一种新的逼近精确罚函数的罚函数及性质

Operations Research Transactions ›› 2012, Vol. 16 ›› Issue (1): 56-66.

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A New Penalty Function Based on Non-coercive Penalty Functions

Shang-You-Lin¹, LIU Mu-Hua¹, LI Pu¹

1. School of Mathematics & Statistics, Henan University of Science & Technology, Luoyang 471003, China

Received:2011-04-02 Revised:2011-12-26 Online:2012-03-15 Published:2012-03-15
Supported by:
The work is partially supported by The National Natural Science Foundation of China (No.10971053, 10771162), and The National Natural Science Foundation of Henan (No. 094300510050).

Abstract

Abstract: For the differentiable nonlinear programming problem, this paper proposes a new penalty function form of the approached exact penalty function, presents
with the gradual approximation algorithm and evolutionary algorithm, and proves that if the sequences of the approximation algorithm exist accumulation point, it certainly is the optimal solution of original problem. In the weak assumptions, we prove that the minimum sequences from the algorithm is bounded, and its accumulation points are the optimal solution of the original problem and get that in the Mangasarian-Fromovitz qualification condition, through limited iterations the minimum point is the feasible point.

Key words: exact penalty function, the feasible point, optimal solution, nonlinear programming

Shang-You-Lin, LIU Mu-Hua, LI Pu. A New Penalty Function Based on Non-coercive Penalty Functions[J]. Operations Research Transactions, 2012, 16(1): 56-66.

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A New Penalty Function Based on Non-coercive Penalty Functions

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