2 Search Results for "Golia, Priyanka"

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DOI: 10.4230/LIPIcs.CP.2022.36

On Quantitative Testing of Samplers

Authors: Mate Soos, Priyanka Golia, Sourav Chakraborty, and Kuldeep S. Meel

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract

The problem of uniform sampling is, given a formula F, sample solutions of F uniformly at random from the solution space of F. Uniform sampling is a fundamental problem with widespread applications, including configuration testing, bug synthesis, function synthesis, and many more. State-of-the-art approaches for uniform sampling have a trade-off between scalability and theoretical guarantees. Many state of the art uniform samplers do not provide any theoretical guarantees on the distribution of samples generated, however, empirically they have shown promising results. In such cases, the main challenge is to test whether the distribution according to which samples are generated is indeed uniform or not. Recently, Chakraborty and Meel (2019) designed the first scalable sampling tester, Barbarik, based on a grey-box sampling technique for testing if the distribution, according to which the given sampler is sampling, is close to the uniform or far from uniform. They were able to show that many off-the-self samplers are far from a uniform sampler. The availability of Barbarik increased the test-driven development of samplers. More recently, Golia, Soos, Chakraborty and Meel (2021), designed a uniform like sampler, CMSGen, which was shown to be accepted by Barbarik on all the instances. However, CMSGen does not provide any theoretical analysis of the sampling quality. CMSGen leads us to observe the need for a tester to provide a quantitative answer to determine the quality of underlying samplers instead of merely a qualitative answer of Accept or Reject. Towards this goal, we design a computational hardness-based tester ScalBarbarik that provides a more nuanced analysis of the quality of a sampler. ScalBarbarik allows more expressive measurement of the quality of the underlying samplers. We empirically show that the state-of-the-art sampler, CMSGen is not accepted as a uniform-like sampler by ScalBarbarik. Furthermore, we show that ScalBarbarik can be used to design a sampler that can achieve balance between scalability and uniformity.

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Mate Soos, Priyanka Golia, Sourav Chakraborty, and Kuldeep S. Meel. On Quantitative Testing of Samplers. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 36:1-36:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{soos_et_al:LIPIcs.CP.2022.36,
  author =	{Soos, Mate and Golia, Priyanka and Chakraborty, Sourav and Meel, Kuldeep S.},
  title =	{{On Quantitative Testing of Samplers}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{36:1--36:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.36},
  URN =		{urn:nbn:de:0030-drops-166655},
  doi =		{10.4230/LIPIcs.CP.2022.36},
  annote =	{Keywords: SAT Sampling, Testing of Samplers, SAT Solvers}
}

Document

DOI: 10.4230/LIPIcs.CP.2021.58

Engineering an Efficient PB-XOR Solver

Authors: Jiong Yang and Kuldeep S. Meel

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract

Despite the NP-completeness of Boolean satisfiability, modern SAT solvers are routinely able to handle large practical instances, and consequently have found wide ranging applications. The primary workhorse behind the success of SAT solvers is the widely acclaimed Conflict Driven Clause Learning (CDCL) paradigm, which was originally proposed in the context of Boolean formulas in CNF. The wide ranging applications of SAT solvers have highlighted that for several domains, CNF is not a natural representation and the reliance of modern SAT solvers on resolution proof system limit their ability to efficiently solve several families of constraints. Consequently, the past decade has witnessed the design of solvers with native support for constraints such as Pseudo-Boolean (PB) and CNF-XOR. The primary contribution of our work is an efficient solver engineered for PB-XOR formulas, i.e., formulas consisting of a conjunction of PB and XOR constraints. We first observe that a simple adaption of CNF-XOR architecture does not provide an improvement over baseline; our analysis highlights the need for careful engineering of the order of propagations. To this end, we propose three different tactics, all of which achieve significant performance improvements over the baseline. Our work is motivated by applications arising from binarized neural network verification where the verification of properties such as robustness, fairness, trojan attacks can be reduced to model counting queries; the state of the art model counters reduce counting to polynomially many SAT queries over the original formula conjuncted with randomly generated XOR constraints. To this end, we augment ApproxMC with LinPB and we call the resulting counter as ApproxMCPB. In an extensive empirical comparison over 1076 benchmarks, we observe that ApproxMCPB can solve 912 instances while the baseline version of ApproxMC4 (augmented with CryptoMiniSat) can solve only 802 instances.

Cite as

Jiong Yang and Kuldeep S. Meel. Engineering an Efficient PB-XOR Solver. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 58:1-58:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{yang_et_al:LIPIcs.CP.2021.58,
  author =	{Yang, Jiong and Meel, Kuldeep S.},
  title =	{{Engineering an Efficient PB-XOR Solver}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{58:1--58:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.58},
  URN =		{urn:nbn:de:0030-drops-153499},
  doi =		{10.4230/LIPIcs.CP.2021.58},
  annote =	{Keywords: PB-XOR Solving, Pseudo-Boolean, XOR, Gauss Jordan Elimination, SAT-Solving, Model Counting}
}

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2 Search Results for "Golia, Priyanka"

On Quantitative Testing of Samplers

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Engineering an Efficient PB-XOR Solver

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