Model Counting Aditya A. Shrotri Dept. of Computer Science, Rice - - PowerPoint PPT Presentation

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Model Counting Aditya A. Shrotri Dept. of Computer Science, Rice - - PowerPoint PPT Presentation

Theory and Practice of Efficient Approximate Model Counting Aditya A. Shrotri Dept. of Computer Science, Rice University Research Overview: Approximate Model Counting Aditya A. Shrotri 1/15/2019 1 Beyond SAT: #SAT SAT: Does there exist


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Theory and Practice of Efficient Approximate Model Counting

Aditya A. Shrotri

  • Dept. of Computer Science,

Rice University

1/15/2019 Research Overview: Approximate Model Counting Aditya A. Shrotri 1

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  • SAT: Does there exist a satisfying assignment?
  • #SAT: How many satisfying assignments?
  • Complexity: #P-Complete (contains entire polynomial hierarchy)
  • In Practice: Harder than SAT

Beyond SAT: #SAT

1/15/2019 Research Overview: Approximate Model Counting Aditya A. Shrotri 2

Probabilistic Inference Measuring Information Leakage Network Reliability Probabilistic Databases #SAT

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  • F = Β¬π‘Œ1 ∧ π‘Œ2 ∨ π‘Œ2 ∧ π‘Œ3 ∧ π‘Œ4 ∨ (π‘Œ1 ∧ π‘Œ3 ∧ Β¬π‘Œ5)
  • Disjunction of Cubes
  • DNF-SAT is in P
  • #DNF is #P-Complete [Valiant, ’79]
  • Need to Approximate!

The Disjunctive Normal Form

1/15/2019 Research Overview: Approximate Model Counting Aditya A. Shrotri 3

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Input: DNF Formula F Tolerance Ξ΅ 0 < Ξ΅ < 1 Confidence Ξ΄ 0 < Ξ΄ < 1 Output: Approximate Count C s.t. Pr [#𝐆 β‹…(1-Ξ΅) < C < #𝐆 β‹…(1+Ξ΅) ] > 1-Ξ΄ Challenge: Design a poly(m, n, 1

πœ— , log( 1 πœ€ )) time algorithm

Fully Polynomial Randomized Approximation Scheme

1/15/2019 Research Overview: Approximate Model Counting Aditya A. Shrotri 4

where m = #cubes n = #vars

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  • Monte Carlo Sampling [Karp et al., β€˜89]
  • Complexity: 𝑷(𝒏 β‹… 𝒐 β‹… π’Žπ’‘π’‰(

𝟐 𝜺) β‹… 𝟐 πœ»πŸ‘)

  • Hashing – based [Chakraborty et al., β€˜16]
  • Complexity: O(π’πŸ’ β‹… 𝒐 β‹… π’Žπ’‘π’‰(

𝟐 𝜺) β‹… 𝟐 πœ»πŸ‘)

Paradigms for #DNF FPRAS

1/15/2019 Research Overview: Approximate Model Counting Aditya A. Shrotri 5

U F U F

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  • What is the power of hashing?
  • Is hashing as powerful as Monte Carlo?
  • How do algorithms compare empirically?
  • No rigorous empirical comparison

Open Questions

1/15/2019 Research Overview: Approximate Model Counting Aditya A. Shrotri 6

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1.

New hashing-based algorithm SymbolicDNFApproxMC

1.

Complexity 𝑷(𝒏 β‹… 𝒐 β‹… π’Žπ’‘π’‰(

𝟐 𝜺) β‹… 𝟐 πœ»πŸ‘)

  • Significance: General technique of hashing is as powerful as specialized technique of Monte

Carlo for DNF-Counting!

2.

Introduced generic new concepts applicable beyond DNF formulas

  • Symbolic Hashing
  • A new highly space and time efficient 2-Universal Hash Family
  • Stochastic Cell Counting

2.

Improved complexity of older hashing algorithm DNFApproxMC by 𝑃(π‘œ)

3.

First large scale experimental comparison of 5 FPRASs

Our Contributions

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Experimental Results

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  • Worst-case complexity not the last word
  • DNFApproxMC is most robust and solves

largest number of benchmarks!

  • Previous works used Vazirani Counter – not

the best choice

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SLIDE 9
  • Best complexity possible?
  • Extend to Weighted DNF-Counting
  • Hashing-based FPRAS for other problems
  • Counting Linear Extensions, Perfect Matchings, Knapsacks …
  • Practically efficient Exact Counting

Future Directions

1/15/2019 Research Overview: Approximate Model Counting Aditya A. Shrotri 9

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β€œNot All FPRASs are Equal: Demystifying FPRASs for DNF- Counting” Kuldeep S. Meel, Aditya A. Shrotri, and Moshe Y. Vardi Constraints 1-24 (2018) β€œOn Hashing-Based Approaches to Approximate DNF-Counting” Kuldeep S. Meel, Aditya A. Shrotri, and Moshe Y. Vardi FST&TCS (2017)

Publications

1/15/2019 Research Overview: Approximate Model Counting Aditya A. Shrotri 10