Comments (4)
I implemented a crude version of the Levenberg-Marquardt-method, based on this. It significantly reduces the number of failures to:
[11 21 61 62 81 82 88]
with the same example. It also uses a secondary metric from ILSB's numerics course (here as supposed to here), which seems to perform better.
There are two versions with a boolean flag here, where either the lambda * diag(J^TJ)
is added or lambda * I
, as shown in the wiki article. I did not observe any meaningful difference.
If only LM is used the failures are these:
[ 1 11 19 20 21 29 31 61 62 80 81 82 88 91]
@j042 : Is this worth further investigation?
from splinepy.
I implemented a crude version of the Levenberg-Marquardt-method, based on this. It significantly reduces the number of failures to:
[11 21 61 62 81 82 88]
with the same example. It also uses a secondary metric from ILSB's numerics course (here as supposed to here), which seems to perform better.There are two versions with a boolean flag here, where either the
lambda * diag(J^TJ)
is added orlambda * I
, as shown in the wiki article. I did not observe any meaningful difference.If only LM is used the failures are these:
[ 1 11 19 20 21 29 31 61 62 80 81 82 88 91]
@j042 : Is this worth further investigation?
Side-note. If we do not change the penalisation damping parameter, even if the coefficient rho
is out of bounds, but only when the solution gets worse, it converges for all points. This is the latest version in the branch
from splinepy.
I implemented a crude version of the Levenberg-Marquardt-method, based on this. It significantly reduces the number of failures to:
[11 21 61 62 81 82 88]
with the same example. It also uses a secondary metric from ILSB's numerics course (here as supposed to here), which seems to perform better.
There are two versions with a boolean flag here, where either thelambda * diag(J^TJ)
is added orlambda * I
, as shown in the wiki article. I did not observe any meaningful difference.
If only LM is used the failures are these:[ 1 11 19 20 21 29 31 61 62 80 81 82 88 91]
@j042 : Is this worth further investigation?Side-note. If we do not change the penalisation damping parameter, even if the coefficient
rho
is out of bounds, but only when the solution gets worse, it converges for all points. This is the latest version in the branch
Thanks a lot for implementing this, sounds great to me!
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I had a weird case where max_iterations
value was set to 0
at ~60 query within a call. This happened at PySpline::Proximities
. I wonder if that was compiler error or something that happens due to our implementation.
from splinepy.
Related Issues (20)
- numpy DeprecationWarning in helpme/fit.py HOT 2
- Inconsistent variables for export function in io module HOT 2
- inplace knot vector update
- Pictures in documentation HOT 1
- Error in example show_microstructures - DoubleLattice
- Issue with Matrix solve HOT 4
- Test Microstructure
- Error in microstructure with normal splinepy install HOT 2
- `boundary_multipatch` seems to be deleting interface information HOT 1
- Bezier Extraction issues HOT 4
- Bezier Extraction matrices for 0th degree spline
- `TileBase` missing `create_tile` interface HOT 1
- inconsistency in fitting HOT 3
- multipatch boundary id magic number HOT 2
- Swept Surface
- revolve with angle=-180
- xml io issues for CATS splines HOT 9
- migration to numpy 2.0 HOT 1
- Visualizing 1D geometry with 'k3d' HOT 1
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