Now showing items 61-67 of 67
An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization
Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth optimization problems (chiefly but not necessarily convex programs) ...
User's Guide for LMaFit: Low-rank Matrix Fitting
This User's Guide describes the functionality and basic usage of the Matlab package LMaFit for low-rank matrix optimization. It also briefly explains the formulations and algorithms used.
Convergence of a Class of Stationary Iterative Methods for Saddle Point Problems
A unified convergence result is derived for an entire class of stationary iterative methods for solving equality constrained quadratic programs or saddle point problems. This class is constructed from essentially all ...
User's Guide For YALL1: Your Algorithms for L1 Optimization
This User's Guide describes the functionality and basic usage of the Matlab package YALL1 for L1 minimization. The one-for-six algorithm used in the YALL1 solver is briefly introduced in the appendix.
Alternating Direction Algorithms for L1-Problems in Compressive Sensing
In this paper, we propose and study the use of alternating direction algorithms for several L1-norm minimization problems arising from sparse solution recovery in compressive sensing, including the basis pursuit problem, ...
A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing
Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation and I/O throughputs, especially when real-time processing is desired. In this paper, we investigate a ...
An Alternating Direction and Projection Algorithm for Structure-enforced Matrix Factorization
Structure-enforced matrix factorization (SeMF) represents a large class of mathematical models ap- pearing in various forms of principal component analysis, sparse coding, dictionary learning and other machine learning ...