Towards a Unified Theory of Proof and Circuit Complexity

请考虑以下问题：给你一个非常大的数（比如10，000位），要求你找出除它的任何数（除了1或它本身）。2显然，这个问题对于强大的计算机来说是很难有效解决的。此外，现代世界的许多技术都依赖于这个问题难以解决的事实，因为它的硬度是密码学的基础，使我们所有的互联网通信安全！ 这些关于计算任务的内在困难的问题是计算复杂性理论的研究，这是我的研究领域。在复杂性理论中，我们研究计算机执行计算所必须消耗的资源量，如运行时间、内存或能量。通过这种方式，计算复杂性是算法设计的硬币的另一面，算法设计关注的是为给定任务找到最有效的算法。这项研究计划概述了一个新的和强大的家庭的技术-所谓的提升定理-已在计算复杂性理论的发展。这些新技术已经有了令人眼花缭乱的应用数量，导致了大量的计算复杂性理论和离散数学的难题的解决。此外，它们揭示了算法和证明之间的深层新联系;表明在许多利益情况下，算法是证明，证明是算法，因此分析一个对象可以让我们分析另一个对象。“起重革命”远未结束，在本建议中，我们确定了三个具体的方向，我们认为这些新技术的攻击已经成熟。第一个是加深我们对优化中常用算法的理解;第二个是量子计算;最后一个是数字电路理论（与运行计算机的电路类型相同）。最后，我们相信，一个更深层次的-目前，只有部分理解-理论，解释了这些应用程序的惊人的广度也可以开发，并表明在这个方向上的一些有前途的工作。

Consider the following problem: you are given an extremely large (say, 10,000 digit) number, and are asked to find any number (other than 1 or itself) that divides it. This problem is, apparently, very hard for powerful computers to solve efficiently. Moreover, much of the technology of the modern world crucially relies on the fact that this problem is hard to solve, since its hardness is the foundation of the cryptography that keeps all of our internet communications safe! Such questions about the intrinsic hardness of computational tasks are the study of computational complexity theory, which is my area of research. In complexity theory, we study the amounts of resources --- like running time, memory, or energy --- that computers must consume to perform their computations. In this way, computational complexity is the flip side of the coin to algorithm design, which is concerned with finding the most efficient algorithms for a given task. This research proposal outlines a new and powerful family of techniques --- called lifting theorems --- that have been developed in computational complexity theory. These new techniques have had a dazzling number of applications, leading to the resolution of a large number of hard problems in both computational complexity theory and discrete mathematics. Furthermore, they have revealed deep new connections between algorithms and proofs; showing that, in many cases of interests, algorithms are proofs and proofs are algorithms, and so analyzing one object allows us to analyze the other. The "lifting revolution" is far from being over and, in this proposal, we identify three concrete directions that we believe are ripe for attack with these new techniques. The first is deepening our understanding of algorithms commonly used in optimization; the second, in quantum computation; and the last, the theory of digital circuits (the same types of circuits that run your computer). Finally, we believe that a deeper --- and currently, only partially understood --- theory that explains the surprising breadth of these applications can also be developed, and indicate some promising work in this direction.