The computational complexity of polynomial time problems

多项式时间问题的计算复杂度

• 批准号: 9979-2012
• 负责人: McKenzie, Pierre
• 金额: $ 1.6万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634388
• 关键词: computational; complexity; polynomial; time; problems

Complexity theory aims at rigorously classifying problems solvable by computers on the basis of the amounts of resources needed to solve them. Computing time is considered as a resource, as well as memory, processors, random bits, communication bits, etc. The methodology of complexity theory rests on the definition of abstract computer models, on the development of algorithms solving a problem on such models, and (ideally) on the mathematical proof that the algorithms found are the best possible. In some cases, such a proof would have great practical value; for example, the security of cryptographic protocols in actual use today rests on the perceived --but as yet unproven-- difficulty of computational problems such as factoring a large number into two smaller numbers that multiply out to this number.The long-term goal of my research is to make progress towards elucidating the structure of the complexity class P of problems solvable in polynomial time. In particular, I'm interested in the 40 year-old open question of whether every problem in P can be solved using only a logarithmic amount of memory. That question and others like it hint at the main shortcoming of complexity theory: despite intensive efforts by a generation of researchers, very few satisfactory lower bounds on the complexity of specific problems within the complexity class NP (a class presumably larger than P) are known.My short-term goal is to determine the complexity of specific problems by (A) comparing this complexity with that of other well-studied problems, (B) proving lower bounds on restricted models of computation and (C) further investigating the connections between algebra, logic and complexity. The hope is that understanding the power of restricted models of computation and characterizing the latter in different ways will help developing proof technology that can eventually tackle problems of immediate practical concern (such as factoring).

复杂性理论旨在根据解决问题所需的资源数量对计算机可解决的问题进行严格分类。计算时间被认为是一种资源，以及存储器，处理器，随机位，通信位等复杂性理论的方法论依赖于抽象计算机模型的定义，解决此类模型问题的算法的发展，以及（理想情况下）找到的算法是最好的数学证明。在某些情况下，这样的证明会有很大的实用价值;例如，在一个实施例中，当今实际使用的密码协议的安全性依赖于可感知的但尚未被证明的计算问题的难度，例如将一个大的数分解为两个较小的数并乘以该数。我的研究的长期目标是在阐明可在多项式时间内解决的问题的复杂性类P的结构方面取得进展。特别是，我对40年前的一个悬而未决的问题感兴趣，即是否P中的每个问题都可以只用对数数量的内存来解决。这个问题和其他类似的问题暗示了复杂性理论的主要缺点：尽管一代研究者付出了大量的努力，但在NP复杂性类中，对于特定问题的复杂性，很少有令人满意的下界（一个大概比P大的类）是已知的。我的短期目标是通过（A）将这个复杂性与其他研究得很好的问题的复杂性进行比较，（B）证明计算的限制模型的下界和（C）进一步研究代数、逻辑和复杂性之间的联系。希望是理解限制计算模型的力量，并以不同的方式描述后者，将有助于开发证明技术，最终解决直接的实际问题（如因子分解）。