Automated Tail Bound Analysis for Probabilistic Recurrence Relations

Abstract

Probabilistic recurrence relations (PRRs) are a standard formalism for describing the runtime of a randomized algorithm. Given a PRR and a time limit $$\kappa $$ κ , we consider the tail probability $$\Pr [T \ge \kappa ]$$ Pr [ T ≥ κ ] , i.e., the probability that the randomized runtime T of the PRR exceeds $$\kappa $$ κ . Our focus is the formal analysis of tail bounds that aims at finding a tight asymptotic upper bound $$u \ge \Pr [T\ge \kappa ]$$ u ≥ Pr [ T ≥ κ ] . To address this problem, the classical and most well-known approach is the cookbook method by Karp (JACM 1994), while other approaches are mostly limited to deriving tail bounds of specific PRRs via involved custom analysis. In this work, we propose a novel approach for deriving the common exponentially-decreasing tail bounds for PRRs whose preprocessing time and random passed sizes observe discrete or (piecewise) uniform distribution and whose recursive call is either a single procedure call or a divide-and-conquer. We first establish a theoretical approach via Markov’s inequality, and then instantiate the theoretical approach with a template-based algorithmic approach via a refined treatment of exponentiation. Experimental evaluation shows that our algorithmic approach is capable of deriving tail bounds that are (i) asymptotically tighter than Karp’s method, (ii) match the best-known manually-derived asymptotic tail bound for QuickSelect, and (iii) is only slightly worse (with a $$\log \log n$$ log log n factor) than the manually-proven optimal asymptotic tail bound for QuickSort. Moreover, our algorithmic approach handles all examples (including realistic PRRs such as QuickSort, QuickSelect, DiameterComputation, etc.) in less than 0.1 s, showing that our approach is efficient in practice.

Automated Synthesis of Decision Lists for Polynomial Specifications over Integers

“…A related line of work also considers synthesizing programs by filling holes in user-provided sketches or templates [50,31,44]. This approach has also been applied to termination and runtime analysis of programs [15,17,16,37,10], invariant generation [18,47,25,42,30], static cost analysis of probabilistic programs [21,19,54,53,22,51], detection of deep bugs [8], and LTL model-checking [20]. A more recent but quite orthogonal paradigm is example-driven synthesis, where programs are synthesized from examples using techniques from formal methods and machine learning, e.g., [48,11,33].…”