In this research, we solve polynomial, Sobolev polynomial, rational, and Sobolev rational least squares problems. Although the increase in the approximation degree allows us to fit the data better in attacking least squares problems, the ill-conditioning of the coefficient matrix fuels the dramatic decrease in the accuracy of the approximation at higher degrees. To overcome this drawback, we first show that the column space of the coefficient matrix is equivalent to a Krylov subspace. Then the connection between orthogonal polynomials or rational functions and orthogonal bases for Krylov subspaces in order to exploit Krylov subspace methods like Arnoldi orthogonalization is established. Furthermore, some examples are provided to illustrate the theory and the performance of the proposed approach.
Stability and Bifurcation Analysis of Nonlinear PDEs via Random Projection-based PINNs: A Krylov-Arnoldi Approach
Hopkins Extreme Materials Institute, Johns Hopkins University（霍普金斯极端材料研究所，约翰霍普金斯大学）；Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University（化学与生物分子工程系，约翰霍普金斯大学）；School of Chemical Engineering, National Technical University of Athens（希腊技术大学化学工程学院）；Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”, Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学Renato Caccioppoli数学与应用系）；Dept. of Applied Mathematics and Statistics, Johns Hopkins University（应用数学与统计学系，约翰霍普金斯大学）
Non-Linear Least-Squares Optimization of Rational Filters for the Solution of Interior Eigenvalue Problems
RWTH Aachen University（亚琛工业大学）；Jülich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich GmbH（于利希超级计算中心，先进模拟研究所，于利希研究中心有限公司）
Weighted polynomial approximation of rational Bézier curves
Institute of Computer Science, University of Wrocław（弗罗茨瓦夫大学计算机研究所）；Faculty of Mathematics and Information Science, Warsaw University of Technology（华沙理工大学数学与信息科学学院）
