Doubly-Efficient Pseudo-Deterministic Proofs

10/02/2019
by   Michel Goemans, et al.
0

In [20] Goldwasser, Grossman and Holden introduced pseudo-deterministic interactive proofs for search problems where a powerful prover can convince a probabilistic polynomial time verifier that a solution to a search problem is canonical. They studied search problems for which polynomial time algorithms are not known and for which many solutions are possible. They showed that whereas there exists a constant round pseudo deterministic proof for graph isomorphism where the canonical solution is the lexicographically smallest isomorphism, the existence of pseudo-deterministic interactive proofs for NP-hard problems would imply the collapse of the polynomial time hierarchy. In this paper, we turn our attention to studying doubly-efficient pseudo-deterministic proofs for polynomial time search problems: pseudo-deterministic proofs with the extra requirement that the prover runtime is polynomial and the verifier runtime to verify that a solution is canonical is significantly lower than the complexity of finding any solution, canonical or otherwise. Naturally this question is particularly interesting for search problems for which a lower bound on its worst case complexity is known or has been widely conjectured. We show doubly-efficient pseudo-deterministic algorithms for a host of natural problems whose complexity has long been conjectured. In particular: We show a doubly efficient pseudo-deterministic proof for linear programming where the canonical solution which the prover will provide is the lexicographically greatest optimal solution for the LP. We show a doubly efficient pseudo-deterministic proof for 3-SUM and problems reducible to 3-SUM. We show a doubly-efficient pseudo-deterministic proof for the hitting set problem. We show a doubly-efficient pseudo-deterministic proof for the Zero Weight Triangle problem.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset
Success!
Error Icon An error occurred

Sign in with Google

×

Use your Google Account to sign in to DeepAI

×

Consider DeepAI Pro