A Sequential Quadratic Programming Algorithm with Non-Monotone Line Search
Yu-hong Dai,
K. Schittkowski: Pacific Journal of Optimization, Vol. 4, 335-351
(2008)
Abstract:
Today, practical smooth nonlinear programming problems are routinely solved by
sequential quadratic programming (SQP) methods stabilized by a monotone line
search procedure subject to a suitable merit function. In case of computational
errors as for example caused by inaccurate function or gradient evaluations,
however, the approach is unstable and often terminates with an error message. To
prevent this situation, a non-monotone line search is proposed which allows the
acceptance of a larger steplength. As a by-product, we consider also the
possibility to adapt the line search to run under distributed systems. Global
convergence of the new SQP algorithm is proved. Numerical results are included,
which show that in case of very noisy function values a drastic improvement of
the performance is achieved compared to the version with monotone line search.
To download a preprint, click here: nm_sqp2.pdf