2011
DOI: 10.1142/s021820251100560x
|View full text |Cite
|
Sign up to set email alerts
|

Pseudodifferential Equations on the Sphere With Spherical Splines

Abstract: Spherical splines are used to define approximate solutions to strongly elliptic pseudodifferential equations on the unit sphere. These equations arise from geodesy. The approximate solutions are found by using Galerkin method. We prove optimal convergence (in Sobolev norms) of the approximate solution by spherical splines to the exact solution. Our numerical results underlie the theoretical result.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
11
0

Year Published

2011
2011
2019
2019

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 14 publications
(11 citation statements)
references
References 21 publications
0
11
0
Order By: Relevance
“…For more details about the above evaluation, please refer to [15]. The right hand side of the linear system (2.21) has entries given by…”
Section: Numerical Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…For more details about the above evaluation, please refer to [15]. The right hand side of the linear system (2.21) has entries given by…”
Section: Numerical Resultsmentioning
confidence: 99%
“…Spherical splines (which were developed in [2][3][4] and have many properties in common with classical polynomial splines over planar triangulations) are another tool well suited for scattered data interpolation and approximation problems [15]. However, as proved in Proposition 2.6 in the next section and evidenced by the numerical results in Table 1, the Galerkin method with spherical splines yields a linear system which may also be ill-conditioned, though the ill-conditionedness is not as bad as with radial basis functions.…”
Section: )mentioning
confidence: 99%
“…However, finding the eigenvalues δ 1 and δ 2 is a more complicated problem than solving the original problem (15). Konovalov [8] showed that if {c l } are the iterates obtained by solving (15) with the steepest descend method and if…”
Section: Alternate Triangular Preconditionermentioning
confidence: 99%
“…Numerical experiments show that the optimal value for κ(C(ω l )A) is κ(C(ω opt )A) , where ω opt = lim l→∞ωl . The obtained value ω opt is then used to solve (15) with the preconditioned conjugate gradient method with the optimal preconditioner C(ω opt ) . Table 2 gives a pseudocode for the proposed method to find ω opt .…”
Section: Alternate Triangular Preconditionermentioning
confidence: 99%
See 1 more Smart Citation