(Kenosha Pass, September 2017 (Colorful CO))
Dual Principal Component Pursuit

This software package contains a collection of tools for the Dual Principal Component Pursuit (DPCP) method that robustly fits a subspace to data contaminated with outliers.
For more information, see “Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms,” by Z. Zhu, Y. Wang, D. P. Robinson, D. Naiman, R. Vidal, and M. C. Tsakiris.
The code can be downloaded here; see the included readme file for a detailed description of the contents and for usage instructions.

Nonconvex Robust Lowrank Matrix Recovery

This software package contains a simple demo for SubGradient Method that is used to efficiently recover a lowrank matrix from a number of random linear measurements that are corrupted by outliers taking arbitrary values.
For more information, see “Nonconvex Robust Lowrank Matrix Recovery,” by X. Li, Z. Zhu, A.M.C. So, and R. Vidal.
The code can be downloaded here.

Symmetric Nonnegative Matrix Factorization

This software package contains a collection of tools for solving Symmetric Nonnegative Matrix Factorization by efficient alternating minimization algorithms.
For more information, see “Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization,” by Z. Zhu, X. Li, K. Liu, and Q. Li.
The code can be downloaded here; see the included readme file for a detailed description of the contents and for usage instructions.

Collaborative Compressive Sensing Systems
Fast Slepian Transform

This software package contains a collection of tools for implementing fast alorithms for working with the Slepian basis, also known as discrete prolate spheroidal sequences.
For more information, see “The Fast Slepian Transform,” by S. Karnik, Z. Zhu, M. B. Wakin, J. Romberg, and M. A. Davenport.
The code can be downloaded here; see the included readme file for a detailed description of the contents and for usage instructions.

Circulant Approximation
Optimized Sparse Sensing Matrix

This software package contains a collection of tools for desinging a sparse sensing matrix for compressive sensing systems.
For more information, see “Optimized Sparse Projections for Compressive Sensing,” by T. Hong, X. Li, Z. Zhu, and Q. Li.
The code can be downloaded here; see the included readme file for a detailed description of the contents and for usage instructions.

Optimized Sensing Matrix

This software package contains a collection of tools for desinging the sensing matrix by minimizing the mutual coherence of the compressive sensing system.
For more information, see “On Projection Matrix Optimization for Compressive Sensing Systems,” by G. Li, Z. Zhu, D. Yang, L. Chang, and H. Bai.
The code can be downloaded here; see the included readme file for a detailed description of the contents and for usage instructions.

