, and an integer connectivity requirement <i>k</i>, the(More)
We give a robust version of the celebrated result of Kruskal on the uniqueness of tensor decompositions: we prove that given a tensor whose decomposition satisfies a robust form of Kruskal’s rank condition, it is possible to approximately recover the decomposition if the tensor is known up to a sufficiently small (inverse polynomial) error. Kruskal’s(More)
The Densest k-subgraph problem (i.e. find a size k subgraph with maximum number of edges), is one of the notorious problems in approximation algorithms. There is a significant gap between known upper and lower bounds for Densest k-subgraph: the current best algorithm gives an ≈ O(n) approximation, while even showing a small constant factor hardness requires(More)
In this paper, we propose and study a new semi-random model for graph partitioning problems. We believe that it captures many properties of real-world instances. The model is more flexible than the semi-random model of Feige and Kilian and planted random model of Bui, Chaudhuri, Leighton and Sipser. We develop a general framework for solving semi-random(More)
We consider the problem of computing the q 7→ p norm of a matrix A, which is defined for p, q ≥ 1, as ‖A‖q 7→p = max x 6=~0 ‖Ax‖p ‖x‖q . This is in general a non-convex optimization problem, and is a natural generalization of the well-studied question of computing singular values (this corresponds to p = q = 2). Different settings of parameters give rise to(More)