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A Superlinearly feasible SQP algorithm for Constrained Optimization

Zhibin Zhu, Guohua Chen

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Abstract

This paper is concerned with a Superlinearly feasible SQP algorithm algorithm for general constrained optimization. As compared with the existing SQP methods, it is necessary to solve equality constrained quadratic programming sub-problems at each iteration, which shows that the computational effort of the proposed algorithm is reduced further. Furthermore, under some mild assumptions, the algorithm is globally convergent and its rate of convergence is one-step superlinearly.

About this research paper

What this paper is about

This paper is concerned with a Superlinearly feasible SQP algorithm algorithm for general constrained optimization. As compared with the existing SQP methods, it is necessary to solve equality constrained quadratic programming sub-problems at each iteration, which shows that the computational effort of the proposed algorithm is reduced further. Furthermore, under some mild assumptions, the algorithm is globally convergent and its rate of convergence is one-step superlinearly.

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Key contribution

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Method / approach

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Available abstract

This paper is concerned with a Superlinearly feasible SQP algorithm algorithm for general constrained optimization. As compared with the existing SQP methods, it is necessary to solve equality constrained quadratic programming sub-problems at each iteration, which shows that the computational effort of the proposed algorithm is reduced further. Furthermore, under some mild assumptions, the algorithm is globally convergent and its rate of convergence is one-step superlinearly.

Key concepts: Sequential quadratic programming, Mathematical optimization, Mathematics, Convergence (economics), Quadratic programming, Constrained optimization, Algorithm, Rate of convergence

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