作为结果，算法具有全局和超线性收敛性。
As a result, the proposed algorithm has global and superlinear convergence.
本文还讨论了特殊情况下算法的超线性收敛性。
The superlinear convergence for some special cases is also discussed.
证明了方法的局部收敛性和局部超线性收敛性。
Local convergence and local superlinear convergence rate are proved.
另外在较弱的条件下，证明该方法具有超线性收敛性。
We also prove that the method has superlinear convergence rate under some mild conditions.
在适当的条件下，该算法也具有收敛性和超线性收敛性。
Under mild conditions, we prove that the global convergence and superlinear convergence of our algorithm under suitable conditions.
证明该算法在目标函数为一致凸时具有局部超线性收敛性。
It was proved that, when the objective function was uniformly convex, this algorithm possessed superlinear convergence.
在一般假设条件下，证明了算法的全局收敛性和超线性收敛性。
Under the general assumption, the algorithm of global convergence and superlinear convergence are proved.
在适当的条件下我们将证明此方法的全局收敛性和超线性收敛性。
We prove that the method possesses the global and superlinear convergence under suitable conditions.
由于引进了新的逼近技术，该方法具有全局收敛性和局部超线性收敛性。
The global convergence and local superlinear convergence of the method are established by introducing new approximation techniques.
在适当的假设条件下，我们证明了算法具有全局收敛性和超线性收敛性。
Under mild conditions, we establish the global and superlinear convergence results for the method.
在适当的条件下，比较新颖的证明了算法的全局收敛性及超线性收敛性。
The global convergence and superlinear convergence results of algorithm are novel proved under proper conditions.
此外，在不需要严格互补的温和条件下，我们证明了算法的全局收敛性和超线性收敛性。
Under mild assumptions without the strict complementarity, it is shown that the proposed algorithm enjoys the properties of global and superlinear convergence.
详细分析和论证两个模型的局部超线性收敛性及二次收敛性条件，其中并不需要严格互补条件。
The local superlinear and quadratic convergence of this two models under some mild conditions without the strict complementary condition are analysed and proved.
给出一种新的非单调信赖域方法，证明了算法的全局收敛性和超线性收敛性，最后给出了数值结果。
A new nonmonotonic trust region method is given in this paper. And its global convergence and superlinear convergence are proved. Numerical results are given.
在目标函数为一致凸函数的假设条件下，证明了LRKOPT方法的具有全局收敛和局部超线性收敛性。
Under the assumption condition of taking target function as an uniform convex function. We have proved that the LRKOPT has the global convergence and partial superlinear convergence.
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.
该方法被证明具有超线性和二次收敛性。
The proposed methods are proved to possess the superlinear and quadratic convergence.
研究球形约束变分不等式求解的算法，提出一种光滑化牛顿方法，证明了该方法具有全局收敛性和超线性收敛。
In this paper we present a smoothing Newton method for solving ball constrained variational inequalities. Global and superlinear convergence theorems of the proposed method are established.
在一定的假设条件下，证明了该算法的全局收敛性和超线性收敛。
Under some conditions, the global convergence and the super-linear convergence are proven.
研究了一类超定非线性方程组的牛顿迭代法的收敛性。
The convergence properties of Newton's method for a type of overdetermined systems of equations were studied.
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.
提出复合非光滑优化问题的一类算法，并证明这种算法保持全局收敛性且敛速达到超线性。
This paper discusses a model algorithm for composite nonsmooth optimization problems and proves that the algorithm holds global convergence and in the meantime the convergent rate is superlinear.
证明了此方法的全局收敛性，并给出了它在一定条件下的超线性收敛的结果。
The global convergence results are given for the nonmonotonic trust region technique. Furthermore, the proposed algorithm is superlinearly convergent under a certain growth condition.
在通常条件下，证明了全局收敛性及局部超线性收敛结果，数值结果验证了新方法的有效性。
Under general conditions, the local and global convergence results of the new method are proved. Numerical experiments show that the new method is very efficient.
在通常条件下，证明了全局收敛性及局部超线性收敛结果，数值结果验证了新方法的有效性。
Under general conditions, the local and global convergence results of the new method are proved. Numerical experiments show that the new method is very efficient.
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