Switching Stepsize Strategies for Sequential Quadratic Programming


DOI: 10.1007/s10957-010-9790-2

Cite this article as:
Tzallas-Regas, G. & Rustem, B. J Optim Theory Appl (2011) 149: 269. doi:10.1007/s10957-010-9790-2


A Sequential Quadratic Programming (in short, SQP) algorithm is presented for solving constrained nonlinear programming problems. The algorithm uses three stepsize strategies, in order to achieve global and superlinear convergence. Switching rules are implemented that combine the merits and avoid the drawbacks of the three stepsize strategies. A penalty parameter is determined, using an adaptive strategy that aims to achieve sufficient decrease of the activated merit function. Global convergence is established and it is also shown that, locally, unity step sizes are accepted. Therefore, superlinear convergence is not impeded under standard assumptions. Global convergence and convergence of the stepsizes are displayed on test problems from the Hock and Schittkowski collection.


Nonlinear programming SQP Global convergence Stepsize convergence Merit functions Switching stepsize strategies 

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of ComputingImperial College LondonLondonUK

Personalised recommendations