A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis
published: Dec. 5, 2015, recorded: October 2015, views: 1816
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The Orthant-Wise Limited memory Quasi-Newton (OWL-QN) method has been demonstrated to be very effective in solving the ℓ1-regularized sparse learning problem. OWL-QN extends the L-BFGS from solving unconstrained smooth optimization problems to ℓ1-regularized (non-smooth) sparse learning problems. At each iteration, OWL-QN does not involve any ℓ1-regularized quadratic optimization subproblem and only requires matrix-vector multiplications without an explicit use of the (inverse) Hessian matrix, which enables OWL-QN to tackle large-scale problems efficiently. Although many empirical studies have demonstrated that OWL-QN works quite well in practice, several recent papers point out that the existing convergence proof of OWL-QN is flawed and a rigorous convergence analysis for OWL-QN still remains to be established. In this paper, we propose a modified Orthant-Wise Limited memory Quasi-Newton (mOWL-QN) algorithm by slightly modifying the OWL-QN algorithm. As the main technical contribution of this paper, we establish a rigorous convergence proof for the mOWL-QN algorithm. To the best of our knowledge, our work fills the theoretical gap by providing the first rigorous convergence proof for the OWL-QN-type algorithm on solving ℓ1-regularized sparse learning problems. We also provide empirical studies to show that mOWL-QN works well and is as efficient as OWL-QN.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !