Karp: a language for NP reductions

Chenhao Zhang, Jason D. Hartline, Christos Dimoulas
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Abstract

In CS theory courses, NP reductions are a notorious source of pain for students and instructors alike. Invariably, students use pen and paper to write down reductions that “work” in many but not all cases. When instructors observe that a student’s reduction deviates from the expected one, they have to manually compute a counterexample that exposes the mistake. In other words, NP reductions are subtle yet, most of the time, unimplemented programs. And for a good reason: there exists no language tailored to NP reductions. We introduce Karp, a language for programming and testing NP reductions. Karp combines an array of programming languages techniques: language-oriented programming and macros, solver-aided languages, property testing, higher-order contracts and gradual typing. To validate the correctness of Karp, we prove that its core is well-defined. To validate its pragmatics, we demonstrate that it is expressive and performant enough to handle a diverse set of reduction exercises from a popular algorithms textbook. Finally, we report the results from a preliminary user study with Karp.
Karp:用于NP约简的语言
在计算机科学理论课程中,NP约简对学生和教师来说都是一个臭名昭著的痛苦来源。学生们总是用纸和笔写下在许多情况下“有效”的缩略词,但不是所有情况。当教师观察到学生的还原与预期的偏离时,他们必须手动计算一个反例来暴露错误。换句话说,NP减少是微妙的,但大多数时候,未实施的程序。有一个很好的理由:不存在专门针对NP约简的语言。我们介绍Karp,一种用于编程和测试NP约简的语言。Karp结合了一系列编程语言技术:面向语言的编程和宏、求解器辅助语言、属性测试、高阶契约和渐进类型。为了验证Karp的正确性,我们证明了它的核心是定义良好的。为了验证其语用性,我们证明它具有足够的表现力和性能,可以处理流行算法教科书中的各种简化练习。最后,我们报告了与Karp进行的初步用户研究的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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