On the hardness of analyzing probabilistic programs

Abstract

We study the hardness of deciding probabilistic termination as well as the hardness of approximating expected values (e.g. of program variables) and (co)variances for probabilistic programs.

Termination We distinguish two notions of probabilistic termination: Given a program P and an input \(\sigma \)...

  1. 1.

    ...does P terminate with probability 1 on input \(\sigma \)? (almost-sure termination)

  2. 2.

    ...is the expected time until P terminates on input \(\sigma \) finite? (positive almost-sure termination)

For both of these notions, we also consider their universal variant, i.e. given a program P, does P terminate on all inputs? We show that deciding almost-sure termination as well as deciding its universal variant is \(\varPi ^0_2\)-complete in the arithmetical hierarchy. Deciding positive almost-sure termination is shown to be \(\varSigma _2^0\)-complete, whereas its universal variant is \(\varPi _3^0\)-complete.

Expected values Given a probabilistic program P and a random variable f mapping program states to rationals, we show that computing lower and upper bounds on the expected value of f after executing P is \(\varSigma _1^0\)- and \(\varSigma _2^0\)-complete, respectively. Deciding whether the expected value equals a given rational value is shown to be \(\varPi ^0_2\)-complete.

Covariances We show that computing upper and lower bounds on the covariance of two random variables is both \(\varSigma _2^0\)-complete. Deciding whether the covariance equals a given rational value is shown to be in \(\varDelta _3^0\). In addition, this problem is shown to be \(\varSigma ^0_2\)-hard as well as \(\varPi ^0_2\)-hard and thus a “proper” \(\varDelta _3^0\)-problem. All hardness results on covariances apply to variances as well.

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Notes

  1. 1.

    The left branch is executed with probability \(\nicefrac {1}{3}\) and the right branch with probability \(\nicefrac {2}{3}\).

  2. 2.

    i.e. \({\textsf {diverge}}\) is syntactic sugar for \({\textsf {while}}\, \left( {\textsf {true}} \right) \, \left\{ {{\textsf {skip}}} \right\} \).

  3. 3.

    The notion of valid inputs is needed due to our restriction that program states have finite domains. If we drop this restriction, the set of all program states becomes uncountable. Moreover, note that it is clearly decidable whether a program state is valid for a given program.

  4. 4.

    Note that we allow the values of the quantified variables to be drawn from a computable domain other than \(\mathbb {N}\) that could be encoded in the natural numbers such as \(\mathbb {Q}\), the set of syntactically correct programs, etc.

  5. 5.

    The last quantifier is universal if n is even and existential if n is odd.

  6. 6.

    In this context, a set is cofinite iff its relative complement, i.e. its complement with respect to some appropriate universe, is finite.

  7. 7.

    i.e. iff \(\mathbb {S}_P \setminus \{\sigma \in \mathbb {S}_P ~|~ (P,\, \sigma ) \in \mathcal {H} \}\) is finite.

  8. 8.

    This is because we ask for an a-priori expected value with respect to an initial state.

  9. 9.

    I.e. the postexpectation that maps every program state to constantly 1.

  10. 10.

    The \(\varepsilon \) in the \( T _k(\ldots ,\, \varepsilon )\) comes from the fact that \( T _k\) is supposed to simulate k steps of an ordinary program. The \(\varepsilon \) thus stands for an empty sequence of resolutions of probabilistic choices.

  11. 11.

    The program P cheers as it was able to prove the termination of Q on input \(g_Q(i)\).

  12. 12.

    The runtime of a program corresponds to the number of execution steps in our operational semantics of pGCL (Definition 3). If more fine-grained runtime models are considered that take, for instance, the size of numbers into account, a single program step can be simulated in at most polynomial time on a Turing machine. To this end, we first translate pGCL programs to programs on a random access machine and translate the resulting program to Turing machines (cf. [37, Theorem 2.5]). In particular, our reduction remains valid for such more fine-grained runtime models.

  13. 13.

    Rounding up the value of i to a natural number, i.e. computing \(\max \{\lceil i \rceil ,\, 0\}\), is a technical necessity: We assume that variable valuations range over \(\mathbb {Q}\) but the domain of \(g_Q\) is \(\mathbb {N}\).

  14. 14.

    As opposed to a smoothed analysis of an algorithm.

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Acknowledgements

The authors would like to thank Luis María Ferrer Fioriti (Saarland University), Federico Olmedo (University of Chile), and Wolfgang Thomas (RWTH Aachen University) for the fruitful discussions on the topics of this paper. Furthermore, we acknowledge the valuable and very constructive comments we received from the anonymous referees that lead to substantial improvements of this paper.

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Correspondence to Benjamin Lucien Kaminski or Joost-Pieter Katoen or Christoph Matheja.

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This research is Funded by the Excellence Initiative of the German federal and state governments, by the RTG 2236 UnRAVeL, and by the CDZ Project CAP (GZ 1023).

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Kaminski, B.L., Katoen, JP. & Matheja, C. On the hardness of analyzing probabilistic programs. Acta Informatica 56, 255–285 (2019). https://doi.org/10.1007/s00236-018-0321-1

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