多项式基函数法

提出一种新型的数值计算方法--基函数法.此方法直接在非结构网格上离散微分算子,采用基函数展开逼近真实函数,构造出了导数的中心格式和迎风格式,取二阶多项式为基函数,并采用通量分裂法及中心格式和迎风格式相结合的技术以消除激波附近的非物理波动,构造出数值求解无粘可压缩流动二阶多项式的基函数格式,通过多个二维无粘超音速和跨音速可压缩流动典型算例的数值计算表明,该方法是一种高精度的、对激波具有高分辨率的无波动新型数值计算方法,与网格自适应技术相结合可得到十分满意的结果.     

粘弹性动力问题中的一种新的时域递进算法

提出一种新的求解粘弹性动力的时域递时技术,在离散时段内将各物理量展开,导出有限元形式的递推计算格式,对不同的时间步长,可做自适应计算,以保证计算精度,数值验证给出了令人满意的结果。     

基于非结构自适应网格的二维Euler方程数值求解方法研究

提出了一种基于非结构自适应网格的二维Euler方程的数值解法.采用有限体积法进行空间离散,通量计算采用Jamson中心格式,使得它适用于任意多边形计算单元.为了得到定常解,采用一种显式的四步Runge-Kutta迭代方法对时间进行积分.根据流场参数的变化梯度确定加密边,由加密准则进行自适应网格剖分,然后得到分布合理的加密过后的网格.求解二维Euler方程,对NACA0012翼型进行了数值模拟,通过对自适应前后的数值解的对比,说明所建立的方法是正确的.     

奇异摄动反应扩散方程的后验误差估计及自适应算法

研究了一类奇异摄动半线性反应扩散方程的自适应网格方法.在任意非均匀网格上建立迎风有限差分离散格式,并推导出离散格式的后验误差界,然后用该误差界设计自适应网格移动算法.数值实验结果证明了所提出的自适应网格方法的有效性.     

带间断扩散系数热传导方程的新型自适应数值解法

本文研究带间断扩散系数热传导方程在大变形网格上的高精度数值模拟方法.该方法在算每条边上的能流时采用了本文提出的"孪生逼近"方法,提高了扩散系数间断处能流的计算精度,给出了"孪生逼近"的误差分析.应用该方法于二维大变形网格上热传导问题计算,构造r网格边上能流的一种自适应高精度计算方法,其中自适应指的是自适应选取模板和自适应选取权重大小.数值试验表明该方法能适应网格大变形和扩散系数间断的困难情况.     

离散算子差分法的单元函数

给出了弹性力学离散算子差分法的离散格式，并给出了该方法的几个板弯曲单元和平面四边形单元，通过对它们的考察，分析了离散算子差分方法中的离散格式对单元位移函数的反映能力。在离散算子差分方法中，无论单元位移函数是否协调，其位移函数均能在离散格式中得到十分好的再现，说明了离散算子差分方法的离散格式是一种性能很优良的离散格式。     

变截面激波管中波系的一个二维不定常Lagrangian算法及流动分析

本文用Lagrangian坐标计算二维不定常轴对称气流在变截面激波管内的流动,对管壁、对称轴、接触间断都定出了具体的计算格式。 Lagragian差分格式的优点是处理二种气体的分界面比较自然,因分界面随时间的进程是人们关心的问题。分界面的二侧是二种不同的气体,它们的状态方程自然也不一     

高阶特征线性及其与semi-Lagrangian方法的比较

特征线性与semi-Lagrangian方法都是处理流体方程时间离散的两种有效的方法．它们比经典的半隐格式，如Backward-Euler／Adams-Bashforth方法有更好的稳定性．本提出一种基于高阶空间离散的特征线法，通过稳定性，精度和计算复杂性与semi-Lagrangian方法进行比较，分析了高阶特征线法的有效性和适用性，并从数值试验上对分析结果进行验证．     

求解Klein-Gordon-Schrdinger方程组的一个新型守恒差分算法的收敛性分析

对复Schrdinger场和实Klein-Gordon场相互作用下一类耦合方程组的初边值问题进行了数值研究,提出了一个高效差分格式,该格式非耦合且为半显格式,因此比隐格式具有更快的计算速度,而且便于并行计算;同时,该格式很好地模拟了初边值问题的守恒性质,保证了格式计算的可靠性,从而便于长时间计算.细致讨论了格式的守恒性质,并在先验估计的基础上运用能量方法分析了差分格式的收敛性.     

时间分数阶期权定价模型的一类有效差分方法

时间分数阶期权定价模型(时间分数阶Black-Scholes方程)数值解法的研究具有重要的理论意义和实际应用价值.对时间分数阶Black-Scholes方程构造了显-隐格式和隐-显差分格式,讨论了两类格式解的存在唯一性,稳定性和收敛性.理论分析证实,显-隐格式和隐-显格式均为无条件稳定和收敛的,两种格式具有相同的计算量.数值试验表明:显-隐和隐-显格式的计算精度与经典Crank-Nicolson(C-N)格式的计算精度相当,其计算效率(计算时间)比C-N格式提高30%.数值试验验证了理论分析,表明本文的显-隐和隐-显差分方法对求解时间分数阶期权定价模型是高效的,证实了时间分数阶Black-Scholes方程更符合实际金融市场.     

Building nonredundant adaptive wavelets by update lifting

In this paper, we propose a technique for building adaptive wavelets by means of an extension of the lifting scheme. Our scheme comprises an adaptive update lifting step and a fixed prediction lifting step. The adaptivity consists hereof that the system can choose between two different update filters, and that this choice is triggered by the local gradient of the original signal. If the gradient is large (in some seminorm sense) it chooses one filter, if it is small the other. We derive necessary and sufficient conditions for the invertibility of such an adaptive system for various scenarios. Furthermore, we present some examples to illustrate our theoretical results.     

Stability for a novel time-delay financial hyperchaotic system by adaptive periodically intermittent linear control

In this paper, we get a time-delay new financial hyperchaotic system by modifying an old financial hyperchaotic system. we study the stability of a time-delay financial hyperchaotic system via adaptive periodically intermittent linear control method. Stability is obtained by using Lyapunov stability theorem, adaptive update laws and differential inequalities. Moreover, some numerical simulations are performed to show the advantage of the applications of this method.     

A two-stage prediction–correction method for solving monotone variational inequalities

In this paper, we present a two-stage prediction–correction method for solving monotone variational inequalities. The method generates the two predictors which should satisfy two acceptance criteria. We also enhance the method with an adaptive rule to update prediction step size which makes the method more effective. Under mild assumptions, we prove the convergence of the proposed method. Our proposed method based on projection only needs the function values, so it is practical and the computation load is quite tiny. Some numerical experiments were carried out to validate its efficiency and practicality.     

Wavelet adaptive method for second order elliptic problems: boundary conditions and domain decomposition

Summary. Wavelet methods allow to combine high order accuracy, multilevel preconditioning techniques and adaptive approximation, in order to solve efficiently elliptic operator equations. One of the main difficulty in this context is the efficient treatment of non-homogeneous boundary conditions. In this paper, we propose a strategy that allows to append such conditions in the setting of space refinement (i.e. adaptive) discretizations of second order problems. Our method is based on the use of compatible multiscale decompositions for both the domain and its boundary, and on the possibility of characterizing various function spaces from the numerical properties of these decompositions. In particular, this allows the construction of a lifting operator which is stable for a certain range of smoothness classes, and preserves the compression of the solution in the wavelet basis. An explicit construction of the wavelet bases and the lifting is proposed on fairly general domains, based on conforming domain decomposition techniques. Received November 2, 1998 / Published online April 20, 2000     

A new modified nonmonotone adaptive trust region method for unconstrained optimization

In this paper, we present an adaptive trust region method for solving unconstrained optimization problems which combines nonmonotone technique with a new update rule for the trust region radius. At each iteration, our method can adjust the trust region radius of related subproblem. We construct a new ratio to adjust the next trust region radius which is different from the ratio in the traditional trust region methods. The global and superlinear convergence results of the method are established under reasonable assumptions. Numerical results show that the new method is efficient for unconstrained optimization problems.     

On regularization in parameter-free shape optimization

This contribution is concerned with a parameter-free approach to computational shape optimization of mechanically-loaded structures. Thereby the term ’parameter-free’ refers to approaches in shape optimization in which the design variables are not derived from an existing CAD-parametrization of the model geometry but rather from its finite element discretization. One of the major challenges in using this type of approach is the avoidance of oscillating boundaries in the optimal design trials. This difficulty is mainly attributed to a lack of smoothness of the objective sensitivities and the relatively high number of design variables within the parameter-free regime. To compensate for these deficiencies, Azegami introduced the concept of the so-called traction method, in which the actual design update is deduced from the deformation of a fictitious continuum that is loaded in proportion to the negative shape gradient. We investigate a discrete variant of the traction method, in which the design sensitivities are computed with respect to variations of the design nodes for a given finite element mesh rather than on the abstract level by means of the speed method. Moreover, the design update process is accompanied by adaptive mesh refinement based on discrete material residual forces. Therein, we consider radaptive node relocation as well as hadaptive mesh refinement. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Recursive prediction of chaotic time series

Summary Considerable progress has been made in recent years in the analysis of time series arising from chaotic systems. In particular, a variety of schemes for the short-term prediction of such time series has been developed. However, hitherto all such algorithms have used batch processing and have not been able to continuously update their estimate of the dynamics using new observations as they are made. This severely limits their usefulness in real time signal processing applications. In this paper we present a continuous update prediction scheme for chaotic time series that overcomes this difficulty. It is based on radial basis function approximation combined with a recursive least squares estimation algorithm. We test this scheme using simulated data and comment on its relationship to adaptive transversal filters, which are widely used in conventional signal processing.     

An adaptive updating full-Newton step interior-point algorithm with modified Newton direction

We adopt the self-adaptive strategy to update the barrier parameter of a feasible primal-dual interior-point algorithm. We obtain two adaptive updating methods, namely, cheap updates and sharp updates. We compare the effectiveness of the short updates with the adaptive update methods on some benchmark problems. The numerical results show that the sharp updates method is superior to short updates and cheap updates methods.     

An adaptive scaled BFGS method for unconstrained optimization

A new adaptive scaled Broyden-Fletcher-Goldfarb-Shanno (BFGS) method for unconstrained optimization is presented. The third term in the standard BFGS update formula is scaled in order to reduce the large eigenvalues of the approximation to the Hessian of the minimizing function. Under the inexact Wolfe line search conditions, the global convergence of the adaptive scaled BFGS method is proved in very general conditions without assuming the convexity of the minimizing function. Using 80 unconstrained optimization test functions with a medium number of variables, the preliminary numerical experiments show that this variant of the scaled BFGS method is more efficient than the standard BFGS update or than some other scaled BFGS methods.     

Convergence and Optimality of Adaptive Regularization for Ill-posed Deconvolution Problems in Infinite Spaces

The adaptive regularization method is first proposed by Ryzhikov et al. in [6] for the deconvolution in elimination of multiples which appear frequently in geoscience and remote sensing. They have done experiments to show that this method is very effective. This method is better than the Tikhonov regularization in the sense that it is adaptive, i.e., it automatically eliminates the small eigenvalues of the operator when the operator is near singular. In this paper, we give theoretical analysis about the adaptive regularization. We introduce an a priori strategy and an a posteriori strategy for choosing the regularization parameter, and prove regularities of the adaptive regularization for both strategies. For the former, we show that the order of the convergence rate can approach O（｜｜n｜｜^4v/4v＋1） for some 0 〈 v 〈 1, while for the latter, the order of the convergence rate can be at most O（｜｜n｜｜^2v/2v＋1） for some 0 〈 v 〈 1.