A Globally Convergent Line Search Filter SQP Method for Inequality Constrained Optimization | Zendy

Zhong Jin | Zendy

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A Globally Convergent Line Search Filter SQP Method for Inequality Constrained Optimization

Author(s) -

Zhong Jin

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/524539

Subject(s) - sequential quadratic programming , line search , backtracking , mathematical optimization , convergence (economics) , mathematics , filter (signal processing) , trust region , line (geometry) , computer science , quadratic programming , path (computing) , geometry , radius , computer security , economics , computer vision , programming language , economic growth

A line search filter SQP method for inequality constrained optimization is presented. This method makes use of a backtracking line search procedure to generate step size and the efficiency of the filter technique to determine step acceptance. At each iteration, the subproblem is always consistent, and it only needs to solve one QP subproblem. Under some mild conditions, the global convergence property can be guaranteed. In the end, numerical experiments show that the method in this paper is effective

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