- Accelerating block coordinate descent methods with identification strategies Ronaldo Lopes (ronaldolps3gmail.com) Sandra Augusta Santos (sandraime.unicamp.br) Paulo J. S. Silva (pjssilvaime.unicamp.br) Abstract: This work is about active set identification strategies aimed at accelerating block-coordinate descent methods (BCDM) applied to large-scale problems. We start by devising an identification function tailored for bound-constrained composite minimization together with an associated version of the BCDM, called Active BCDM, that is also globally convergent. The identification function gives rise to an efficient practical strategy for Lasso and $\ell_1$-regularized logistic regression. The computational performance of Active BCDM is contextualized using comparative sets of experiments that are based on the solution of problems with data from deterministic instances from the literature. These results have been compared with those of well-established and state-of-the-art methods that are particularly suited for the classes of applications under consideration. Active BCDM has proved useful in achieving fast results due to its dentification strategy. Besides that, an extra second-order step was used, with favorable cost-benefit. Keywords: Block coordinate descent; active-set identification; large-scale optimization; $\ell_1$ regularization Category 1: Nonlinear Optimization (Bound-constrained Optimization ) Citation: Download: [PDF]Entry Submitted: 11/22/2017Entry Accepted: 11/22/2017Entry Last Modified: 06/20/2018Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.