Eigen and cholesky decomposition: instability issues?
I'm working on a 3-D surface mesh and trying to obtain gaussian curvature values by fitting a quadric polynomial to each vertex neighborhood. In order to obtain the polynomial coefficients I'm using the standard cholesky decomposition (LL^T) included on Eigen package (C++), for solving my system Ax = b, where A is a symmetric, positive definite matrix.
When solving the system I'm getting the strangest values only for one of the solution (among about 2000 vertices!). Here's a sample of the what would be the inverse of my symmetric, positive definite matrix:
Vertex 85:
288.413 6.45563 3.95006 -48.7131 -17.8428 -30.1609
6.45563 0.199845 0.103744 -1.09986 -0.409136 -0.673235
3.95006 0.103744 0.0979785 -0.666895 -0.241934 -0.41527
-48.7131 -1.09986 -0.666895 8.24633 3.03219 5.09083
-17.8428 -0.409136 -0.241934 3.03219 1.14272 1.8648
-30.1609 -0.673235 -0.41527 5.09083 1.8648 3.16057
Vertex 86:
496411 2072.94 -8090.开发者_C百科48 -48680.5 23267.4 -88125.4
2072.94 8.69566 -33.8001 -203.285 97.1605 -367.998
-8090.48 -33.8001 131.916 793.391 -379.213 1436.27
-48680.5 -203.285 793.391 4773.85 -2281.71 8641.99
23267.4 97.1605 -379.213 -2281.71 1090.6 -4130.56
-88125.4 -367.998 1436.27 8641.99 -4130.56 15644.5
Vertex 87:
523.131 -1.91535 -7.666 -49.6631 17.1292 -96.191
-1.91535 0.0430769 0.0135821 0.181351 -0.061675 0.352979
-7.666 0.0135821 0.174082 0.72835 -0.249803 1.41043
-49.6631 0.181351 0.72835 4.72212 -1.62466 9.12386
17.1292 -0.061675 -0.249803 -1.62466 0.58246 -3.16276
-96.191 0.352979 1.41043 9.12386 -3.16276 17.7163
Just by comparing the first element of each matrix you can see the differences: 288 (vertex 85), 496411 (vertex 86) and back to normal values on vertex 87: 523.131...
Here's part of the code I'm using (B is my symmetric, positive definite matrix):
MatrixXd B = A.adjoint()*A;
MatrixXd x = B.llt().solve(A.adjoint()*b)
I don't know if I'm missing something... It might be an instability issue, maybe round-off errors? How to get rid of these unexpected results? Thanks, Miguel
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