Ofer Grossman

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 >>>

Shafi Goldwasser, Ofer Grossman

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 >>>

Igor Carboni Oliveira, Rahul Santhanam

We study {\it pseudodeterministic constructions}, i.e., randomized algorithms which output the {\it same solution} on most computation paths. We establish unconditionally that there is an infinite sequence $\{p_n\}_{n \in \mathbb{N}}$ of increasing primes and a randomized algorithm $A$ running in expected sub-exponential time such that for each $n$, on input ... more >>>

Shafi Goldwasser, Ofer Grossman, Dhiraj Holden

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.

Ofer Grossman, Yang P. Liu

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 >>>