Meta-Algorithms versus Circuit Lower Bounds

元算法与电路下界

• 批准号: 298363-2012
• 负责人: Kabanets, Valentine
• 金额: $ 2.48万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571486
• 关键词: Meta; Algorithms; versus; Circuit; Lower

Computers have dramatically changed our lives. We can now efficiently solve many computational problems that would be impossible to solve without computers. Yet, many important problems still seem beyond the reach of even our most powerful super-computers. Is the apparent difficulty of these problems real (intrinsic to the problem), or these problems do have efficient algorithmic solutions that we haven't been able to discover yet? The field of computational complexity studies precisely this question: what problems require excessive computational resources (computation time, memory, etc.)? In addition to clarifying the boundary of what can be solved efficiently, identifying computationally hard problems also has important practical consequences. For instance, the security of virtually all cryptographic systems in use today (including electronic banking) relies on the unproven assumptions that certain computational problems are very hard to solve. Thus, proving that such problems are actually hard would prove that these cryptographic protocols are truly secure. One of important discoveries of modern complexity theory is the deep connection between proving the hardness of computational problems and designing efficient algorithms for related computational problems. The two tasks are like Ying and Yang of Computer Science: progress in one is impossible without progress in the other. The main goal of the proposed research is to gain better insight into this connection, and exploit it to make further progress in both computational hardness and efficient algorithm design.

计算机极大地改变了我们的生活。我们现在可以有效地解决许多没有计算机就无法解决的计算问题。然而，许多重要的问题似乎仍然超出了我们最强大的超级计算机的能力范围。这些问题的表面难度是否真实（问题的本质），或者这些问题确实有我们尚未发现的有效算法解决方案？ 计算复杂性领域正是研究这个问题：哪些问题需要过多的计算资源（计算时间、内存等）？除了澄清可以有效解决的边界之外，识别计算困难问题也具有重要的实际意义。例如，当今使用的几乎所有密码系统（包括电子银行）的安全性都依赖于未经证实的假设，即某些计算问题很难解决。因此，证明这些问题实际上很困难就可以证明这些加密协议是真正安全的。 现代复杂性理论的重要发现之一是深层联系 在证明计算问题的难度和为相关计算问题设计有效的算法之间。这两项任务就像计算机科学中的 Ying 和 Yang 一样：如果一项任务没有进展，另一项任务就不可能取得进展。 本研究的主要目标是更好地了解这种联系，并利用它在计算难度和高效算法设计方面取得进一步进展。