EAGER: Convex Optimization Algorithms for 21st Century Challenges

动机。对更快、更好的优化算法的需求无处不在，而且还在不断增加。除了要分析的数据的绝对规模之外，现代优化实例的性质对部分指定、不确定或高维数据的数据提出了巨大的挑战。凸优化仍然是主要的工作，但需要以多种方式开发以应对这些挑战。该项目旨在通过(a)使用随机化开发更快的凸优化算法(b)使优化算法对不确定性具有鲁棒性，以及(c)在输入仅部分指定或不确定时提供鲁棒性保证。知识价值。这个项目的基本思想是新颖的，及时的，将扩展我们的知识优化的前沿。他们整合了多个学科——运筹学、理论计算机科学、信号处理和统计学习——共同的目标是快速、鲁棒和通用的凸优化算法。用准确性换取效率，随机化的使用，面对不确定数据的保证，以及学习凸优化问题的新公式，都是具有广泛适用性的有前途的方法。更广泛的影响。这个项目的动机是应用数学中涉及许多不同应用领域的几个一般问题。这些问题的进展，包括结构化矩阵分解和估计、图估计、鲁棒多阶段决策和信号恢复，将对医学成像、雷达阵列处理、被动声成像和数字通信等各种应用产生直接和持久的影响。在多个EAGER中，指导和合作一名研究生和一名共享的博士后是该EAGER更广泛影响的另一个方面。

Motivation. The need for faster and better optimization algorithms is ubiquitous and ever increasing. Besides the sheer size of data to be analyzed, the nature of modern optimization instances presents formidable challenges with data being partially specified, uncertain or high-dimensional data. Convex optimization remains the principal workhorse, but needs to be developed in several ways to meet these challenges. This project aims to do so by (a) developing faster algorithms for convex optimization using randomization (b) making optimization algorithms robust to uncertainty, and (c) providing robust guarantees when the input is only partially specified or uncertain.Intellectual Merit. The foundational ideas of this project are novel, timely and will extend the frontier of our knowledge of optimization. They integrate multiple disciplines --- operations research, theoretical computer science, signal processing and statistical learning --- with the common goal of fast, robust and versatile convex optimization algorithms. Trading off accuracy for efficiency, the use of randomization, guarantees in the face of uncertain data and new formulations of convex optimization problems for learning, are all promising methods with wide applicability. Broader Impact. This project is motivated by several general problems in applied mathematics that touch many different application areas. Progress on these problems, which include structured matrix factorization and estimation, graph estimation, robust multi-stage decision making and signal recovery, will have direct and lasting impact in applications as diverse as medical imaging, radar array processing, passive acoustic imaging, and digital communications. Mentoring and collaborating with a graduate student and a shared postdoctoral student across multiple EAGERs are additional aspects of the broader impact of this EAGER.