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Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis

Katya Scheinberg (kas410***at***lehigh.edu)
Xiaocheng Tang (xit210***at***lehigh.edu)

Abstract: Recently several methods were proposed for sparse optimization which make careful use of second-order information [11, 30, 17, 3] to improve local convergence rates. These methods construct a composite quadratic approximation using Hessian information, optimize this approximation using a first-order method, such as coordinate descent and employ a line search to ensure sufficient descent. Here we propose a general framework, which includes slightly modified versions of existing algorithms and also a new algorithm, which uses limited memory BFGS Hessian approximations, and provide a global convergence rate analysis in the spirit of proximal gradient methods, which includes analysis of method based on coordinate descent.

Keywords: Convex optimization, convergence rates, quasi-Newton, coordinate descent

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation: COR@L Technical Report 13T-02-R1, ISE Department, Lehigh

Download: [PDF]

Entry Submitted: 11/25/2013
Entry Accepted: 11/25/2013
Entry Last Modified: 03/19/2014

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