Complexity theoretic implications of pseudodeterministic algorithms for PPT-search problems

Webpage for a paper by Oded Goldreich and Roei Tell


Abstract

Pseudodeterministic algorithms are probabilistic algorithms that solve search problems but do so by always providing the same (``canonical'') solution to a given instance, except with small probability. While the complexity theoretic implications of pseudodeterministic algorithms were explored in the past, we suggest to conduct this exploration within the framework of the recently defined class of PPT-search problems. Specifically, we use this framework in order to re-formulate some known observations as well as present some new results. In particular, as observed in the past, the difference between general probabilistic polynomial-time algorithms and pseudodeterministic ones is reflected in the difference between promise-$\BPP$ and $\BPP$. We make this connection stronger by showing that every PPT-search problem has a pseudodeterministic polynomial-time algorithm if and only if every decisional problem in promise-$\BPP$ can be extended to a problem in $\BPP$. Our main focus is on the class of PPT-search problems with unary instances (a.k.a explicit construction problems). In particular, we prove the following results.

We discuss the gap between the former hypotheses and the result provided by Chen, Lu, Oliveira, Ren, and Santhanam (FOCS23).

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