Orthogonal polynomial expansions on sparse grids

被引:2
|
作者
Cao, Yanzhao [1 ,2 ]
Jiang, Ying [2 ]
Xu, Yuesheng [2 ,3 ]
机构
[1] Auburn Univ, Dept Math & Stat, Auburn, AL 36830 USA
[2] Sun Yat Sen Univ, Sch Math & Computat Sci, Guangdong Prov Key Lab Computat Sci, Guangzhou 510275, Guangdong, Peoples R China
[3] Syracuse Univ, Dept Math, Syracuse, NY 13244 USA
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
Orthogonal polynomial; Sparse grid; Spectral method; Collocation method; FAST ALGORITHMS; INTERPOLATION; TRANSFORMS;
D O I
10.1016/j.jco.2014.04.001
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
We study the orthogonal polynomial expansion on sparse grids for a function of d variables in a weighted L-2 space. Two fast algorithms are developed for computing the orthogonal polynomial expansion and evaluating a linear combination of orthogonal polynomials on sparse grids by combining the fast cosine transform, the fast transforms between the qChebyshev orthogonal polynomial basis and the orthogonal polynomial basis for the weighted L2 space, and a fast algorithm of computing hierarchically structured basis functions. The total number of arithmetic operations used in both algorithms is O(n log(d+1) n) where n is the highest polynomial degree in one dimension. The exponential convergence of the approximation for the analytic function is investigated. Specifically, we show the sub-exponential convergence for analytic functions and moreover we prove the approximation order is optimal for the Chebyshev orthogonal polynomial expansion. We furthermore establish the fully exponential convergence for functions with a somewhat stronger analytic assumption. Numerical experiments confirm the theoretical results and demonstrate the efficiency and stability of the proposed algorithms. (C) 2014 Elsevier Inc. All rights reserved.
引用
下载
收藏
页码:683 / 715
页数:33
相关论文
共 50 条
  • [41] Sharp estimates of the Cesaro kernels for weighted orthogonal polynomial expansions in several variables
    Dai, Feng
    Ge, Yan
    JOURNAL OF FUNCTIONAL ANALYSIS, 2021, 280 (04)
  • [42] Optimal decay rates on the asymptotics of orthogonal polynomial expansions for functions of limited regularities
    Xiang, Shuhuang
    Liu, Guidong
    NUMERISCHE MATHEMATIK, 2020, 145 (01) : 117 - 148
  • [43] ALMOST EVERYWHERE SUMMABILITY OF ORTHOGONAL POLYNOMIAL-EXPANSIONS ON THE UNIT-CIRCLE
    MATE, A
    JOURNAL OF APPROXIMATION THEORY, 1992, 71 (03) : 252 - 262
  • [44] A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions
    Alemazkoor, Negin
    Meidani, Hadi
    JOURNAL OF COMPUTATIONAL PHYSICS, 2018, 371 : 137 - 151
  • [45] A robust and efficient stepwise regression method for building sparse polynomial chaos expansions
    Abraham, Simon
    Raisee, Mehrdad
    Ghorbaniasl, Ghader
    Contino, Francesco
    Lacor, Chris
    JOURNAL OF COMPUTATIONAL PHYSICS, 2017, 332 : 461 - 474
  • [46] Uncertainty propagation of p-boxes using sparse polynomial chaos expansions
    Schobi, Roland
    Sudret, Bruno
    JOURNAL OF COMPUTATIONAL PHYSICS, 2017, 339 : 307 - 327
  • [47] A weighted l1-minimization approach for sparse polynomial chaos expansions
    Peng, Ji
    Hampton, Jerrad
    Doostan, Alireza
    JOURNAL OF COMPUTATIONAL PHYSICS, 2014, 267 : 92 - 111
  • [48] Application of Conditional Random Fields and Sparse Polynomial Chaos Expansions to Geotechnical Problems
    Schobi, Roland
    Sudret, Bruno
    GEOTECHNICAL SAFETY AND RISK V, 2015, : 445 - 450
  • [49] Efficient computation of global sensitivity indices using sparse polynomial chaos expansions
    Blatman, Geraud
    Sudret, Bruno
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2010, 95 (11) : 1216 - 1229
  • [50] Spectral representation of stochastic field data using sparse polynomial chaos expansions
    Abraham, Simon
    Tsirikoglou, Panagiotis
    Miranda, Joao
    Lacor, Chris
    Contino, Francesco
    Ghorbaniasl, Ghader
    JOURNAL OF COMPUTATIONAL PHYSICS, 2018, 367 : 109 - 120