CIF: Small: A Simple and Unifying Optimization Framework for Signal and Information Processing Problems with Min-Max Structures

CIF：Small：针对具有最小-最大结构的信号和信息处理问题的简单且统一的优化框架

• 批准号: 1910385
• 负责人: Mingyi Hong
• 金额: $ 41万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910385&HistoricalAwards=false
• 关键词: CIF; Small; Simple; Unifying; Optimization

Over the past two decades, advances in signal and information processing have been heavily influenced by new developments in optimization. Modern optimization methods, from linear programming to convex optimization, have become indispensable tools to approach problems that arise in a number of core application areas such as compressive sensing, bioinformatics, wireless communication, and data analytics. Meanwhile, the tremendous growth in optimization algorithms for signal processing applications calls for the development of unifying frameworks, which can help simplify our understanding of algorithmic behavior, predict performance, and streamline the design of application-specific algorithms. The framework developed in this project will benefit a wide range of applications well beyond signal processing, including machine learning, data mining and computer vision. The proposed efforts also offer rich and varied opportunities for engaging undergraduate students in cross-disciplinary research, as well as in K12 outreach activities. This project is focused on designing a framework that deals with a challenging class of block Minimization-Maximization problems, which involves jointly minimizing and maximizing a particular class of structured objective functions. This covers many signal processing applications, old and new, such as min rate utility maximization and robust data analytics. This project will take a bottom-up approach, where starting from a relatively simple formulation, problem-specific features and properties will be gradually built. Rigorous performance analysis will be conducted, and the resulting algorithms will be specialized and evaluated in a number of signal processing applications, including wireless resource allocation in the presence of jammers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的二十年中，信号和信息处理的进展受到优化新发展的严重影响。从线性编程到凸优化的现代优化方法已成为必不可少的工具，可以解决在许多核心应用领域（例如压缩传感，生物信息学，无线通信和数据分析）中出现的问题。同时，信号处理应用程序优化算法的巨大增长要求开发统一的框架，这可以帮助简化我们对算法行为的理解，预测性能并简化应用程序特定算法的设计。该项目中开发的框架将使广泛的应用程序受益于信号处理，包括机器学习，数据挖掘和计算机视觉。拟议的努力还为使本科生参与跨学科研究以及K12外展活动提供了丰富而多样的机会。该项目的重点是设计一个框架，该框架处理一类充满挑战的块最小化最大化问题，这涉及共同最大程度地减少和最大化特定类别的结构化目标功能。这涵盖了许多新旧信号处理应用程序，例如最低率实用程序最大化和强大的数据分析。该项目将采用自下而上的方法，从相对简单的公式开始，特定于问题的功能和属性将逐渐构建。将进行严格的性能分析，并将在许多信号处理应用中进行专业和评估所得的算法，包括在Jammers存在的情况下进行的无线资源分配。该奖项反映了NSF的法定任务，并被视为值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持。

项目成果

Finding First-Order Nash Equilibria of Zero-Sum Games with the Regularized Nikaido-Isoda Function

• 发表时间: 2021
• 作者: Ioannis C. Tsaknakis;Mingyi Hong

Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

• 发表时间: 2021-12
• 作者: Yihua Zhang;Guanhua Zhang;Prashant Khanduri;Min-Fong Hong;Shiyu Chang;Sijia Liu

Distributed Adversarial Training to Robustify Deep Neural Networks at Scale

• DOI: 10.48550/arxiv.2206.06257
• 发表时间: 2022-06
• 作者: Gaoyuan Zhang;Songtao Lu;Yihua Zhang;Xiangyi Chen;Pin-Yu Chen;Quanfu Fan;Lee Martie;L. Horesh

Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications

• DOI: 10.1109/tsp.2020.2986363
• 发表时间: 2019-02
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Songtao Lu;Ioannis C. Tsaknakis;Mingyi Hong;Yongxin Chen

Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks

• 发表时间: 2020-07
• 作者: Sijia Liu;Songtao Lu;Xiangyi Chen;Yao Feng;Kaidi Xu;Abdullah Al-Dujaili;Mingyi Hong;Una-May O’Reilly