Applied Harmonic Analysis Methods for Non-Convex Optimizations and Low-Rank Matrix Analysis

非凸优化和低阶矩阵分析的应用调和分析方法

基本信息

批准号：
2108900
负责人：
Radu Balan
金额：
$ 30.5万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2108900&HistoricalAwards=false
关键词：
Applied Harmonic Analysis Methods Non

项目摘要

This project promotes the progress of science by expanding the area of applied harmonic analysis both in mathematics and in applications. The research addresses complex optimization problems that are intractable by current super-computers. The projects broaden the two-way communication between mathematics on one side and engineering and computer science on the other side while promoting teaching, training, and learning. The PI will train graduate students for a globally competitive STEM force through internships in industry and government labs. The project increases the existing partnerships with industry while offering opportunities to explore mathematics of real-world applications and to create novel solutions to existing problems.The project contains two thrusts. The first thrust develops homotopic methods for non-convex optimizations. In particular, the project will focus on low-rank matrix estimation, such as the case in the phase retrieval problem, and quadratic assignment problems, as in graph matching problems. The homotopic method extends the original search space (the phase space) by one continuous parameter that trades between the target non-convex objective function and a carefully chosen convex penalty term. A path tracker is initialized at the global optimizer and gradually evolved towards the global optimum of the non-convex objective function. The research will obtain guarantees of optimality for this method. The second thrust relates to geometric and functional analysis of low-rank positive semi-definite matrices. The program studies various metrics and Lipschitz embeddings of these metric spaces. Preliminary results show a rich and complex collection of metrics on this semi-algebraic variety. In particular, one such measure is related to optimal expansions into sums of nonnegative rank-one matrices. It turns out this decomposition is directly related to the analysis of compact integral operators with kernels in certain modulation spaces. These results will be extended to finite-dimensional settings.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.

该项目通过扩大数学和应用中应用谐波分析的领域来促进科学的进步。该研究解决了当前超级计算机棘手的复杂优化问题。这些项目在促进教学，培训和学习的同时，扩大了一侧数学与工程和计算机科学之间的双向交流。 PI将通过在工业和政府实验室实习来培训研究生为全球竞争性的STEM力量。该项目增加了与行业的现有合作伙伴关系，同时提供了探索现实世界应用数学的机会，并为现有问题创建新颖的解决方案。该项目包含两个推力。第一个推力开发了非凸优化的同位方法。特别是，该项目将集中于低率矩阵估计，例如阶段检索问题中的情况以及二次分配问题，如图匹配问题。同位方法将原始搜索空间（相位空间）扩展到一个连续参数，该参数在目标非凸目标函数和精心选择的凸额惩罚项之间进行交易。路径跟踪器以全局优化器初始化，并逐渐发展为非凸目标函数的全局最佳。该研究将为这种方法获得最佳保证。第二个推力与低级别阳性半明确矩阵的几何和功能分析有关。该计划研究了这些度量空间的各种指标和Lipschitz的嵌入。初步结果表明，该半代数品种的指标丰富而复杂。特别是，这样的措施与最佳扩展与非负等级矩阵的总和有关。事实证明，这种分解与在某些调制空间中与内核的紧凑型积分算子分析直接相关。这些结果将扩展到有限维度的环境。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Lipschitz Analysis of Generalized Phase Retrievable Matrix Frames

DOI：
10.1137/21m1435446
发表时间：
2021-09
期刊：
SIAM J. Matrix Anal. Appl.
影响因子：
0
作者：
R. Balan;Chris B. Dock
通讯作者：
R. Balan;Chris B. Dock

Graph Regression and Classification using Permutation Invariant Representations

DOI：
发表时间：
2021
期刊：
影响因子：
0
作者：
Naveed Haghani;Maneesh Singh;R. Balan
通讯作者：
Naveed Haghani;Maneesh Singh;R. Balan

Estimating Noise Propagation of Neural Network Based Image Reconstruction Using Automated Differentiation

使用自动微分估计基于神经网络的图像重建的噪声传播

DOI：
发表时间：
2022
期刊：
ISMRM
影响因子：
0
作者：
Wang, Xiaoke;Ludwig, Danial;Rawson, Michael;Balan, Radu;Ernst, Thomas
通讯作者：
Ernst, Thomas

Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations

DOI：
10.48550/arxiv.2303.02304
发表时间：
2023-03
期刊：
ArXiv
影响因子：
0
作者：
Xiongye Xiao;De-An Cao;Ruochen Yang;Gaurav Gupta;Gengshuo Liu;Chenzhong Yin;R. Balan;P. Bogdan
通讯作者：
Xiongye Xiao;De-An Cao;Ruochen Yang;Gaurav Gupta;Gengshuo Liu;Chenzhong Yin;R. Balan;P. Bogdan

VQ-Flows: Vector Quantized Local Normalizing Flows

VQ-Flows：矢量量化局部归一化流