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2015•Journal of Qinzhou UniversityRequires access

An Improved SQP Method for Solving Unconstrained Minimax Problem

Lei Shi

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Abstract

The unconstrained minimax problems are discussed in this paper. By using normrelaxed sequential quadratic programming( SQP) method and combining the active set identification technique,a perturbed SQP method without hypothesis of positive definite matrix is proposed. Under mild conditions,the algorithm has global convergence. Preliminary numerical results show that the algorithm is effective.

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What this paper is about

The unconstrained minimax problems are discussed in this paper. By using normrelaxed sequential quadratic programming( SQP) method and combining the active set identification technique,a perturbed SQP method without hypothesis of positive definite matrix is proposed. Under mild conditions,the algorithm has global convergence. Preliminary numerical results show that the algorithm is effective.

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Available abstract

The unconstrained minimax problems are discussed in this paper. By using normrelaxed sequential quadratic programming( SQP) method and combining the active set identification technique,a perturbed SQP method without hypothesis of positive definite matrix is proposed. Under mild conditions,the algorithm has global convergence. Preliminary numerical results show that the algorithm is effective.

Key concepts: Sequential quadratic programming, Minimax, Mathematical optimization, Convergence (economics), Mathematics, Quadratic programming, Set (abstract data type), Computer science

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