一类新的非单调信赖域算法

提出了一类带线性搜索的非单调信赖域算法.算法将非单调Armijo线性搜索技术与信赖域方法相结合,使算法不需重解子问题.而且由于采用了MBFGS校正公式,使矩阵Bk能较好地逼近目标函数的Hesse矩阵并保持正定传递.在较弱的条件下,证明了算法的全局收敛性.数值结果表明算法是有效的.     

Global convergence of the nonmonotone MBFGS method for nonconvex unconstrained minimization

In this paper, we propose a new nonmonotone Armijo type line search and prove that the MBFGS method proposed by Li and Fukushima with this new line search converges globally for nonconvex minimization. Some numerical experiments show that this nonmonotone MBFGS method is efficient for the given test problems.     

Nonmonotone trust region algorithm for unconstrained optimization problems

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.     

一类新的非单调记忆梯度法及其全局收敛性

在非单调Armijo线搜索的基础上提出一种新的非单调线搜索,研究了一类在该线搜索下的记忆梯度法,在较弱条件下证明了其全局收敛性。与非单调Armijo线搜索相比,新的非单调线搜索在每次迭代时可以产生更大的步长,从而使目标函数值充分下降,降低算法的计算量。     

一个新的MBFGS信赖域算法

本文研究了无约束最优化问题.利用MBFGS信赖域算法的基本思想,通过对BFGS校正公式的改进,并结合线搜索技术,提出了一种新的MBFGS信赖域算法,拓宽了信赖域算法的适用范围,并在一定条件下证明了该算法的全局收敛性和超线性收敛性.     

A BFGS trust-region method with a new nonmonotone technique for nonlinear equations

In this paper, we consider a trust-region method for solving nonlinear equations which employs a new nonmonotone technique. A strong nonmonotone strategy and a weaker nonmonotone strategy can be obtained by choosing the parameter adaptively. Thus, the disadvantages of the traditional nonmonotone strategy can be avoided. It does not need to compute the Jacobian matrix at every iteration, so that the workload and time are decreased. Theoretical analysis indicates that the new algorithm preserves the global convergence under classical assumptions. Moreover, superlinear and quadratic convergence are established under suitable conditions. Numerical experiments show the efficiency and effectiveness of the proposed method for solving nonlinear equations.     

求解非线性互补问题的一类光滑Broyden-like方法

非线性互补问题可以等价地转换为光滑方程组来求解. 基于一种新的非单调线搜索准则, 提出了求解非线性互补问题等价光滑方程组的一类新的非单调光滑 Broyden-like 算法.在适当的假设条件下, 证明了该算法的全局收敛性与局部超线性收敛性. 数值实验表明所提出的算法是有效的.     

Global Convergence Analysis of a New Nonmonotone BFGS Algorithm on Convex Objective Functions

In this paper, a new nonmonotone BFGS algorithmfor unconstrained optimization is introduced. Under mild conditions,the global convergence of this new algorithm on convex functions isproved. Some numerical experiments show that this new nonmonotoneBFGS algorithm is competitive to the BFGS algorithm.     

基于非单调技术的ODE型混合方法

基于非单调线搜索技术和IMPBOT算法,提出了一个求解无约束优化问题的ODE型混合方法.该方法的主要特点是：为了求得试验步,该方法在每次迭代时不必求解带信赖域界的子问题,仅需要求解一线性方程组系统;当试验步不被接受时,该方法就执行改进的Wolfe-型非单调线搜索来获得下一个新的迭代点,从而避免了反复求解线性方程组系统. 在一定条件下,所提算法还是整体收敛和超线性收敛的. 数值试验结果表明该方法是有效的.     

采用不定Dogleg路径的非单调自适应信赖域算法解无约束极小化(英文)

In this paper, we combine the nonmonotone and adaptive techniques with trust region method for unconstrained minimization problems. We set a new ratio of the actual descent and predicted descent. Then, instead of the monotone sequence, the nonmonotone sequence of function values are employed. With the adaptive technique, the radius of trust region △k can be adjusted automatically to improve the efficiency of trust region methods. By means of the Bunch-Parlett factorization, we construct a method with indefinite dogleg path for solving the trust region subproblem which can handle the indefinite approximate Hessian Bk. The convergence properties of the algorithm are established. Finally, detailed numerical results are reported to show that our algorithm is efficient.     

非单调广义对角拟牛顿算法

本文研究了无约束最优化的求解问题.利用新的对角拟牛顿校正和非单调技术,获得了一种非单调广义对角拟牛顿算法.新算法具有低存储、低计算量的特点,非常适合大规模问题的求解,推广了文献[8]的结果.     

一类带非单调搜索的SQP算法

本文给出了一个SQP新算法，其特点是使用了非单调搜索，并不再使用严格互补条件，使得算法在一定阶段后具有十分简洁的形式并保持整体收敛与超线性收敛性．     

一类带非单调线搜索的信赖域算法

通过将非单调Wolfe线搜索技术与传统的信赖域算法相结合,我们提出了一类新的求解无约束最优化问题的信赖域算法.新算法在每一迭代步只需求解一次信赖域子问题,而且在每一迭代步Hesse阵的近似都满足拟牛顿条件并保持正定传递.在一定条件下,证明了算法的全局收敛性和强收敛性.数值试验表明新算法继承了非单调技术的优点,对于求解某...     

约束最优化的一个使用非单调线搜索的超线性收敛算法

Abstract. In the paper, a new mixed algorithm combined with schemes of nonmonotone line search, the systems of linear equations for higher order modification and sequential quadratic programming for constrained optimizations is presented. Under some weaker assumptions,without strict complementary condition, the algorithm is globally and superlinearly convergent.     

求解无约束最优化问题的非单调MFR,MPRP方法

本文在求解无约束最优化问题的MFR共轭梯度法和MPRP共轭梯度法中引入两种非单调线性搜索技术.我们证明在适当条件下采用非单调线性搜索的MFR算法和MPRP算法具有全局收敛性.数值结果表明非单调线性搜索具有优越性.     

A nonmonotone line search based algorithm for distribution center location selected

The minimax optimization model introduced in this paper is an important model which has received some attention over the past years. In this paper, the application of minimax model on how to select the distribution center location is first introduced. Then a new algorithm with nonmonotone line search to solve the non-decomposable minimax optimization is proposed. We prove that the new algorithm is global Convergent. Numerical results show the proposed algorithm is effective.     

新的非单调线搜索规则BFGS算法的全局收敛性

本文在Zhang H.C.的非单调线搜索规则的基础上,设计了求解无约束最优化问题的新的非单调线搜索BFGS算法,在一定 的条件下证明了算法的线性收敛性和超线性收敛性分析.数值例子表明算法是有效的.     

A nonmonotone smoothing Newton algorithm for solving general box constrained variational inequalities

In this paper, based on a new smoothing function, the general box constrained variational inequalities are solved by a smoothing Newton algorithm with a nonmonotone line search. The proposed algorithm is proved to be globally and locally superlinearly convergent under suitable assumptions. The preliminary numerical results are reported.     

一种非单调滤子信赖域算法解线性不等式约束优化

本文给出了一种新的多维滤子算法结合非单调信赖域策略解线性约束优化.目标函数及其投影梯度的分量组成了新的多维滤子，并且与信赖域半径有关.当信赖域半径充分小时，新的滤子能接受试探点，避免算法无限循环.非单调信赖域策略保证了新算法的整体收敛性.目前为止，多维滤子算法局部收敛性分析仍然没有解决，在合理假设下，我们分析了新算法的局部超线性收敛性.数值结果验证了算法的有效性.     

不等式约束最优化的一个使用非单调搜索的可行算法

对不等式约束最优化问题本文提出了一个新算法。算法使用了非单调搜索,它不仅放松了每步迭代中对搜索的限制,而且使得算法迭代到一定阶段后具有非常简洁的形式,在不需要严格互补条件的较弱假设下,算法是整体和超线性收敛的。