Analysis of the Self Projected Matching Pursuit Algorithm
arXiv:1609.00053
Abstract
The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented. This approach renders an iterative way of solving the least squares problem with much less storage requirement than direct linear algebra techniques. Hence, it appropriate for solving large linear systems. The analysis highlights its suitability within the class of well posed problems.
arXiv commentThe routines for implementing the methods, as well as scripts to reproduce the examples in the manuscript, are available on the website: http://www.nonlinear-approx.info/examples/node04.html