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Electronic Colloquium on Computational Complexity

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All reports by Author Ofer Grossman:

TR20-038 | 15th March 2020
Ofer Grossman, Justin Holmgren

Error Correcting Codes for Uncompressed Messages

Most types of messages we transmit (e.g., video, audio, images, text) are not fully compressed, since they do not have known efficient and information theoretically optimal compression algorithms. When transmitting such messages, standard error correcting codes fail to take advantage of the fact that messages are not fully compressed.

We ... more >>>

TR19-185 | 6th December 2019
Greg Bodwin, Ofer Grossman

Strategy-Stealing is Non-Constructive

In many combinatorial games, one can prove that the first player wins under best play using a simple but non-constructive argument called strategy-stealing.
This work is about the complexity behind these proofs: how hard is it to actually find a winning move in a game, when you know by strategy-stealing ... more >>>

TR19-177 | 6th December 2019
Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David Woodruff

Pseudo-deterministic Streaming

A pseudo-deterministic algorithm is a (randomized) algorithm which, when run multiple times on the same input, with high probability outputs the same result on all executions. Classic streaming algorithms, such as those for finding heavy hitters, approximate counting, $\ell_2$ approximation, finding a nonzero entry in a vector (for turnstile algorithms) ... more >>>

TR19-072 | 17th May 2019
Lijie Chen, Ofer Grossman

Broadcast Congested Clique: Planted Cliques and Pseudorandom Generators

Consider the multiparty communication complexity model where there are n processors, each receiving as input a row of an n by n matrix M with entries in {0, 1}, and in each round each party can broadcast a single bit to all other parties (this is known as the BCAST(1) ... more >>>

TR18-048 | 11th March 2018
Ofer Grossman, Yang P. Liu

Reproducibility and Pseudo-Determinism in Log-Space

A curious property of randomized log-space search algorithms is that their outputs are often longer than their workspace. This leads to the question: how can we reproduce the results of a randomized log space computation without storing the output or randomness verbatim? Running the algorithm again with new random bits ... more >>>

TR17-105 | 14th June 2017
Shafi Goldwasser, Ofer Grossman, Dhiraj Holden

Pseudo-Deterministic Proofs

We introduce pseudo-deterministic interactive proofs (psdAM): interactive proof systems for search problems where
the verifier is guaranteed with high probability to output the same output on different executions.
As in the case with classical interactive proofs,
the verifier is a probabilistic polynomial time algorithm interacting with an untrusted powerful prover.

... more >>>

TR15-208 | 26th December 2015
Shafi Goldwasser, Ofer Grossman

Perfect Bipartite Matching in Pseudo-Deterministic $RNC$

Revisions: 2

In this paper we present a pseudo-deterministic $RNC$ algorithm for finding perfect matchings in bipartite graphs. Specifically, our algorithm is a randomized parallel algorithm which uses $poly(n)$ processors, $poly({\log n})$ depth, $poly(\log n)$ random bits, and outputs for each bipartite input graph a unique perfect matching with high probability. That ... more >>>

TR15-207 | 23rd December 2015
Ofer Grossman

Finding Primitive Roots Pseudo-Deterministically

Pseudo-deterministic algorithms are randomized search algorithms which output unique solutions (i.e., with high probability they output the same solution on each execution). We present a pseudo-deterministic algorithm that, given a prime $p,$ finds a primitive root modulo $p$ in time $\exp(O(\sqrt{\log p \log \log p}))$. This improves upon the previous ... more >>>

TR15-158 | 27th September 2015
Ofer Grossman, Dana Moshkovitz

Amplification and Derandomization Without Slowdown

We present techniques for decreasing the error probability of randomized algorithms and for converting randomized algorithms to deterministic (non-uniform) algorithms. Unlike most existing techniques that involve repetition of the randomized algorithm, and hence a slowdown, our techniques produce algorithms with a similar run-time to the original randomized algorithms.

The ... more >>>

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