Local convergence and local superlinear convergence rate are proved.
证明了方法的局部收敛性和局部超线性收敛性。
The superlinear convergence for some special cases is also discussed.
本文还讨论了特殊情况下算法的超线性收敛性。
As a result, the proposed algorithm has global and superlinear convergence.
作为结果，算法具有全局和超线性收敛性。
We also prove that the method has superlinear convergence rate under some mild conditions.
另外在较弱的条件下，证明该方法具有超线性收敛性。
The global and superlinear convergence of the method is obtained under very mild conditions.
在较弱的条件下，证明了算法的全局收敛性。
Under mild conditions, we establish the global and superlinear convergence results for the method.
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。
We prove that the method possesses the global and superlinear convergence under suitable conditions.
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。
Under the general assumption, the algorithm of global convergence and superlinear convergence are proved.
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。
The global convergence and superlinear convergence results of algorithm are novel proved under proper conditions.
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。
It was proved that, when the objective function was uniformly convex, this algorithm possessed superlinear convergence.
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。
The global convergence and local superlinear convergence of the method are established by introducing new approximation techniques.
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。
Under mild conditions, we prove that the global convergence and superlinear convergence of our algorithm under suitable conditions.
在适当的条件下，该算法也具有收敛性和超线性收敛性。
A new nonmonotonic trust region method is given in this paper. And its global convergence and superlinear convergence are proved. Numerical results are given.
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。
Under mild assumptions without the strict complementarity, it is shown that the proposed algorithm enjoys the properties of global and superlinear convergence.
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 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.
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。
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.
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。
The local superlinear and quadratic convergence of this two models under some mild conditions without the strict complementary condition are analysed and proved.
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。
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.
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。
The proposed methods are proved to possess the superlinear and quadratic convergence.
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.
该方法被证明具有超线性和二次收敛性。
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