超线性收敛

证明了这些修正的算法具有全局收敛性且保持局部二步Q超线性收敛。

It is proved that the improved algorithms possess global and two step Q superlinear convergence properties .

本文提出一个求解非线性minimax问题的新算法，该算法具有全局和一步超线性收敛性。

In this paper , a new algorithm for nonlinear minimax problems is presented which has global and one - step superlinear convergence .

前馈网络的一种超线性收敛BP学习算法

Super-Linearly Convergent BP Learning Algorithm for Feedforward Neural Networks

Minimax问题的一个超线性收敛的SQP算法

A superlinearly convergent SQP algorithm for Minimax Problems

证明了这种方法是q-超线性收敛的和大范围收敛的，并给出了与Newton法的数值比较。

The method is shown to be q-superlinear convergent and global convergent , and the numerical comparison with Newton method is given .

约束优化无严格互补的超线性收敛SQP强次可行算法

A Superlinearly Convergent Strongly Sub-Feasible SQP Algorithm for Constrained Optimization Without Strict Complementarity

等式约束优化问题SQP算法的超线性收敛充要条件

Sufficient and Essential Conditions of Super-linear Convergence for SQP Algorithm in Equality Constrained Optimization Proble

在F为一致P-函数情形，证明了算法的全局收敛性、局部超线性收敛性和二次收敛性，对于非退化的线性互补问题仍具有有限步收敛性。

Global convergence , local superlinear convergence , quadratic convergence and finite termination for nondegenerative linear complementarity problems are proved under the assumption that F is a uniform P & function .

一类修正BFGS算法的局部超线性收敛性

Local Q-superlinear Convergence of a Modified BFGS Algorithm

在第一章中，对具有一般约束的非线性规划构造出新的具有超线性收敛性的SQP算法。

In the first chapter , introduces a new SQP algorithm for nonlinear optimization with equality and inequality constraint .

并且，算法使用BFGS拟牛顿方法更新矩阵Bk，不需要函数是一致凸的假设条件，我们证明该方法具有全局收敛性和超线性收敛性。

Furthermore , we use BFGS method to update matrix B_k , without the assumption condition of f is uniformly convex , we show that the method is globally and superlinearly convergent .

利用一个修正的BFGS公式，提出了结合线搜索技术的BFGS-信赖域方法，并在一定条件下证明了该方法的全局收敛性和超线性收敛性。

By using a modified BFGS formula , a BFGS-type trust region method with line search technique for unconstrained optimization problems is proposed .

本文将集中讨论局部收敛性，特别是证明了在使用DFP或PSB等矩阵校正公式时，修正后的方法在一定的条件下是超线性收敛的。

Particularly , it is proved that if DFP or PSB matrix updating formulae are used , then our method will be convergent superlinearly under some conditions .

在合理的条件下，算法具有整体收敛性且两块校正的双边既约Hessian投影法将保持超线性收敛速率。

The global convergence results of the proposed algorithm are proved while maintaining fast local superlinear convergence rate is established by performing a two-piece update of two-side projected reduced Hessian .

D-F-P方法的超线性收敛

Superlinear convergence of the d-f-p method

在第四章，我们考虑约束规划的一个基于SQP方法的信赖域算法，证明了它的全局收敛性和超线性收敛。

Both of them deal with the equality constrained optimization . In Chapter ⅳ, a trust-region algorithm based on SQP method is given , its global convergence and superlinear convergence are proved .

文中对非线性最小二乘问题给出了一种新型求解方法&分块尺度化ABS求解方法，并证明了这类方法的局部超线性收敛性，最后在尺度矩阵取特殊值时给出了数值结果。

As a new type of method for nonlinear least square problems , the block scald ABS meth-ods are presented , and their locally superlinear convergence is proved . Finally the numerical re-sults are given with the special choice of the sealed matrices .

提出一个求解LC1无约束优化问题的信赖域算法，在较弱条件下证明了全局收敛性和超线性收敛性。

A trust region method for LC ~ 1 unconstrained problems was presented and under mild conditions we proved its global and superlinear convergene .

该算法全局和超线性收敛并且去掉了传统SQCQP算法全局收敛性分析中的一致正定性假设。

The algorithm is globally and superlinearly convergent , and the uniformly positive definiteness assumption in the global convergence analysis of traditional SQCQP algorithms is removed .

本文采用Wolfe线性搜索原则来替代该BFGS-SQP算法的Armijo原则，经过类似的分析，同样得到了BFGS-SGP算法的全局收敛性及超线性收敛性。

In this paper , the Wolfe line search is used to replace the Armijo line search in the BFGS-SQP algorithm , through the same analysis as Chen , the algorithm even has global and q-super-linear convergence .

Powell在文献[1]中证明了D-F-P方法是超线性收敛的，Dennis等人在文献[2]也给出了同样的结果，但是证明方法不同。

The D-F-P method was proved to be superlinear convergence in reference [ 1 ] by Powell . Same result was given in reference [ 2 ] by Dennis and More ' but the methods used were different between reference [ 1 ] and [ 2 ] .

非线性约束最优化一族超线性收敛的可行方法

A Class of Superlinearly Convergent Feasible Methods for Nonlinearly Constrained Optimizations

均衡约束优化具有超线性收敛性算法的研究

Research on the Superlinearly Convergent Algorithms for Optimization with Equilibrium Constraints

非线性规划的一个超线性收敛算法

An Algorithm with Superlinear Rate of Convergence for Nonlinear Programming

通过二阶校正，还可以得到局部超线性收敛性。

Furthermore , by second order correction , local convergence is proved .

在一定条件下算法具有全局收敛性和超线性收敛性。

Global and superlinear convergence can be induced under some suitable assumptions .

证明了此法具有全局收敛性和局部超线性收敛性。

The authors also prove that the method has superlinear convergence rate .

非线性约束条件下一个超线性收敛的可行方法

A Superlinear Convergent Feasible Method for Nonlinear Programming with Nonlinear Inequality Constraints

在适当的条件下，该法是超线性收敛的。

The superlinear convergence is obtained under the suitable conditions .

线性互补约束优化问题一个超线性收敛的序列线性方程组算法

A Superlinearly Convergent SSLE Algorithm for Optimization Problem with Linear Complementarity Constraints