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Ofer Grossman

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers
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3

FOCS Conference 2023 Conference Paper

Tight Space Lower Bound for Pseudo-Deterministic Approximate Counting

  • Ofer Grossman
  • Meghal Gupta
  • Mark Sellke

We investigate one of the most basic problems in streaming algorithms: approximating the number of elements in the stream. Famously, [Mor78] gave a randomized algorithm achieving a constant-factor approximation error for streams of length at most N in space $O(\log\log N)$. We investigate the pseudo-deterministic complexity of the problem and prove a tight $\Omega(\log N)$ lower bound, thus resolving a problem of [GGMW20].

SODA Conference 2019 Conference Paper

Reproducibility and Pseudo-Determinism in Log-Space

  • 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 may result in a new (and potentially different) output. We show that every problem in search-RL has a randomized log-space algorithm where the output can be reproduced. Specifically, we show that for every problem in search-RL, there are a pair of log-space randomized algorithms A and B where for every input x, A will output some string t x of size O (log n ), such that B when running on ( x, t x ) will be pseudo-deterministic: that is, running B multiple times on the same input ( x, t x ) will result in the same output on all executions with high probability. Thus, by storing only O (log n ) bits in memory, it is possible to reproduce the output of a randomized log-space algorithm. An algorithm is reproducible without storing any bits in memory (i. e. , | t x | = 0) if and only if it is pseudo-deterministic. We show pseudo-deterministic algorithms for finding paths in undirected graphs and Eulerian graphs using logarithmic space. Our algorithms are substantially faster than the best known deterministic algorithms for finding paths in such graphs in log-space. The algorithm for search-RL has the additional property that its output, when viewed as a random variable depending on the randomness used by the algorithm, has entropy O (log n ).

FOCS Conference 2016 Conference Paper

Amplification and Derandomization without Slowdown

  • Ofer Grossman
  • Dana Moshkovitz

We present techniques for decreasing the error probability of randomized algorithms and for converting randomized algorithms to deterministic (nonuniform) 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 amplification technique is related to a certain stochastic multi-armed bandit problem. The derandomization technique - which is the main contribution of this work - points to an intriguing connection between derandomization and sketching/sparsification. We demonstrate the techniques by showing algorithms for approximating free games (constraint satisfaction problems on dense bipartite graphs).

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