2-循环系数矩阵对称MSOR法收敛的充分必要条件

讨论了A为2-循环系数矩阵的线性方程组AX=b的对称MSOR迭代求解问题.在系数矩阵A为2-循环系数矩阵且相应的Jacobi迭代矩阵的特征值为实数或纯虚数时对称MSOR法收敛的充分必要条件,并举例说明所得结果的优点.     

2-循环系数矩阵对称MSOR法最优参数估计

讨论了A为2-循环系数矩阵的线性方程组AX=b的对称MSOR迭代求解问题.在线性方程组AX=b的系数矩阵为2-循环系数矩阵且Jacobi迭代矩阵的特征值都是实数或纯虚数的情况下,估计对称MSOR方法的最优参数,且举例说明所得的结果.     

PSD迭代法收敛的一个充分条件

讨论了线性方程Ax=b的PSD迭代求解问题.在系数矩阵A为相客次序矩阵且A的Jacobi迭代矩阵的特征值μ_j=β_ji,β_j∈R且0<|β_j|<1的条件下得到PSD收敛的一个充分条件,并给出数值例子.     

Improvement of red-black parallel algorithm

红黑排序混合算法包括Jacobi迭代混合算法、CG迭代混合算法和GMERS混合算法等.为加快收敛速度,对方法——Jacobi迭代混合算法的迭代矩阵I-A做了改进,用D-1(D—A)(D为A的对角矩阵)代替.在保持并行性的基础上,减少了迭代次数,节省了运行时间.数值实验的结果显示了改进的算法有更快的收敛速度.     

高阶2PPJ迭代收敛的充要条件

探讨线性方程组Ax=b的高阶2PPJ迭代收敛的充要条件。假设系数矩阵A的Jacobi矩阵特征值的平方为零或纯虚数,利用A的Jacobi矩阵特征值与高阶2PPJ迭代矩阵的关系,结合外插迭代引理,进一步给出此类方程组高阶2PPJ迭代收敛的充要条件。给出了2个数例对结论加以验证。     

红黑并行算法的改进

红黑排序混合算法包括Jacobi迭代混合算法、CG迭代混合算法和GMERS混合算法等.为加快收敛速度,对方法——Jacobi迭代混合算法的迭代矩阵I-A做了改进,用D-1（D-A）（D为A的对角矩阵）代替.在保持并行性的基础上,减少了迭代次数,节省了运行时间.数值实验的结果显示了改进的算法有更快的收敛速度.     

相容次序矩阵SAOR方法收敛的充要条件

讨论了A为大型稀疏非奇异矩阵的线性方程组Ax=b的SAOR迭代求解问题.在系数矩阵为对角元素非零的相容次序矩阵且相应的Jacobi迭代矩阵的特征值都是实数的情况下,得到了SAOR方法收敛的充要条件.     

相容次序矩阵AOR迭代收敛的充要条件

讨论了A为大型稀疏非奇异矩阵的线性方程组Ax=b的AOR迭代求解问题.在系数矩阵A为对角元素非零的(1,1)相容次序矩阵且其相应的Jacobi矩阵的特征值的平方数均为纯虚数或零的情况下,得到了AOR方法收敛的充要条件.并给出一个数值例子对结论作以说明.     

(1,2)相容次序矩阵SOR迭代法的敛散性

当线性方程组Ax=b的系数矩阵A为(1,2)相容次序矩阵时,将几何和代数方法相结合,讨论了SOR迭代法分别在Jacobi迭代矩阵的所有特征值的3次幂非正和非负情况下的敛散性.最后得到了在Jacobi迭代矩阵所有特征值的3次幂为实数时,SOR迭代法的敛散区间并以实例说明,其中A∈Cn×n,x∈Cn,b∈Cn.     

一类预条件矩阵USSOR迭代方法的比较定理

为了提高线性方程组迭代法的收敛速度,采用适当的预处理方法是必要的,即PAx=Pb.利用新预条件矩阵P=I+C′α,当系数矩阵A为非奇异M-矩阵时,运用USSOR迭代方法及矩阵分裂理论,获得了新的比较定理.最后通过数值例子验证了所得的主要结论.     

Solving large mixed linear models using preconditioned conjugate gradient iteration

Continuous evaluation of dairy cattle with a random regression test-day model requires a fast solving method and algorithm. A new computing technique feasible in Jacobi and conjugate gradient based iterative methods using iteration on data is presented. In the new computing technique, the calculations in multiplication of a vector by a matrix were recorded to three steps instead of the commonly used two steps. The three-step method was implemented in a general mixed linear model program that used preconditioned conjugate gradient iteration. Performance of this program in comparison to other general solving programs was assessed via estimation of breeding values using univariate, multivariate, and random regression test-day models. Central processing unit time per iteration with the new three-step technique was, at best, one-third that needed with the old technique. Performance was best with the test-day model, which was the largest and most complex model used. The new program did well in comparison to other general software. Programs keeping the mixed model equations in random access memory required at least 20 and 435% more time to solve the univariate and multivariate animal models, respectively. Computations of the second best iteration on data took approximately three and five times longer for the animal and test-day models, respectively, than did the new program. Good performance was due to fast computing time per iteration and quick convergence to the final solutions. Use of preconditioned conjugate gradient based methods in solving large breeding value problems is supported by our findings.     

Solving large test-day models by iteration on data and preconditioned conjugate gradient

A preconditioned conjugate gradient method was implemented into an iteration on a program for data estimation of breeding values, and its convergence characteristics were studied. An algorithm was used as a reference in which one fixed effect was solved by Gauss-Seidel method, and other effects were solved by a second-order Jacobi method. Implementation of the preconditioned conjugate gradient required storing four vectors (size equal to number of unknowns in the mixed model equations) in random access memory and reading the data at each round of iteration. The preconditioner comprised diagonal blocks of the coefficient matrix. Comparison of algorithms was based on solutions of mixed model equations obtained by a single-trait animal model and a single-trait, random regression test-day model. Data sets for both models used milk yield records of primiparous Finnish dairy cows. Animal model data comprised 665,629 lactation milk yields and random regression test-day model data of 6,732,765 test-day milk yields. Both models included pedigree information of 1,099,622 animals. The animal model ?random regression test-day model? required 122 ?305? rounds of iteration to converge with the reference algorithm, but only 88 ?149? were required with the preconditioned conjugate gradient. To solve the random regression test-day model with the preconditioned conjugate gradient required 237 megabytes of random access memory and took 14% of the computation time needed by the reference algorithm.     

Jacobi矩阵特征值的并行算法

提出了并行求解实三对角矩阵特征值方法,该方法主要针对Jacobi矩阵.应用求多项式根的Sturm法,将矩阵特征多项式的求根区间隔离成单根区间;对已隔离出的单根区间先用二分法求解,达到一定精度后再用牛顿法精确求解.考虑到处理机负载平衡问题,将求根区间分成若干等分,然后按区间循环地将其分给各个处理机.各处理机并行地进行求根...     

牛顿迭代法收敛速度分析

根据一阶导数的性质,讨论了在一些相对弱的条件下牛顿迭代法的超线性收敛性,得到了迭代法的收敛因子.在Visual C 环境下的数值实验表明,近似效果良好.     

稀疏线性方程组的一种预处理并行算法

给出了一种求解系数矩阵为稀疏对称正定矩阵的线性方程组的预处理共轭梯度法的并行算法.该方法提出了迭代法的预处理模式.基于此思想,首先给出预条件子M,然后构造并行迭代求解预处理方程组的迭代格式,进而使用共轭梯度法并行求解.通过数值试验,与直接使用共轭梯度法及传统的预处理共轭梯度方法（迭代1次）相比,该方法提高了收敛速度,同时具有很好的并行性.