Synthesis of efficient constraint-satisfaction programs

Westfold, Stephen J.; Smith, Douglas R.

doi:10.1017/s0269888901000029

Cited by 15 publications

(8 citation statements)

References 16 publications

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“…Much of the literature just mentioned focuses on algorithm configuration tools [16,26,15,2,32,20] (which we take as given in this paper) rather than algorithm design methodology. Most work in the latter vein either addresses the much broader problem of algorithm synthesis (e.g., [29,5,31]) or defines the overall approach only implicitly (e.g., [22]). The most prominent exception is "programming by optimization" [14]; however, it emphasizes connections to software engineering and does not limit itself to parametric design spaces.…”

Section: The Station Repacking Problemmentioning

confidence: 99%

Deep optimization for spectrum repacking

2017

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Over 13 months in 2016-17 the US Federal Communications Commission conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. In the end, the auction yielded $19.8 billion, $10.05 billion of which was paid to 175 broadcasters for voluntarily relinquishing their licenses across 14 UHF channels. Stations that continued broadcasting were assigned potentially new channels to fit as densely as possible into the channels that remained. The government netted more than $7 billion (used to pay down the national debt) after covering costs. A crucial element of the auction design was the construction of a solver, dubbed SATFC, that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote. This paper describes the process by which we built SATFC. We adopted an approach we dub "deep optimization", taking a data-driven, highly parametric, and computationally intensive approach to solver design. More specifically, to build SATFC we designed software that could pair both complete and local-search SAT-encoded feasibility checking with a wide range of domain-specific techniques, such as constraint graph decomposition and novel caching mechanisms that allow for reuse of partial solutions from related, solved problems. We then used automatic algorithm configuration techniques to construct a portfolio of eight complementary algorithms to be run in parallel, aiming to achieve good performance on instances that arose in proprietary auction simulations. To evaluate the impact of our solver in this paper, we built an open-source reverse auction simulator. We found that within the short time budget required in practice, SATFC solved more than 95% of the problems it encountered. Furthermore, the incentive auction paired with SATFC produced nearly optimal allocations in a restricted setting and substantially outperformed other alternatives at national scale.

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Section: The Station Repacking Problemmentioning

confidence: 99%

Deep optimization for spectrum repacking

2017

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“…The rules above are used to interpret the timed Espec into a CSP. Each of the generated constraints are in definite clause form [18,25] allowing a linear time propagation algorithm. Whenever a constraint is violated by current variable ranges, the solver performs the least refinement of one of the ranges so that the constraint is satisfied.…”

Section: Propagating Timing Constraintsmentioning

confidence: 99%

Specification-Carrying Software: Evolving Specifications for Dynamic System Composition

Anlauff¹,

Pavlović²,

Smith³

2005

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“…These systems have been successful in practical applications (Smith & Parra, 1993;Gomes et al, 1996;Blaine et al, 1998;Westfold & Smith, 2001) but are currently limited to the development of sequential algorithms.…”

Section: Hol-mlmentioning

confidence: 99%

Building reliable, high-performance networks with the Nuprl proof development system

Kreitz¹

2004

J. Funct. Prog.

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Proof systems for expressive type theories provide a foundation for the verification and synthesis of programs. But despite their successful application to numerous programming problems there remains an issue with scalability. Are proof environments capable of reasoning about large software systems? Can the support they offer be useful in practice?In this article we answer this question by showing how the Nuprl proof development system and its rich type theory have contributed to the design of reliable, high-performance networks by synthesizing optimized code for application configurations of the Ensemble group communication toolkit. We present a type-theoretical semantics of OCaml, the implementation language of Ensemble, and tools for automatically importing system code into the Nuprl system. We describe reasoning strategies for generating verifiably correct fast-path optimizations of application configurations that substantially reduce end-to-end latency in Ensemble. We also discuss briefly how to use Nuprl for checking configurations against specifications and for the design of reliable adaptive network protocols.

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Synthesis of efficient constraint-satisfaction programs

Cited by 15 publications

References 16 publications

Deep optimization for spectrum repacking

Deep optimization for spectrum repacking

Specification-Carrying Software: Evolving Specifications for Dynamic System Composition

Building reliable, high-performance networks with the Nuprl proof development system

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