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In this paper, a new wide-neighborhood path-following algorithms for uniform P- function nonlinear complementary problems is presented, and its convergence and computational complexity is discussed. The results indicate that the algorithm is a polynomial-time one.

对一致P-函数非线性互补问题,提出了一种新的宽部域N(上标-下标∞路径跟踪算法,并讨论了该算法的收敛性及计算复杂性,分析结果表明,所给方法是一多项式时间算法。

Finally, the numerical results show this class of conjugate gradient methods is very efficient.

然后证明三种新算法在强Wolfe线搜索下的全局收敛性,数值验证表明这类共轭梯度法是比较有效的。

In this paper a class of new conjugate gradient methods are presented, with which the global convergence with generalized Wolfe line search is proven.

本文提出一族新的共轭梯度法,证明了其在推广的Wolfe非精确线搜索条件下具有全局收敛性。

The paper considers applying generalized pattern search methods to linear equality constrained minimization and analyzes the global convergence.

本文的主要内容是对广义模式搜索算法关于线性等式约束最优化问题的全局收敛性做了一些研究。

Because the effect between flow field and stress field will never continue forever,it is important to ensure the constringency of the result.

由于实际工程渗流场和应力场在一定的条件下趋于平衡,所以在编程时要求确保结果具有很好的收敛性。

A reinforcement learning algorithm based on linear average is proposed, which is used to solve nonconvergent problems of reinforcement learning function approximation in continuous state space.

提出了一种基于最小线性平均的强化学习算法,用于解决连续空间下强化学习函数估计的非收敛性问题。

A reinforcement learning algorithm based on linear average is proposed, which is used to solve non-convergent problems of reinforcement learning function approximation in continuous state space.

提出了一种基于最小线性平均的强化学习算法,用于解决连续空间下强化学习函数估计的非收敛性问题。

This approach was to replace the derivative operator in the continuous time model by an approximation and estimation of the physical parameters of structures by the least-squares method.

讨论了导数近似值的选择方法,并分析了算法的收敛性。

Furthermore, without any assumption, the global convegence is proved.

在不需要任何假设的情况下,证明了算法的全局收敛性。

The former need establish an asymptotic sequence, research domain, convergence and limit of the sequence.

Picard逼近法的要点是建立一个逼近序列,然后考察这个序列取值范围、一致收敛性和极限的存在唯一性。

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这一模式非常关注商人的网络信用基础。

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