Special Session 7: Topological and combinatorial dynamics
Contents
The study of complexity of algorithms can be very difficult and pose significant scientific challenge. We present a study of complexity of the Orthogonal Matching Pursuit algorithm under Restricted Isometry Property conditions using the estimates of convergence rate of iterations of a family of functions.
Restricted Isometry Property was introduced by Terrence Tao et. al. and is considered a very important topic in compressed sensing and Machine Learning.
Orthogonal Matching Pursuit is a classical feature-selection algorithm in Machine Learning.