共查询到20条相似文献,搜索用时 10 毫秒
1.
Ying Ji Yijun Li Xinli Zhang 《Journal of Computational and Applied Mathematics》2010,233(8):1746-1754
In this paper, we present a new nonmonotone trust-region method of conic model for solving unconstrained optimization problems. Both the local and global convergence properties are analyzed under reasonable assumptions. Numerical experiments are conducted to compare this method with some existed ones which indicate that the new method is efficient. 相似文献
2.
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. 相似文献
3.
In this paper, we present a nonmonotone adaptive trust region method for unconstrained optimization based on conic model. The new method combines nonmonotone technique and a new way to determine trust region radius at each iteration. The local and global convergence properties are proved under reasonable assumptions. Numerical experiments show that our algorithm is effective. 相似文献
4.
In this paper, we present a nonmonotone conic trust region method based on line search technique for unconstrained optimization. The new algorithm can be regarded as a combination of nonmonotone technique, line search technique and conic trust region method. When a trial step is not accepted, the method does not resolve the trust region subproblem but generates an iterative point whose steplength satisfies some line search condition. The function value can only be allowed to increase when trial steps are not accepted in close succession of iterations. The local and global convergence properties are proved under reasonable assumptions. Numerical experiments are conducted to compare this method with the existing methods. 相似文献
5.
《Applied Mathematical Modelling》2014,38(9-10):2601-2612
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. 相似文献
6.
In this paper, we propose and analyze a new conic trust-region algorithm for solving the unconstrained optimization problems. A new strategy is proposed to construct the conic model and the relevant conic trust-region subproblems are solved by an approximate solution method. This approximate solution method is not only easy to implement but also preserves the strong convergence properties of the exact solution methods. Under reasonable conditions, the locally linear and superlinear convergence of the proposed algorithm is established. The numerical experiments show that this algorithm is both feasible and efficient. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
7.
The aim of this paper is to incorporate the preconditioned gradient path in a nonmonotone stabilization algorithm for unconstrained optimization. The global convergence and locally superlinear convergence are established for this class of algorithms. Finally, we report in details the numerical results which show the effectiveness of the proposed algorithm. 相似文献
8.
Lijuan ZHAO Wenyu SUN Raimundo J. B. de SAMPAIO 《Frontiers of Mathematics in China》2014,9(5):1211-1238
We propose a nonmonotone adaptive trust region method based on simple conic model for unconstrained optimization. Unlike traditional trust region methods, the subproblem in our method is a simple conic model, where the Hessian of the objective function is approximated by a scalar matrix. The trust region radius is adjusted with a new self-adaptive adjustment strategy which makes use of the information of the previous iteration and current iteration. The new method needs less memory and computational efforts. The global convergence and Q-superlinear convergence of the algorithm are established under the mild conditions. Numerical results on a series of standard test problems are reported to show that the new method is effective and attractive for large scale unconstrained optimization problems. 相似文献
9.
M.A. Diniz-Ehrhardt J.M. Martínez M. Raydan 《Journal of Computational and Applied Mathematics》2008,219(2):383
A tolerant derivative–free nonmonotone line-search technique is proposed and analyzed. Several consecutive increases in the objective function and also nondescent directions are admitted for unconstrained minimization. To exemplify the power of this new line search we describe a direct search algorithm in which the directions are chosen randomly. The convergence properties of this random method rely exclusively on the line-search technique. We present numerical experiments, to illustrate the advantages of using a derivative-free nonmonotone globalization strategy, with approximated-gradient type methods and also with the inverse SR1 update that could produce nondescent directions. In all cases we use a local variation finite differences approximation to the gradient. 相似文献
10.
11.
In this paper, an adaptive nonmonotone line search method for unconstrained minimization problems is proposed. At every iteration,
the new algorithm selects only one of the two directions: a Newton-type direction and a negative curvature direction, to perform
the line search. The nonmonotone technique is included in the backtracking line search when the Newton-type direction is the
search direction. Furthermore, if the negative curvature direction is the search direction, we increase the steplength under
certain conditions. The global convergence to a stationary point with second-order optimality conditions is established. Some
numerical results which show the efficiency of the new algorithm are reported.
相似文献
12.
Wu Qing-jun 《Applied mathematics and computation》2010,217(8):4274-4281
In this paper, a nonmonotone trust region algorithm for unconstrained optimization problems is presented. In the algorithm, a kind of nonmonotone technique, which is evidently different from Grippo, Lampariello and Lucidi’s approach, is used. Under mild conditions, global and local convergence results of the algorithm are established. Preliminary numerical results show that the new algorithm is efficient. 相似文献
13.
A quasi-Newton trust-region method 总被引:1,自引:0,他引:1
The classical trust-region method for unconstrained minimization can be augmented with a line search that finds a point that satisfies the Wolfe conditions. One can use this new method to define an algorithm that simultaneously satisfies the quasi-Newton condition at each iteration and maintains a positive-definite approximation to the Hessian of the objective function. This new algorithm has strong global convergence properties and is robust and efficient in practice. 相似文献
14.
The monotone trust-region methods are well-known techniques for solving unconstrained optimization problems. While it is known
that the nonmonotone strategies not only can improve the likelihood of finding the global optimum but also can improve the
numerical performance of approaches, the traditional nonmonotone strategy contains some disadvantages. In order to overcome
to these drawbacks, we introduce a variant nonmonotone strategy and incorporate it into trust-region framework to construct
more reliable approach. The new nonmonotone strategy is a convex combination of the maximum of function value of some prior
successful iterates and the current function value. It is proved that the proposed algorithm possesses global convergence
to first-order and second-order stationary points under some classical assumptions. Preliminary numerical experiments indicate
that the new approach is considerably promising for solving unconstrained optimization problems. 相似文献
15.
《Applied Mathematical Modelling》2014,38(11-12):3003-3015
This study presents a new trust-region procedure to solve a system of nonlinear equations in several variables. The proposed approach combines an effective adaptive trust-region radius with a nonmonotone strategy, because it is believed that this combination can improve the efficiency and robustness of the trust-region framework. Indeed, it decreases the computational cost of the algorithm by decreasing the required number of subproblems to be solved. The global and the quadratic convergence of the proposed approach is proved without any nondegeneracy assumption of the exact Jacobian. Preliminary numerical results indicate the promising behavior of the new procedure to solve systems of nonlinear equations. 相似文献
16.
This paper presents a quadratically converging algorithm for unconstrained minimization. All the accumulation points that it constructs satisfy second-order necessary conditions of optimality. Thus, it avoids second-order saddle andinflection points, an essential feature for a method to be used in minimizing the modified Lagrangians in multiplier methods.The work of the first author was supported by NSF RANN AEN 73-07732-A02 and JSEP Contract No. F44620-71-C-0087; the work of the second author was supported by NSF Grant No. GK-37672 and the ARO Contract No. DAHCO4-730C-0025. 相似文献
17.
18.
Jinhua Fu Wenyu Sun Raimundo J. B. De Sampaio 《Journal of Applied Mathematics and Computing》2005,19(1-2):165-177
In this paper, an adaptive trust region method based on the conic model for unconstrained optimization problems is proposed and analyzed. We establish the global and superlinear convergence results of the method. Numerical tests are reported that confirm the efficiency of the new method. 相似文献
19.
Yigui Ou 《Applied Numerical Mathematics》2011,61(7):900-909
This paper presents a hybrid trust region algorithm for unconstrained optimization problems. It can be regarded as a combination of ODE-based methods, line search and trust region techniques. A feature of the proposed method is that at each iteration, a system of linear equations is solved only once to obtain a trial step. Further, when the trial step is not accepted, the method performs an inexact line search along it instead of resolving a new linear system. Under reasonable assumptions, the algorithm is proven to be globally and superlinearly convergent. Numerical results are also reported that show the efficiency of this proposed method. 相似文献
20.
A truncated Newton method with nonmonotone line search for unconstrained optimization 总被引:7,自引:0,他引:7
L. Grippo F. Lampariello S. Lucidi 《Journal of Optimization Theory and Applications》1989,60(3):401-419
In this paper, an unconstrained minimization algorithm is defined in which a nonmonotone line search technique is employed in association with a truncated Newton algorithm. Numerical results obtained for a set of standard test problems are reported which indicate that the proposed algorithm is highly effective in the solution of illconditioned as well as of large dimensional problems. 相似文献