Adaptive Barrier Strategies for Nonlinear Interior Methods

Copyright © [2008] by The Society for Industrial and Applied Mathematics. All rights reserved

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.

By: Jorge Nocedal; Andreas Wächter; Richard A. Waltz

Published in: SIAM Journal on Optimization, volume 19, (no 4), pages 1674-1693 in 2008


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