Optimization Online


Adaptive Barrier Strategies for Nonlinear Interior Methods

Jorge Nocedal (nocedal***at***ece.northwestern.edu)
Andreas Wächter (andreasw***at***watson.ibm.com)
Richard A Waltz (rwaltz***at***ece.northwestern.edu)

Abstract: This paper considers strategies for selecting the barrier parameter at every iteration of an interior-point method for nonlinear programming. Numerical experiments suggest that adaptive choices, such as Mehrotra's probing procedure, outperform static strategies that hold the barrier parameter fixed until a barrier optimality test is satisfied. A new adaptive strategy is proposed based on the minimization of a quality function. The paper also proposes a globalization framework that ensures the convergence of adaptive interior methods. The barrier update strategies proposed in this paper are applicable to a wide class of interior methods and are tested in the two distinct algorithmic frameworks provided by the Ipopt and Knitro software packages.


Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Research Report RC23563, IBM T. J. Watson Research Center, Yorktown, USA

Download: [Postscript][Compressed Postscript][PDF]

Entry Submitted: 03/04/2005
Entry Accepted: 03/04/2005
Entry Last Modified: 03/18/2005

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