CAREER: Optimizations for Sparse Solutions and Applications

职业：稀疏解决方案和应用程序的优化

• 批准号: 0748839
• 负责人: Wotao Yin
• 金额: $ 40.58万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-15 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0748839&HistoricalAwards=false
• 关键词: CAREER; Optimizations; Sparse; Solutions; Applications

In many areas such as signal processing, control, statistics, learning, inverse problems, and management, "large" data sets are often processed to find "small" solutions, those depending ultimately upon a small number of factors. Since these solutions tend to be sparse in a way, it is possible for methods that pick out the sparse solutions to find them from a reduced number of indirect measurements compared to what are usually considered necessary. This is the emerging technology of compressed sensing (CS). In this research, the PI proposes to study a broad range of issues and techniques to advance CS. His proposed reseach includes the introduction of new methodolgies for exploiting solution sparsity to accelerate CS computation, the development of algorithms that utilize operations requiring low storage and maintain robustness to noise and errors in data, and the discovery of efficient methods for minimizing the l1-norms of wide classes of functions such as first and higher-order differences. This project will include an integrated educational program involving a new course, one Ph.D. student, and the participation in the Rice-Houston AGEP program in producing competitive women and minority graduate students.The new emerging technology of "compressed sensing" is a complement to traditional data compression. While the traditional technology encodes digital data using fewer bits in order to save storage and transmission time, the new technology can significantly reduce the time, energy, and cost associated with the acquisition of digital data. This is achieved by acquiring digit information of an object of interest from a reduced number of obervations than what is usually necessary. For example, the life of an aeriel such as a space telescope can be greatly extended due to a lower sampling rate (and thus a lower power demand). Hyperspectral and infrared imaging devices can produce the same images with smaller sensors, or if with the same sensors, images at higher resolution. As such, the new technology can lead to breakthroughs for applications where the bottleneck lies in the high cost of data acquisition.

在许多领域，如信号处理，控制，统计，学习，逆问题和管理，“大”的数据集往往被处理，以找到“小”的解决方案，这些最终取决于少数因素。由于这些解决方案往往是稀疏的方式，它是可能的方法，挑选出稀疏的解决方案，以找到他们从一个减少数量的间接测量相比，通常被认为是必要的。这就是压缩传感（CS）的新兴技术。在这项研究中，PI建议研究广泛的问题和技术，以推进CS。他提出的研究包括引入新的方法学来利用解的稀疏性来加速CS计算，开发利用低存储操作并保持对数据中噪声和错误的鲁棒性的算法，以及发现有效的方法来最小化宽类函数的l1范数，如一阶和高阶差分。该项目将包括一个综合教育计划，涉及一个新的课程，一个博士学位。学生，并参与赖斯-休斯顿AGEP计划，在生产有竞争力的妇女和少数民族研究生。新兴的技术“压缩传感”是一个补充，传统的数据压缩。虽然传统技术使用较少的比特对数字数据进行编码以节省存储和传输时间，但新技术可以显着减少与获取数字数据相关的时间，能源和成本。这是通过从比通常所需的观察次数减少的观察次数中获取感兴趣对象的数字信息来实现的。例如，由于较低的采样率（以及因此较低的功率需求），诸如太空望远镜的天线的寿命可以大大延长。高光谱和红外成像设备可以用更小的传感器产生相同的图像，或者如果使用相同的传感器，则可以产生更高分辨率的图像。因此，新技术可以为瓶颈在于数据采集成本高的应用带来突破。