收敛速率

Our calculation shows that qa decreasing with the simulationtime can accelerate the convergence significantly,which is somehow similar tointroducing different temperatures for visiting and acceptance probability.

为了增加收敛速率，让原来固定不动的qa随计算机时间作线性下降，对粒子数N＝12，51的Thomson问题的测试表明我们的改进效果是很明显的。qa随计算机时间作线性下降类似于对接收几率引入了单独的温度，这个温度不同于跃迁温度。

Its global convergence and linear convergence rate were proved under some mild conditions.

提出一类新的求解无约束优化问题的记忆梯度法，在较弱条件下证明了算法具有全局收敛性和线性收敛速率。

We prove general convergence and local convergence of this algorithm.

第四章证明了对偶算法的全局收敛性和局部收敛速率。

This method has the advantage of the well-known Newton's method, considered by, thus it does not need to solve linear equations.

3.4.6分别给出了它在各种条件下的收敛性的证明，并估计了收敛速率。

Based on it, a new efficient implementation of preconditioned conjugate gradient is put forward to solve the Gauss-Newton equation and save the cost on computation with the same exactly quadratic convergence to the traditional choleski factorization.

新算法与传统的使用Choleski技术的Gauss-Newton法具有相同的收敛速率，但在求解 Gauss-Newton方程组时减少了代数运算的计算量。

When the diffusion matrix is symmetric and locally uniform positive definite, we prove that the solution tends to the thermal equilibrium state exponentially in L〓 norm and relative entropy.

当扩散矩阵对称且局部一致正定时，利用熵方法证明了其解在L〓或相对熵意义下指数衰减到热平衡状态，并且能明确的计算出收敛速率。

In the paper, the methods above are studied for element solve singular problems the convergence of the method is proved and the error estimate is also obtained.

本文将讨论零空间为一维情况下Chebyshev 方法求解奇异问题的收敛性，得到了相应的渐近收敛速率。4。

Thirdly, under the condition with noαpriori smoothness, in order to obtain the optimal convergence rates, it is not practical to use theαpriori rule. And it is necessary to use the a posteriori stopping rule which not only depends on the error boundδbut also on the perturbed data y~δ.

再次，在没有先验的光滑性信息情况下，为了获得最优收敛速率，利用先验选取准则则变得不再实用，而利用不仅依赖于误差界δ而且依赖于扰动数据y~δ的后验停止准则是必要的。

The convergence rate and stational error of the proposed algorithm are compared with those of other constant modulus algorithms by computer simulation.

在最后的数据仿真中，与基本CMA等算法在收敛速率、稳态误差等方面进行了对比分析，验证了新算法的有效性。

Simulation results indicate that this method is effective,multiwavelet neural network converges more rapidly than wavelet neural network.

仿真结果表明，该方法是有效的，而且比小波神经网络方法的收敛速率快。

What are your goals and strategies for growth?

你的成长目标和策略是什么？

And unto the angel of the church in Sardis write; These things saith he that hath the seven irits of God, and the seven star I know thy works, that thou hast a name that thou livest, and art dead.

3：1 你要写信给撒狄教会的使者，说，那有神的七灵和七星的，说，我知道你的行为，按名你是活的，其实是死的。