A line search trust-region algorithm with nonmonotone adaptive radius for a system of nonlinear equations (original) (raw)
Related papers
A Nonmonotone trust region method with adaptive radius for unconstrained optimization problems
Computers & Mathematics with Applications, 2010
In this paper, we incorporate a nonmonotone technique with the new proposed adaptive trust region radius (Shi and Guo, 2008) [4] in order to propose a new nonmonotone trust region method with an adaptive radius for unconstrained optimization. Both the nonmonotone techniques and adaptive trust region radius strategies can improve the trust region methods in the sense of global convergence. The global convergence to first and second order critical points together with local superlinear and quadratic convergence of the new method under some suitable conditions. Numerical results show that the new method is very efficient and robustness for unconstrained optimization problems.
An Improved Adaptive Trust-Region Method for Unconstrained Optimization
Mathematical Modelling and Analysis, 2014
In this study, we propose a trust-region-based procedure to solve unconstrained optimization problems that take advantage of the nonmonotone technique to introduce an efficient adaptive radius strategy. In our approach, the adaptive technique leads to decreasing the total number of iterations, while utilizing the structure of nonmonotone formula helps us to handle large-scale problems. The new algorithm preserves the global convergence and has quadratic convergence under suitable conditions. Preliminary numerical experiments on standard test problems indicate the efficiency and robustness of the proposed approach for solving unconstrained optimization problems.
An efficient adaptive trust-region method for systems of nonlinear equations
International Journal of Computer Mathematics, 2014
The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
A new nonmonotone adaptive trust region algorithm
Applications of Mathematics, 2021
In this paper, we propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstrained optimization problems. This algorithm incorporates two novelties: it benefits from a radius dependent shrinkage parameter for adjusting the trust region radius that avoids undesirable directions and it exploits a new strategy to prevent sudden increments of objective function values in nonmonotone trust region techniques. Global convergence of this algorithm is investigated under some mild conditions. Numerical experiments demonstrate the efficiency and robustness of the proposed algorithm in solving a collection of unconstrained optimization problems from the CUTEst package.
An adaptive nonmonotone trust-region method with curvilinear search for minimax problem
In this paper we propose an adaptive nonmonotone algorithm for minimax problem. Unlike traditional nonmonotone method, the nonmonotone technique applied to our method is based on the nonmonotone technique proposed by Zhang and Hager [H.C. Zhang, W.W. Hager, A nonmonotone line search technique and its application to unconstrained optimization, SIAM J. Optim. 14 1043-1056] instead of that presented by Grippo et al. [L. Grippo, F. Lampariello, S. Lucidi, A nonmonotone line search technique for Newton's method, SIAM J. Numer. Anal. 23(4)(1986) 707-716]. Meanwhile, by using adaptive technique, it can adaptively perform the nonmonotone trust-region step or nonmonotone curvilinear search step when the solution of subproblems is unacceptable. Global and superlinear convergences of the method are obtained under suitable conditions. Preliminary numerical results are reported to show the effectiveness of the proposed algorithm.
A nonmonotone trust-region line search method for large-scale unconstrained optimization
Applied Mathematical Modelling, 2012
We consider an efficient trust-region framework which employs a new nonmonotone line search technique for unconstrained optimization problems. Unlike the traditional nonmonotone trust-region method, our proposed algorithm avoids resolving the subproblem whenever a trial step is rejected. Instead, it performs a nonmonotone Armijo-type line search in direction of the rejected trial step to construct a new point. Theoretical analysis indicates that the new approach preserves the global convergence to the first-order critical points under classical assumptions. Moreover, superlinear and quadratic convergence are established under suitable conditions. Numerical experiments show the efficiency and effectiveness of the proposed approach for solving unconstrained optimization problems.
A hybrid of adjustable trust-region and nonmonotone algorithms for unconstrained optimization
Applied Mathematical Modelling, 2014
This study devotes to incorporating a nonmonotone strategy with an automatically adjusted trust-region radius to propose a more efficient hybrid of trust-region approaches for unconstrained optimization. The primary objective of the paper is to introduce a more relaxed trust-region approach based on a novel extension in trust-region ratio and radius. The next aim is to employ stronger nonmonotone strategies, i.e. bigger trust-region ratios, far from the optimizer and weaker nonmonotone strategies, i.e. smaller trust-region ratios, close to the optimizer. The global convergence to first-order stationary points as well as the local superlinear and quadratic convergence rates are also proved under some reasonable conditions. Some preliminary numerical results and comparisons are also reported.
A New Modified TR Algorithm with Adaptive Radius to Solve a Nonlinear Systems of Equations
IOP Publishing, 2021
The trust region method (TRM) is a very important technique to solve both of linear and nonlinear systems of equations. In this work, a new modified algorithm of a TRM with adaptive radius is introduced in purpose of solving systems of nonlinear equations. At each iteration, the new algorithm changes the trust region radius (TRR) automatically to reduce the subproblems resolving number when the current radius is rejected. The global convergence results of the new procedure under some appropriate conditions is established. The numerical effects indicate that the suggested algorithm is interesting and robustness.
IRJET-A New Non-monotonic Self-adaptive Trust Region Algorithm with Non-monotonic Line Search
We consider an efficient trust-region framework which employs a new non-monotone line search technique for unconstrained optimization problems. Unlike the traditional non-monotonic trust-region method, the new point is given by the non-monotonic Wolfe line search at each iteration, and the trust region radius is updated at a variable rate. The new algorithm solves the trust region sub-problem only once at each iteration. Under certain conditions, the global convergence of the algorithm is proved.
Asia-Pacific Journal of Operational Research, 2011
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.