An affine scaling derivative-free trust region method with interior backtracking technique for bounded-constrained nonlinear programming

Wedge trust region method based on traditional trust region is designed for derivative free optimization problems. This method adds a constraint to the trust region problem, which is called “wedge method”. The problem is that the updating strategy of wedge trust region radius is somewhat simple. In this paper, we develop and combine a new radius updating rule with this method. For most test problems, the number of function evaluations is reduced significantly. The experiments demonstrate the effectiveness of the improvement through our algorithm.

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COMBINATION ADAPTIVE TRUST REGION METHOD BY NON-MONOTONE STRATEGY FOR UNCONSTRAINED NONLINEAR PROGRAMMING

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595911003454 ◽

2011 ◽

Vol 28 (05) ◽

pp. 585-600 ◽

Cited By ~ 2

Author(s):

KEYVAN AMINI ◽

MASOUD AHOOKHOSH

Keyword(s):

Nonlinear Programming ◽

Quadratic Convergence ◽

Optimization Problems ◽

Trust Region Method ◽

Trust Region ◽

Trust Region Algorithm ◽

Region Method ◽

Unconstrained Optimization Problems ◽

Adaptive Technique ◽

And Function

In this paper, we present a new trust region method for unconstrained nonlinear programming in which we blend adaptive trust region algorithm by non-monotone strategy to propose a new non-monotone trust region algorithm with automatically adjusted radius. Both non-monotone strategy and adaptive technique can help us introduce a new algorithm that reduces the number of iterations and function evaluations. The new algorithm preserves the global convergence and has local superlinear and quadratic convergence under suitable conditions. Numerical experiments exhibit that the new trust region algorithm is very efficient and robust for unconstrained optimization problems.

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A Modified Filter Trust Region Method for Nonlinear Programming

2007 Chinese Control Conference ◽

10.1109/chicc.2006.4346770 ◽

2006 ◽

Cited By ~ 1

Author(s):

Su Ke

Keyword(s):

Nonlinear Programming ◽

Trust Region Method ◽

Trust Region ◽

Region Method

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Nonmonotone conic trust region method with line search technique for bound constrained optimization

RAIRO - Operations Research ◽

10.1051/ro/2017054 ◽

2019 ◽

Vol 53 (3) ◽

pp. 787-805

Author(s):

Lijuan Zhao

Keyword(s):

Constrained Optimization ◽

Line Search ◽

Optimization Problems ◽

Trust Region Method ◽

Trust Region ◽

Affine Scaling ◽

Search Technique ◽

Region Method ◽

Bound Constrained Optimization ◽

Line Search Technique

In this paper, we propose a nonmonotone trust region method for bound constrained optimization problems, where the bounds are dealt with by affine scaling technique. Differing from the traditional trust region methods, the subproblem in our algorithm is based on a conic model. Moreover, when the trial point isn’t acceptable by the usual trust region criterion, a line search technique is used to find an acceptable point. This procedure avoids resolving the trust region subproblem, which may reduce the total computational cost. The global convergence and Q-superlinear convergence of the algorithm are established under some mild conditions. Numerical results on a series of standard test problems are reported to show the effectiveness of the new method.

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