Block BFGS Methods
Wenbo Gao (wg2279columbia.edu)
Abstract: We introduce a quasi-Newton method with block updates called Block BFGS. We show that this method, performed with inexact Armijo-Wolfe line searches, converges globally and superlinearly under the same convexity assumptions as BFGS. We also show that Block BFGS is globally convergent to a stationary point when applied to non-convex functions with bounded Hessian, and discuss other modifications for non-convex minimization. Numerical experiments comparing Block BFGS, BFGS and gradient descent are presented.
Keywords: Unconstrained optimization, quasi-Newton methods, BFGS, block updates
Category 1: Nonlinear Optimization (Unconstrained Optimization )
Citation: manuscript, Dept. of Industrial Engineering and Operations Research, Columbia University, New York, NY, Sept 2016.
Entry Submitted: 10/01/2016
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