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On the complexity of steepest descent, Newton's and regularized Newton's methods for nonconvex unconstrained optimization

Coralia Cartis (coralia.cartis***at***ed.ac.uk)
Nick Gould (nick.gould***at***stfc.ac.uk)
Philippe Toint (philippe.toint***at***fundp.ac.be)

Abstract: It is shown that the steepest descent and Newton's method for unconstrained nonconvex optimization under standard assumptions may be both require a number of iterations and function evaluations arbitrarily close to O(epsilon^{-2}) to drive the norm of the gradient below epsilon. This shows that the upper bound of O(epsilon^{-2}) evaluations known for the steepest descent is tight, and that Newton's method may be as slow as steepest descent in the worst case. The improved evaluation complexity bound of O(epsilon^{-3/2}) evaluations known for cubically-regularised Newton methods is also shown to be tight.

Keywords: Complexity, nonconvex unconstrained optimization, steepest descent, Newton's method

Category 1: Nonlinear Optimization (Unconstrained Optimization )

Citation: Report 09/14, Department of Mathematics, FUNDP-University of Namur, Namur, Belgium

Download: [PDF]

Entry Submitted: 10/16/2009
Entry Accepted: 10/16/2009
Entry Last Modified: 10/16/2009

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