Theory and Efficient Algorithms for Hard, Large Scale, Numerical Optimization

我的研究重点将是硬问题的正确建模，以及为大规模，硬，优化问题设计和实现高效和强大的数值算法。我将处理的问题出现在许多重要的应用中，例如分子构象（MC），传感器网络定位（SNL），逆成像和机器学习。特别是，这些问题中的许多出现在放松的硬组合优化问题。在许多情况下，通常的建模方法导致的问题，都是大规模和不适定的。因此，它们很难在数值上求解。而不是一个缺点，人们往往可以利用不适定性得到一个稳定的问题，一个是规模较小。特别是，对于SNL这样的问题，人们可以通过利用隐藏的简并性来高精度地解决巨大的问题。我计划将这种技术应用于具有噪声数据的MC问题以及蛋白质设计问题。

The focus of my research will be the proper modelling of hard problems, and the design and implementation of efficient and robust numerical algorithms for large scale, hard, optimization problems. The problems I will deal with arise in many important applications, e.g. molecular conformation (MC), sensor network localization (SNL), inverse imaging and machine learning. In particular, many of these problems arise in the relaxations of hard combinatorial optimization problems. In many instances, the usual modelling approaches result in problems that are both large scale and ill-posed. Therefore, they are hard to solve numerically. Rather than being a disadvantage, one can often take advantage of the ill-posedness to get both a stable problem and one that is smaller in size. In particular, for problems such as SNL one can solve huge problems to high accuracy by exploiting the hidden degeneracy. I plan on applying this technique to MC problems with noisy data as well as to protein design problems.