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A New Nonmonotone Adaptive Trust Region Method for Unconstrained Optimization Problems

Qunyan Zhou

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Abstract

In this paper,a new nonmonotone adaptive trust region method for unconstrained optimization problems is presented.The trust region radius in the new method is determined with the scalar approximation of Hessian matrix of the objective function.Under general conditions,the global and superlinear convergence results of the algorithm are established.Numerical results show that the new method is more efficient.

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What this paper is about

In this paper,a new nonmonotone adaptive trust region method for unconstrained optimization problems is presented.The trust region radius in the new method is determined with the scalar approximation of Hessian matrix of the objective function.Under general conditions,the global and superlinear convergence results of the algorithm are established.Numerical results show that the new method is more efficient.

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Available abstract

In this paper,a new nonmonotone adaptive trust region method for unconstrained optimization problems is presented.The trust region radius in the new method is determined with the scalar approximation of Hessian matrix of the objective function.Under general conditions,the global and superlinear convergence results of the algorithm are established.Numerical results show that the new method is more efficient.

Key concepts: Trust region, Hessian matrix, Mathematical optimization, Convergence (economics), Mathematics, Scalar (mathematics), Optimization problem, Computer science

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