A recursive method for solving unconstrained tangentialinterpolation problems

An efficient recursive solution is presented for the one-sided unconstrained tangential interpolation problem. The method relies on the triangular factorization of a certain structured matrix that is implicitly defined by the interpolation data. The recursive procedure admits a physical interpretation in terms of discretized transmission lines. In this framework the generating system is constructed as a cascade of first-order sections. Singular steps occur only when the input data is contradictory, i.e., only when the interpolation problem does not have a solution. Various pivoting schemes can be used to improve numerical accuracy or to impose additional constraints on the interpolants. The algorithm also provides coprime factorizations for all rational interpolants and can be used to solve polynomial interpolation problems such as the general Hermite matrix interpolation problem. A recursive method is proposed to compute a column-reduced generating system that can be used to solve the minimal tangential interpolation problem     

A behavioral approach to linear exact modeling

The behavioral approach to system theory provides a parameter-free framework for the study of the general problem of linear exact modeling and recursive modeling. The authors present the solution of the (continuous-time) polynomial-exponential time series modeling problem. Both recursive and nonrecursive solutions are provided and classified according to properties like complexity and controllability. It is shown, in particular, that recursive modeling corresponds to updating by means of a cascade inter-connection of systems. As a special case, the solution of several other problems, such as rational interpolation, realization, and modeling of arbitrary discrete-time time series, is obtained     

Modeling for soft sensor systems and parameters updating online

Soft sensor technology is an important means to estimate important process variables in real-time. Modeling for soft sensor system is the core of this technology. Most nonlinear dynamic modeling methods integrate the processes of building the dynamic and static relationships between secondary and primary variables, which limits the estimation accuracy for primary variables. To avoid the problem, a kind of soft sensor model consisting of a dynamic model in cascade with a static one is proposed. The model identification and update online are conducted in substep way. In order to improve the model update efficiency, two improved Gauss–Newton recursive algorithms, which avoid nonsingular covariance matrix, are proposed for time-invariant and time-variant soft sensor systems. The uniform convergence for dynamic model parameter and the existence of estimation deviations for static model parameters are proved for time-invariant soft sensor system. The parameters of time-variant soft sensor system would be boundedly convergent. Case study confirms that, on the basis of the proposed model and recursive algorithms, the dynamic and static characteristics of soft sensor system can be described efficiently, and the primary variables are ensured to be estimated accurately.     

Composite freeform surface reconstruction using recursive interpolating subdivision scheme

Composite freeform surface reconstruction from 3D scanned data of a physical model has become a more and more important topic in the field of CAD/CAE/CAM. By repeated application of a fixed set of recursive interpolation subdivision schemes on the initial mesh of the 3D sparse scanned data of a physical model, a polygonal model of composite freeform surface can be constructed. In the paper, the algorithm for constructing the initial triangular mesh from 3D sparse scanned data is presented. The unified recursive interpolating subdivision scheme for triangular mesh is proposed. A special quad-tree data structure is suggested to store all the necessary information of the vertices and elements of the polygonal model. Examples of composite surface reconstruction are provided to explain the distinguished superiority of subdivision scheme for reconstructing the arbitrary topological complex surface.     

参数估计的Systolic算法

本文根据最小二乘原理在三角形Ｓｙｓｔｏｌｉｃ阵列上实现了单输入单输出系统的递推参数估计算法，首先利用矩阵的三角分解给出了待估参数及协方差阵的递推公式，然后利用正交平面旋转并结合三角形Ｓｙｓｔｏｌｉｃ阵列的特点给出了相应的Ｓｙｓｔｏｌｉｃ递推参数估计算法，最后还考虑了算法实现时的性能指标，其后是一些数值仿真结果，由于文中利用了正交平面旋转，因而所得算法是数值稳定的。     

Identification of a single-variable linear time-varying system via block pulse functions

In this article, the block pulse function expansion is applied to estimate the parameters of a single-input single-output, linear time-variant system which is assumed in a modified observable canonical structure. Under the least-squares criterion, a recursive algorithm is derived so that the system parameters in a piecewise constant form can ho obtained with very low dimensional matrix inversions. A numerical example is given     

任意三角形网格的基于二元四次箱样条分片C1曲面

提出一种以任意三角剖分为控制网格的二元箱样条曲面算法.二元三方向剖分是方向最少的三角剖分,建立在其上的二元三向四次箱样条在CAGD等领域有着广泛的应用.其规范的箱样条曲面计算仅适用于控制点的价数均为6的网格.从规范的算法出发,提出了一种任意价数控制网格的曲面计算算法,并对算法的连续性等进行了详细的分析.生成的曲面具有保凸性,且是分片C¹连续的.该算法可进行3D离散点全局或局部插值,并可应用于3D曲面重构等领域.     

任意三角形网格的基于二元四次箱样条分片C1曲面

提出一种以任意三角剖分为控制网格的二元箱样条曲面算法.二元三方向剖分是方向最少的三角剖分,建立在其上的二元三向四次箱样条在CAGD等领域有着广泛的应用.其规范的箱样条曲面计算仅适用于控制点的价数均为6的网格.从规范的算法出发,提出了一种任意价数控制网格的曲面计算算法,并对算法的连续性等进行了详细的分析.生成的曲面具有保凸性,且是分片C1连续的.该算法可进行3D离散点全局或局部插值,并可应用于3D曲面重构等领域.     

基于角度插值的三角网格变形算法

该文给出了一种仅通过插值角度来实现任意网格变形的方法。首先给出了描述任意三角网格各顶点相对位置的内在角度集,其中给出的角度变量相对于欧氏变换是不变的,并且由角度变量可唯一地确定三角网格的形状和大小。通过插值角度变量,给出中间网格变形序列。该文算法计算速度快,可以达到实时的要求,而且插值网格的形状与首末网格的位置和朝向无关。实验结果表明,按该文算法产生的变形序列过渡平滑,稳定性强。     

Recursion based parallelization of exact dense linear algebra routines for Gaussian elimination

We present block algorithms and their implementation for the parallelization of sub-cubic Gaussian elimination on shared memory architectures. Contrarily to the classical cubic algorithms in parallel numerical linear algebra, we focus here on recursive algorithms and coarse grain parallelization. Indeed, sub-cubic matrix arithmetic can only be achieved through recursive algorithms making coarse grain block algorithms perform more efficiently than fine grain ones. This work is motivated by the design and implementation of dense linear algebra over a finite field, where fast matrix multiplication is used extensively and where costly modular reductions also advocate for coarse grain block decomposition. We incrementally build efficient kernels, for matrix multiplication first, then triangular system solving, on top of which a recursive PLUQ decomposition algorithm is built. We study the parallelization of these kernels using several algorithmic variants: either iterative or recursive and using different splitting strategies. Experiments show that recursive adaptive methods for matrix multiplication, hybrid recursive–iterative methods for triangular system solve and tile recursive versions of the PLUQ decomposition, together with various data mapping policies, provide the best performance on a 32 cores NUMA architecture. Overall, we show that the overhead of modular reductions is more than compensated by the fast linear algebra algorithms and that exact dense linear algebra matches the performance of full rank reference numerical software even in the presence of rank deficiencies.     

A new algorithm for a recursive construction of the minimal interpolation space

In this work, we introduce a new interpolation algorithm, based on a recursive method for computing Lagrange interpolants. This algorithm allows to construct recursively the minimal interpolation space (see [1]) with respect to a finite set of points. We also extend this recursive method to the osculatory interpolation problem.     

A new algorithm for a recursive construction of the minimal interpolation space

In this work, we introduce a new interpolation algorithm, based on a recursive method for computing Lagrange interpolants. This algorithm allows to construct recursively the minimal interpolation space (see [1]) with respect to a finite set of points. We also extend this recursive method to the osculatory interpolation problem.     

基于Delaunay三角网的克里金并行算法优化

当采样点数据量较大时, 可以采用Delaunay三角剖分建立三角网来使用局部邻域采样点进行克里金插值. 但是该算法需要对每个插值点拟合半变异函数, 插值点规模大时造成巨大开销. 为此, 本文提出了一种以三角形为单位拟合半变异函数的克里金插值方法, 采用CPU-GPU负载均衡将部分计算优化, 充分考虑不均匀样本对克里金插值效果的影响. 结果表明, 本文算法能够保证不均匀样本集的插值效果, 提升了计算性能且能够保证较高的精度.     

方块脉冲函数应用于线性时变二次型最优控制的动态规划解法

本文通过应用方块脉冲函数的一些基本性质，将线性时变二次型最优控制问题化成多段动态规划问题，通过求解得到的动态规划问题，可得原问题的分段常值解。文中给出了形式简单且易于计算机求解的递推算法，与参考文献中的方法比较，本文得到的递推算法简单，明了，且计算时间及存储空间极大地减少，文中给出了算法的具体算例，具有明显的优越性。     

四点插入生成曲线的递归算法及在分形绘图中的应用

本文讨论基于四点插入的曲线生成方法的产发实现问题，说明实现算法可以在分形绘图中得到应用，并给出了一些实例。     

Multivariate rational interpolation

Many papers have already been published on the subject of multivariate polynomial interpolation and also on the subject of multivariate Padé approximation. But the problem of multivariate rational interpolation has only very recently been considered; we refer among others to [8] and [3]. The computation of a univariate rational interpolant can be done in various equivalent ways: one can calculate the explicit solution of the system of interpolatory conditions, or start a recursive algorithm, or calculate the convergent of a continued fraction. In this paper we will generalize each of those methods from the univariate to the multivariate case. Although the generalization is simple, the equivalence of the computational methods is completely lost in the multivariate case. This was to be expected since various authors have already remarked [2,7] that there is no link between multivariate Padé approximants calculated by matching the Taylor series and those obtained as convergents of a continued fraction.     

Shifted-Chebyshev series solutions of Takagi–Sugeno fuzzy-model-based dynamic equations

By the use of the elegant operational properties of the shifted-Chebyshev series, a direct computational algorithm for solving the Takagi–Sugeno (TS) fuzzy-model-based dynamic equations is developed in this paper. The basic idea is that the state variables are expressed in terms of the shifted-Chebyshev series. The new method simplifies the procedure of solving the TS-fuzzy-model-based dynamic equations into the successive solution of a system of recursive formulae taking only two terms of expansion coefficients. Based on the presented recursive formulae, an algorithm only involving the straightforward algebraic computation is also proposed in this paper. The computational complexity can, therefore, be reduced remarkably. The illustrated example shows that the proposed method based on the shifted-Chebyshev series can obtain the satisfactory results.     

Recursive formulation of the matrix Padé approximation in packed storage

The Extended Euclidean algorithm for matrix Padé approximants is applied to compute matrix Padé approximants when the coefficient matrices of the input matrix polynomial are triangular. The procedure given by Bjarne S. Anderson et al. for packing a triangular matrix in recursive packed storage is applied to pack a sequence of lower triangular matrices of a matrix polynomial in recursive packed storage. This recursive packed storage for a matrix polynomial is applied to compute matrix Padé approximants of the matrix polynomial using the Matrix Padé Extended Euclidean algorithm in packed form. The CPU time and memory comparison, in computing the matrix Padé approximants of a matrix polynomial, between the packed case and the non-packed case are described in detail.