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We initiate a thorough study of distributed property testing – producing algorithms for the approximation problems of property testing in the CONGEST model. In particular, for the so-called dense graph testing model we emulate sequential tests for nearly all graph properties having 1-sided tests, while in the general and sparse models we obtain faster tests… (More)

- Vikraman Arvind, Sebastian Kuhnert, Johannes Köbler, Yadu Vasudev
- Electronic Colloquium on Computational Complexity
- 2012

We study optimization versions of Graph Isomorphism. Given two graphs G1, G2, we are interested in finding a bijection π from V (G1) to V (G2) that maximizes the number of matches (edges mapped to edges or non-edges mapped to non-edges). We give an n O(log n) time approximation scheme that for any constant factor α < 1, computes an α-approximation. We prove… (More)

- Eldar Fischer, Oded Lachish, Yadu Vasudev
- 2015 IEEE 56th Annual Symposium on Foundations of…
- 2015

We show that every non-adaptive property testing algorithm making a constant number of queries, over a fixed alphabet, can be converted to a sample-based (as per [Gold Reich and Ron, 2015]) testing algorithm whose average number of queries is a fixed, smaller than 1, power of n. Since the query distribution of the sample-based algorithm is not dependent at… (More)

- Vikraman Arvind, Partha Mukhopadhyay, Prajakta Nimbhorkar, Yadu Vasudev
- Electronic Colloquium on Computational Complexity
- 2011

Let G = S be a solvable permutation group given as input by the generating set S. I.e. G is a solvable subgroup of the symmetric group S n. We give a deterministic polynomial-time algorithm that computes an expanding generating set of size O(n 2) for G. More precisely, given a λ < 1, we can compute a subset T ⊂ G of size O(n 2) 1 λ O(1) such that the… (More)

- Ritika Sharma, Kamlesh Gupta, +13 authors Rahul Rishi
- 2013

The transmission of information in a MANET relies on the performance of the traffic scenario (application traffic agent and data traffic) used in a network. The traffic scenario determines the reliability and capability of information transmission, which necessitates its performance analysis. The objective of this paper is to compare the performance of… (More)

- Eldar Fischer, Oded Lachish, Yadu Vasudev
- STACS
- 2017

Distribution testing deals with what information can be deduced about an unknown distribution over {1,. .. , n}, where the algorithm is only allowed to obtain a relatively small number of independent samples from the distribution. In the extended conditional sampling model, the algorithm is also allowed to obtain samples from the restriction of the original… (More)

- Yadu Vasudev
- 2011

Declaration I declare that the thesis titled The Partial Derivative method in Arithmetic Circuit Complexity is a record of the work done by me during Arvind. This work has not been submitted earlier as a whole or in part for a degree, diploma, associateship or fellowship at this institute or any other institute or university. Yadu Vasudev Certicate Certied… (More)

We study the complexity of isomorphism testing for boolean functions that are represented by decision trees or decision lists. Our results are the following: • Isomorphism testing of rank 1 decision trees is complete for logspace. • For any constant r ≥ 2, isomorphism testing for rank r decision trees is polynomial-time equivalent to Graph Isomorphism. As a… (More)

- Hendrik Fichtenberger, Yadu Vasudev
- ArXiv
- 2017

We study the problem of testing conductance in the distributed computing model and give a two-sided tester that takes $\mathcal{O}(\log n)$ rounds to decide if a graph has conductance at least $\Phi$ or is $\epsilon$-far from having conductance at least $\Theta(\Phi^2)$ in the distributed CONGEST model. We also show that $\Omega(\log n)$ rounds are… (More)

- Vikraman Arvind, Yadu Vasudev
- Inf. Comput.
- 2011

Given two n-variable Boolean functions f and g, we study the problem of computing an ε-approximate isomorphism between them. I.e. a permutation π of the n variables such that f (x1, x2,. .. , xn) and g(x π(1) , x π(2) ,. .. , x π(n)) differ on at most an ε fraction of all Boolean inputs {0, 1} n. We give a randomized 2 O(√ n log(n/ε) O(d)) time algorithm… (More)