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CIF: Small: The Interplay Between Convex Feasibility Problems and Minimization Problems in Signal Recovery

CIF：小：信号恢复中凸可行性问题和最小化问题之间的相互作用

基本信息

批准号：
1715671
负责人：
Patrick Combettes
金额：
$ 36.21万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715671&HistoricalAwards=false
关键词：
CIF Small Interplay Between Convex

项目摘要

Signal recovery encompasses the large body of inverse problems in which a signal is to be restored or reconstructed from the observation of data consisting of measurements physically or mathematically related to it. The importance of this field stems from its pervasiveness in numerous areas of science and engineering, including medical imaging, geophysics, astronomy, electron microscopy, nondestructive testing, seismology, telecommunications, social media analysis, and homeland security. This project investigates foundational principles guiding the formulation of signal recovery problems as convex optimization problems and develops new strategies and methodologies for data processing that significantly improve the efficiency of existing techniques and broadens their scope.This research focuses on the interplay between two prominent frameworks that coexist in relative independence in signal recovery, namely convex feasibility problems and convex minimization problems. These two approaches employ different principles to exploit the prior knowledge and the data, their mathematical formalizations lead to distinct fixed point paradigms, and the algorithms used to solve them do not rely on the same techniques. The investigator shows that, despite these profound divergences, fruitful connections can be established between the two formalisms, that are mutually beneficial and suggest new models and algorithms. An important outcome of this research is a relaxation model that bridges the gap between feasibility and minimization formulations. Another highlight is a novel proximal geometric framework for solving structured minimization problems using a deep cutting plane technology adapted from convex feasibility algorithms. The impact of the theoretical findings and of the new algorithms resulting from this research is illustrated through applications to concrete signal recovery problems.

信号恢复包括大量的逆问题，在这些问题中，信号要从观测到的数据中恢复或重建，这些数据包括物理上或数学上与信号相关的测量。这个领域的重要性源于它在许多科学和工程领域的普及，包括医学成像、地球物理、天文学、电子显微镜、无损检测、地震学、电信、社交媒体分析和国土安全。该项目研究了将信号恢复问题描述为凸优化问题的基本原则，并开发了新的数据处理策略和方法，显著提高了现有技术的效率并拓宽了其范围。本研究重点研究了信号恢复中两个相对独立的框架之间的相互作用，即凸可行性问题和凸极小化问题。这两种方法使用不同的原理来利用先验知识和数据，它们的数学形式化导致了不同的不动点范例，用于求解它们的算法不依赖于相同的技术。这位研究人员表明，尽管存在这些深刻的分歧，但这两种形式主义之间可以建立起富有成效的联系，这两种形式是互惠互利的，并提出了新的模型和算法。这项研究的一个重要成果是一个松弛模型，它弥合了可行性和最小化公式之间的差距。另一个亮点是一个新的近邻几何框架，用于解决结构化最小化问题，该框架使用了从凸可行性算法改编而来的深割平面技术。通过对具体信号恢复问题的应用，说明了理论发现和由本研究产生的新算法的影响。