Hardesty/Hardesty2
surface fitting problem (smaller version)
| Name |
Hardesty2 |
| Group |
Hardesty |
| Matrix ID |
2832 |
|
Num Rows
|
929,901 |
|
Num Cols
|
303,645 |
|
Nonzeros
|
4,020,731 |
|
Pattern Entries
|
4,020,731 |
|
Kind
|
Computer Graphics/Vision Problem |
|
Symmetric
|
No |
|
Date
|
2015 |
|
Author
|
S. Hardesty |
|
Editor
|
T. Davis |
| Structural Rank |
303,645 |
| Structural Rank Full |
true |
|
Num Dmperm Blocks
|
1 |
|
Strongly Connect Components
|
1 |
|
Num Explicit Zeros
|
0 |
|
Pattern Symmetry
|
0% |
|
Numeric Symmetry
|
0% |
|
Cholesky Candidate
|
no |
|
Positive Definite
|
no |
|
Type
|
real |
| Download |
MATLAB
Rutherford Boeing
Matrix Market
|
| Notes |
Surface fitting problem for visualization, Sean Hardesty
Visualization of 3D structures in the earth
The Hardesty3 matrix is an interpolation matrix stacked above a
weighted Laplacian, to to fit a surface z(x,y) to a set of points
in R^3 subject to a smoothness constraint enforced via regularization.
Hardesty2 is a smaller version of this problem.
For the big matrix (Hardesty/Hardesty3), sparse QR (via SuiteSparseQR,
or SPQR) finds an R factor and a set of Householder vectors (Q.H) with
about 150 million nonzeros. Sparse LU factorization (with UMFPACK
v5.7.1) sees very high fillin (about 2.5 billion nonzeros in L+U).
The Hardesty1 matrix is a simple discretization of a 2D biharmonic
operator with some Lagrange multiplier constraints used for smoothing.
|
