跳跃扩散下双障碍期权定价的数值解

提出了一种求解带有跳跃的双障碍期权定价模型的数值方法.算法采用了Crank-Nicolson 有限差分格式和复化梯形公式对模型进行离散,对离散后的线性系统采用GMRES迭代法求解,并且构造了一个新的预处理算子以加速迭代法的收敛.数值实验验证了该方法能快速求解模型并达到二阶收敛精度.     

关于鞍点问题的预处理HSS-SOR交替分裂迭代方法

为了高效地求解大型稀疏鞍点问题,在白中治,Golub和潘建瑜提出的预处理对称/反对称分裂(PHss)迭代法的基础上,通过结合SOR-like迭代格式对原有迭代算法进行加速,提出了一种预处理HSS-SOR交替分裂迭代方法,并研究了该算法的收敛性.数值例子表明:通过参数值的选择,新算法比SOR-like和PHSS算法都具有更快的收敛速度和更少的迭代次数,选择了合适的参数值后,可以提高算法的收敛效率.     

守恒型扩散方程非线性离散格式的性质分析和快速求解

对于守恒型扩散方程,研究其二阶时间精度非线性全隐有限差分离散格式的性质,证明了其解的存在唯一性.研究了二阶时间精度的Picard-Newton迭代格式,证明了迭代解对原问题真解的二阶时间和空间收敛性,以及对非线性离散解的二次收敛速度,实现了非线性问题的快速求解.本文中方法也适用于一阶时间精度格式的分析,并可推广至对流扩散问题.数值实验验证了二阶时间精度Picard-Newton迭代格式的高精度和高效率.     

CEV过程下回望期权定价的高精度收敛差分算法

主要研究了CEV过程下一类回望期权的定价的数值解法问题.首先对期权价格所满足的微分方程中的空间变量进行半离散化处理,得到了具体的半离散化差分格式,然后证明了该差分格式具有稳定性和收敛性.数值试验表明本文算法是一个稳定收敛的算法.     

求解非线性算子方程的加速迭代格式

研究非线性算子方程的近似求解方法.首先对通常的求解非线性方程加速迭代格式进行推广,得到高阶收敛速度的加速迭代格式,最后把这种加速迭代格式推广到非线性算子方程的求解中去,利用非线性算子的渐进展开,证明了这种加速格式具有三阶的收敛速度.     

多孔介质一般非Darcy流问题的块中心有限差分算法

本文对描述多孔介质一般非Darcy流的非线性方程,提出一类数值求解的块中心有限差分算法.该格式保持局部质量守恒,并能够同时获得速度和压力近似解.在一般非均匀矩形网格上,本文证明了速度和压力近似在离散l~2模意义下的二阶误差估计.采用该格式进行的数值实验表明,收敛阶与理论分析一致.     

一维非线性Schrödinger 方程的两个无条件收敛的守恒紧致差分格式

本文对一维非线性 Schrödinger 方程给出两个紧致差分格式, 运用能量方法和两个新的分析技 巧证明格式关于离散质量和离散能量守恒, 而且在最大模意义下无条件收敛. 对非线性紧格式构造了 一个新的迭代算法, 证明了算法的收敛性, 并在此基础上给出一个新的线性化紧格式. 数值算例验证 了理论分析的正确性, 并通过外推进一步提高了数值解的精度.     

一维非线性Schrdinger方程的两个无条件收敛的守恒紧致差分格式

本文对一维非线性Schrdinger方程给出两个紧致差分格式,运用能量方法和两个新的分析技巧证明格式关于离散质量和离散能量守恒,而且在最大模意义下无条件收敛.对非线性紧格式构造了一个新的迭代算法,证明了算法的收敛性,并在此基础上给出一个新的线性化紧格式.数值算例验证了理论分析的正确性,并通过外推进一步提高了数值解的精度.     

求解奇异问题一类新的加速迭代法

格式(1.1)每步只需求一次导算子的逆,计算量比现有的加速迭代格式均少,同时具有高阶收敛性。格式(1.2)与文[1]中提出的迭代格式相比,计算量基本相同,但其收敛速度却较快。我们在§2中给出算法(1.1)和(1.2)的收敛性定理及误差估计。对于高阶奇异问题,§3中也给出了相应的加速迭代格式和收敛性定理。§4中给出数值例子。     

广义对称正则长波方程的守恒差分格式

文章考虑了具有齐次边界条件的广义对称正则长波方程的有限差分格式.提出了一个守恒并且线性非耦合的三层有限差分格式,由于格式在计算中只需要解三对角线性方程组,从而避免了其中的迭代计算.文中先讨论了一个离散守恒量,然后我们利用离散泛函分析方法证明了格式的收敛性和稳定性,从理论上得到了收敛阶为O(h~2+τ~2).通过数值试验表明,所提的方法是可靠有效的.     

红黑排序混合算法收敛速度分析

The algorithm of applying the block Gauss elimination to the Red-Black or-dering matrix to reduce the order of the system then solve the reduced system byiterative methods is called Hybrid Red-Black Ordering algorithm.In this paper,we discuss the convergence rate of the hybrid methods combined with JACOBI,CG,GMRES(m).Theoretical analysis shows that without preconditioner thesethree hybrid methods converge about 2 times as fast as the corresponding natural ordering methods.For the case that all the eigenvalues is near the real axis, the GMRES(m) algorithm converges about 3 times faster than the natural ordering GMRES(m).Various numerical experiments are presented.For large scale prob-lem with preconditioners, numerical experiments show that the GMRES(m) hybrid methods converge from about 3 times to even 5 times as fast as the natural order-ing methods and the computing time is reduced to about 1/3 even 1/6 of that of the natural ordering methods.     

Some observations on weighted GMRES

We investigate the convergence of the weighted GMRES method for solving linear systems. Two different weighting variants are compared with unweighted GMRES for three model problems, giving a phenomenological explanation of cases where weighting improves convergence, and a case where weighting has no effect on the convergence. We also present a new alternative implementation of the weighted Arnoldi algorithm which under known circumstances will be favourable in terms of computational complexity. These implementations of weighted GMRES are compared for a large number of examples. We find that weighted GMRES may outperform unweighted GMRES for some problems, but more often this method is not competitive with other Krylov subspace methods like GMRES with deflated restarting or BICGSTAB, in particular when a preconditioner is used.     

Circulant-block preconditioners for solving ordinary differential equations

Boundary value methods for solving ordinary differential equations require the solution of non-symmetric, large and sparse linear systems. In this paper, these systems are solved by using the generalized minimal residual (GMRES) method. A circulant-block preconditioner is proposed to speed up the convergence rate of the GMRES method. Theoretical and practical arguments are given to show that this preconditioner is more efficient than some other circulant-type preconditioners in some cases. A class of waveform relaxation methods is also proposed to solve the linear systems.     

基于GMRES的多项式预处理广义极小残差法

求解大型稀疏线性方程组一般采用迭代法,其中GMRES(m)算法是一种非常有效的算法,然而该算法在解方程组时,可能发生停滞．为了克服算法GMRES(m)解线性系统Ax=b过程中可能出现的收敛缓慢或不收敛,本文利用GMRES本身构造出一种有效的多项式预处理因子pk(z),该多项式预处理因子非常简单且易于实现．数值试验表明,新算法POLYGMRES(m)较好地克服了GMRES(m)的缺陷．     

M/M/c休假排队系统稳态分布的数值计算

从数值计算角度研究M/M/c休假排队系统稳定状态的概率分布.采用GMRES方法求解概率分布向量所满足的大型线性方程,构造了一个循环预处理算子加速GMRES方法的收敛.数值实例验证了该算法的优越性.     

CGS/GMRES(k): AN ADAPTIVE PRECONDITIONED CGS ALGORITHM FOR NONSYMMETRIC LINEAR SYSTEMS

Recently Y. Saad proposed a flexible inner-outer preconditioned GMRES algorithm for nonsymmetric linear systems [4]. Following their ideas, we suggest an adaptive preconditioned CGS method, called CGS/GMRES (k), in which the preconditioner is constructed in the iteration step of CGS, by several steps of GMRES(k). Numerical experiments show that the residual of the outer iteration decreases rapidly. We also found the interesting residual behaviour of GMRES for the skewsymmetric linear system Ax = b, which gives a convergence result for restarted GMRES (k). For convenience, we discuss real systems.     

BCCB preconditioners for systems of BVM‐based numerical integrators

Boundary value methods (BVMs) for ordinary differential equations require the solution of non‐symmetric, large and sparse linear systems. In this paper, these systems are solved by using the generalized minimal residual (GMRES) method. A block‐circulant preconditioner with circulant blocks (BCCB preconditioner) is proposed to speed up the convergence rate of the GMRES method. The BCCB preconditioner is shown to be invertible when the BVM is A_k1,k2‐stable. The spectrum of the preconditioned matrix is clustered and therefore, the preconditioned GMRES method converges fast. Moreover, the operation cost in each iteration of the preconditioned GMRES method by using our BCCB preconditioner is less than that required by using block‐circulant preconditioners proposed earlier. In numerical experiments, we compare the number of iterations of various preconditioners. Copyright © 2003 John Wiley & Sons, Ltd.     

Steepest descent preconditioning for nonlinear GMRES optimization

Steepest descent preconditioning is considered for the recently proposed nonlinear generalized minimal residual (N‐GMRES) optimization algorithm for unconstrained nonlinear optimization. Two steepest descent preconditioning variants are proposed. The first employs a line search, whereas the second employs a predefined small step. A simple global convergence proof is provided for the N‐GMRES optimization algorithm with the first steepest descent preconditioner (with line search), under mild standard conditions on the objective function and the line search processes. Steepest descent preconditioning for N‐GMRES optimization is also motivated by relating it to standard non‐preconditioned GMRES for linear systems in the case of a standard quadratic optimization problem with symmetric positive definite operator. Numerical tests on a variety of model problems show that the N‐GMRES optimization algorithm is able to very significantly accelerate convergence of stand‐alone steepest descent optimization. Moreover, performance of steepest‐descent preconditioned N‐GMRES is shown to be competitive with standard nonlinear conjugate gradient and limited‐memory Broyden–Fletcher–Goldfarb–Shanno methods for the model problems considered. These results serve to theoretically and numerically establish steepest‐descent preconditioned N‐GMRES as a general optimization method for unconstrained nonlinear optimization, with performance that appears promising compared with established techniques. In addition, it is argued that the real potential of the N‐GMRES optimization framework lies in the fact that it can make use of problem‐dependent nonlinear preconditioners that are more powerful than steepest descent (or, equivalently, N‐GMRES can be used as a simple wrapper around any other iterative optimization process to seek acceleration of that process), and this potential is illustrated with a further application example. Copyright © 2012 John Wiley & Sons, Ltd.     

Band-Toeplitz Preconditioned GMRES Iterations for Time-Dependent PDEs

Nonsymmetric linear systems of algebraic equations which are small rank perturbations of block band-Toeplitz matrices from discretization of time-dependent PDEs are considered. With a combination of analytical and experimental results, we examine the convergence characteristics of the GMRES method with circulant-like block preconditioning for solving these systems.This revised version was published online in October 2005 with corrections to the Cover Date.