A Fast Block-Greedy Algorithm for Quasi-optimal Meshless Trial Subspace Selection

Meshless collocation methods are often seen as a flexible alternative to overcome difficulties that may occur with other methods. As various meshless collocation methods gain popularity, finding appropriate settings becomes an important open question. Previously, we proposed a series of sequential-greedy algorithms for selecting quasi-optimal meshless trial subspaces that guarantee stable solutions from meshless methods, all of which were designed to solve a more general problem: “Let $A$ be an $M \times N$ matrix with full rank $M$; choose a large $M \times K$ submatrix formed by $K\leq M$ columns of $A$ such that it is numerically of full rank.” In this paper, we propose a block-greedy algorithm based on a primal/dual residual criterion. Similar to all algorithms in the series, the block-greedy algorithm can be implemented in a matrix-free fashion to reduce the storage requirement. Most significantly, the proposed algorithm reduces the computational cost from the previous $\mathcal{O}(K^4+NK^2)$ to at m...

[1]  T. Wei,et al.  An adaptive greedy technique for inverse boundary determination problem , 2010, J. Comput. Phys..

[2]  Robert Schaback,et al.  Stable and Convergent Unsymmetric Meshless Collocation Methods , 2008, SIAM J. Numer. Anal..

[3]  Robert Schaback,et al.  An improved subspace selection algorithm for meshless collocation methods , 2009 .

[4]  Tobin A. Driscoll,et al.  Computing eigenmodes ofelliptic operators using radial basis functions , 2004 .

[5]  Wen Chen,et al.  The Localized RBFs Collocation Methods for Solving High Dimensional PDEs , 2013 .

[6]  R. Schaback,et al.  Results on meshless collocation techniques , 2006 .

[7]  Robert Schaback,et al.  On unsymmetric collocation by radial basis functions , 2001, Appl. Math. Comput..

[8]  C.M.C. Roque,et al.  Numerical experiments on optimal shape parameters for radial basis functions , 2009 .

[9]  J. Wertz,et al.  The role of the multiquadric shape parameters in solving elliptic partial differential equations , 2006, Comput. Math. Appl..

[10]  Nicholas J. Highamy Estimating the matrix p-norm , 1992 .

[11]  Robert Schaback,et al.  On convergent numerical algorithms for unsymmetric collocation , 2009, Adv. Comput. Math..

[12]  C. Loan On estimating the condition of eigenvalues and eigenvectors , 1987 .

[13]  Jürgen Geiser,et al.  Numerical solution to time-dependent 4D inviscid Burgers' equations , 2013 .

[14]  Robert Schaback,et al.  An Adaptive Greedy Algorithm for Solving Large RBF Collocation Problems , 2004, Numerical Algorithms.

[15]  Elisabeth Larsson,et al.  Stable Computations with Gaussian Radial Basis Functions , 2011, SIAM J. Sci. Comput..

[16]  N. Higham Estimating the matrixp-norm , 1992 .

[17]  D. E. Knuth Semi-optimal bases for linear dependencies , 1985 .

[18]  R. E. Carlson,et al.  Improved accuracy of multiquadric interpolation using variable shape parameters , 1992 .

[19]  Gregory E. Fasshauer,et al.  On choosing “optimal” shape parameters for RBF approximation , 2007, Numerical Algorithms.

[20]  Tobin A. Driscoll,et al.  Computing Eigenmodes of Elliptic Operators Using Radial Basis Functions , 2003 .

[21]  Hamed Rabiei,et al.  On the new variable shape parameter strategies for radial basis functions , 2015 .

[22]  L. Ling,et al.  Solving moving-boundary problems with the wavelet adaptive radial basis functions method , 2013 .

[23]  Bengt Fornberg,et al.  The Runge phenomenon and spatially variable shape parameters in RBF interpolation , 2007, Comput. Math. Appl..

[24]  Gregory E. Fasshauer,et al.  Kernel-based Approximation Methods using MATLAB , 2015, Interdisciplinary Mathematical Sciences.

[25]  Leevan Ling,et al.  An adaptive‐hybrid meshfree approximation method , 2012 .

[26]  Nicholas J. Highham A survey of condition number estimation for triangular matrices , 1987 .

[27]  A. Cheng,et al.  A comparison of efficiency and error convergence of multiquadric collocation method and finite element method , 2003 .

[28]  C. Tsai,et al.  The Golden Section Search Algorithm for Finding a Good Shape Parameter for Meshless Collocation Methods , 2010 .

[29]  Gu Dun-he,et al.  A NOTE ON A LOWER BOUND FOR THE SMALLEST SINGULAR VALUE , 1997 .

[30]  Limin Zou,et al.  A lower bound for the smallest singular value , 2012 .

[31]  Miroslav Tuma,et al.  On Incremental Condition Estimators in the 2-Norm , 2014, SIAM J. Matrix Anal. Appl..

[32]  E. Kansa,et al.  HKBU Institutional Repository , 2018 .

[33]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[34]  Sven Hammarling,et al.  Updating the QR factorization and the least squares problem , 2008 .

[35]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[36]  R. Schaback Adaptive Numerical Solution of MFS Systems , 2007 .

[37]  Hermann Brunner,et al.  Numerical simulations of 2D fractional subdiffusion problems , 2010, J. Comput. Phys..

[38]  E. Kansa Multiquadrics—A scattered data approximation scheme with applications to computational fluid-dynamics—I surface approximations and partial derivative estimates , 1990 .

[39]  C.-S. Huang,et al.  On the increasingly flat radial basis function and optimal shape parameter for the solution of elliptic PDEs , 2010 .

[40]  Bengt Fornberg,et al.  On choosing a radial basis function and a shape parameter when solving a convective PDE on a sphere , 2008, J. Comput. Phys..

[41]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[42]  Elisabeth Larsson,et al.  Stable computations with Gaussian radial basis functions in 2-D , 2009 .

[43]  Michael J. McCourt,et al.  Stable Evaluation of Gaussian Radial Basis Function Interpolants , 2012, SIAM J. Sci. Comput..