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Proceedings of the ACM on Programming Languages
Article . 2025 . Peer-reviewed
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Sound and Complete Proof Rules for Probabilistic Termination

Authors: Rupak Majumdar; V. R. Sathiyanarayana;

Sound and Complete Proof Rules for Probabilistic Termination

Abstract

Deciding termination is a fundamental problem in the analysis of probabilistic imperative programs. We consider the qualitative and quantitative probabilistic termination problems for an imperative programming model with discrete probabilistic choice and demonic bounded nondeterminism. The qualitative question asks if the program terminates almost-surely, no matter how nondeterminism is resolved. The quantitative question asks for a bound on the probability of termination. Despite a long and rich literature on the topic, no sound and relatively complete proof systems were known for these problems. In this paper, we provide sound and relatively complete proof rules for proving qualitative and quantitative termination in the assertion language of arithmetic. Our rules use variant functions as measures of distances to termination as well as supermartingales that restrain the growth of these variants almost-surely. Our completeness result shows how to construct suitable supermartingale and variant functions from an almost-surely terminating program. We also show that proofs of termination in many existing proof systems can be transformed to proofs in our system, pointing to its applicability in practice. As an application of our proof rule, we show an explicit proof of almost-sure termination for the two-dimensional random walker.

Keywords

FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Logic in Computer Science (cs.LO)

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Average
Top 10%
Green
Published in a Diamond OA journal