D 2016

Algorithmic Analysis of Qualitative and Quantitative Termination Problems for Affine Probabilistic Programs

CHATTERJEE, Krishnendu, Hongfei FU, Petr NOVOTNÝ and Rouzbeh HASHEMINEZHAD

Basic information

Original name

Algorithmic Analysis of Qualitative and Quantitative Termination Problems for Affine Probabilistic Programs

Authors

CHATTERJEE, Krishnendu, Hongfei FU, Petr NOVOTNÝ and Rouzbeh HASHEMINEZHAD

Edition

New York, NY, USA, Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL), p. 327--342, 16 pp. 2016

Publisher

ACM

Other information

Language

English

Type of outcome

Stať ve sborníku

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 0.335

ISBN

978-1-4503-3549-2

ISSN

DOI

UT WoS

000374053600028

Keywords in English

Concentration; Probabilistic Programs; Ranking Supermartingale; Termination

Tags

International impact, Reviewed
Změněno: 26/9/2019 09:34, doc. RNDr. Petr Novotný, Ph.D.

Abstract

V originále

In this paper, we consider termination of probabilistic programs with real-valued variables. The questions concerned are: 1. qualitative ones that ask (i) whether the program terminates with probability 1 (almost-sure termination) and (ii) whether the expected termination time is finite (finite termination); 2. quantitative ones that ask (i) to approximate the expected termination time (expectation problem) and (ii) to compute a bound B such that the probability to terminate after B steps decreases exponentially (concentration problem). To solve these questions, we utilize the notion of ranking supermartingales which is a powerful approach for proving termination of probabilistic programs. In detail, we focus on algorithmic synthesis of linear ranking-supermartingales over affine probabilistic programs (APP's) with both angelic and demonic non-determinism. An important subclass of APP's is LRAPP which is defined as the class of all APP's over which a linear ranking-supermartingale exists. Our main contributions are as follows. Firstly, we show that the membership problem of LRAPP (i) can be decided in polynomial time for APP's with at most demonic non-determinism, and (ii) is NP-hard and in PSPACE for APP's with angelic non-determinism; moreover, the NP-hardness result holds already for APP's without probability and demonic non-determinism. Secondly, we show that the concentration problem over LRAPP can be solved in the same complexity as for the membership problem of LRAPP. Finally, we show that the expectation problem over LRAPP can be solved in 2EXPTIME and is PSPACE-hard even for APP's without probability and non-determinism (i.e., deterministic programs). Our experimental results demonstrate the effectiveness of our approach to answer the qualitative and quantitative questions over APP's with at most demonic non-determinism.