Wotao Yin: Optimization with Normalization and Orthogonality Constraints. (Slides)

Nabor Reyna: GPU Computation. (Slides)

Xin Liu: Limited Memory Block Subspace Acceleration for Computing Dominant Singular Values and Vectors. (Slides)

Jorge Castanon: Slides.

Wei Deng: Efficient Optimization Algorithms for Reconstructing Signals with Joint or Group Sparsity. (Slides)

Chengbo Li: Hyperspectral Data Unmixing Using Comressed Sensing Techniques. (Slides)

Yingying Li: 1. Multi-spectral Data to Classification. (Paper)
2. OFDM Channel Estimation using Compressive Sensing. (Paper)

Abstract:  The null space property and the restricted isometry property  for a  measurement matrix are two basic properties in compressive sampling, and are closely related to the sparse approximation. In this talk,  we introduce the  sparse approximation property  for a measurement matrix,  a weaker version of the restricted isometry property and a stronger version of the null space property. We show that the sparse approximation property  for a measurement matrix could be an appropriate condition to consider stable recovery of any compressible signal from its noisy measurements.

2-4pm, 01/19/2011 in Duncan Hall 3076