A stabilized filter SQP algorithm for nonlinear programming

Chungen Shen (), Lei-Hong Zhang and Wei Liu
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Chungen Shen: University of Shanghai for Science and Technology
Lei-Hong Zhang: Shanghai University of Finance and Economics
Wei Liu: Shanghai Key Laboratory of Financial Information Technology (Shanghai University of Finance and Economics)

Journal of Global Optimization, 2016, vol. 65, issue 4, No 3, 677-708

Abstract: Abstract This paper presents a stabilized filter sequential quadratic programming (SQP) method for the general nonlinear optimization problems. The technique of stabilizing the inner quadratic programmings is an efficient strategy for the degenerate problem and brings the local superlinear convergence, while the integrated filter technique works effectively and guarantees the global convergence. The new algorithm works on both the primal and dual variables and solves the problem within the computational complexity comparable to the classical SQP algorithm. For the convergence, we show that (1) it converges either to a Karush–Kuhn–Tucker point at which the cone-continuity property holds, or to a stationary point in the sense of minimizing the constraint violation, and (2) under some second-order sufficient conditions, it converges locally superlinearly without any constraint qualifications. Our preliminary numerical results on a set of CUTEr test problems as well as on degenerate problems demonstrate the efficiency of the new algorithm.

Keywords: Stabilized SQP; Filter; Cone-continuity property; Global convergence; Local convergence; 90C30 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10898-015-0400-6

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