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@inproceedings{SM22,
  author={Soos, Mate and Meel, Kuldeep S.},
  title={
    Arjun: An Efficient Independent Support Computation Technique and its
    Applications to Counting and Sampling
  },
  bib2html_pubtype={Refereed Conference},
  year={2022},
  month=nov,
  bib2html_rescat={Counting,Sampling,Solver Engineering},
  bib2html_dl_pdf={../Papers/iccad22.pdf},
  booktitle=ICCAD,
  abstract={
    Given a Boolean formula F over the set of variables X and a projection set
    P, a subset of variables I is independent support of P if two solutions
    agree on I, then they also agree on P.
    The notion of independent support is related to the classical notion of
    definability dating back to 1901, and have been studied over the decades.
    Recently, the computational problem of determining independent support for a
    given formula has attained importance owing to the crucial importance of
    independent support for hashing-based counting and sampling techniques.
    In this paper, we design an efficient and scalable independent support
    computation technique that can handle formulas arising from real-world
    benchmarks. Our algorithmic framework, called Arjun, employs implicit and
    explicit definability notions, and is based on a tight integration of
    gate-identification techniques and assumption-based framework. We
    demonstrate that augmenting the state of the art model counter ApproxMC4 and
    sampler UniGen3 with Arjun leads to significant performance improvements. In
    particular, ApproxMC4 augmented with Arjun counts 387 more benchmarks out of
    1896 while UniGen3 augmented with Arjun samples 319 more benchmarks within
    the same time limit.
  },
}