Cong Han Lim – Optimization Online

A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization

Published: 2019/12/12, Updated: 2019/12/17

Nonsmooth Optimization, Parallel Algorithms distributed optimization, empirical riskminimization, inexact method, nonsmooth optimization, proximal method, quasi-newton methods, regularized optimization, variable metrics

We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving empirical risk minimization (ERM) problems with a nonsmooth regularization term. Our algorithm is applicable to both the primal and the dual ERM problem. Current second-order and quasi-Newton methods for this problem either do not work well in the distributed setting or … Read more

A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization

Published: 2018/03/04, Updated: 2018/05/26

Stephen Wright

Cong Han Lim

Ching-pei Lee

Data-Mining, Nonlinear Optimization, Parallel Algorithms distributed optimization, empirical risk minimization, inexact method, nonsmooth optimization, proximal method, quasi-newton methods, regularized optimization, variable metrics

We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving ERM problems with a nonsmooth regularization term. Current second-order and quasi-Newton methods for this problem either do not work well in the distributed setting or work only for specific regularizers. Our algorithm uses successive quadratic approximations, and we describe how to … Read more