A Superlinearly feasible SQP algorithm for Constrained Optimization
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.
Keywords: Constrained Optimization, SQP Algorithm, Global convergence, Superlinear convergence rate.
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ABOUT THE AUTHOR
Zhijun Luo
Zhijun Luo received his M.S.degree from Guilin University of Electronic Technology, Guilin, China, in 2008. Now he is a lecturer in Hunan University of Humanities, Science and Technology, Loudi, China. His research interests cover optimization theory, algorithms and their applications.
Zhijun Luo
Zhijun Luo received his M.S.degree from Guilin University of Electronic Technology, Guilin, China, in 2008. Now he is a lecturer in Hunan University of Humanities, Science and Technology, Loudi, China. His research interests cover optimization theory, algorithms and their applications.
