Timeline for Implementation of smoothing splines function
Current License: CC BY-SA 3.0
16 events
| when toggle format | what | by | license | comment | |
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| Jul 27, 2015 at 11:00 | history | edited | J. M.'s missing motivation♦ | CC BY-SA 3.0 |
added 7 characters in body
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| Jul 26, 2015 at 10:38 | answer | added | J. M.'s missing motivation♦ | timeline score: 12 | |
| Jun 6, 2015 at 10:52 | answer | added | Alexey Popkov | timeline score: 7 | |
| Jun 6, 2015 at 10:43 | history | edited | J. M.'s missing motivation♦ |
edited tags
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| Oct 27, 2013 at 12:13 | answer | added | Tobi | timeline score: 15 | |
| Sep 30, 2013 at 17:08 | answer | added | Andy Ross | timeline score: 31 | |
| Sep 29, 2013 at 18:30 | history | edited | jojosthegreat | CC BY-SA 3.0 |
edited title
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| Sep 29, 2013 at 17:51 | comment | added | jojosthegreat | Dear Belissarius thank you for your response. All these posts state that we must duplicate the external knots d times, where d is the degree of the spline. I know that fact, but why my second problem persists? Anyway, this correction seems to cure many instabilities, but not all: a = LinearSolve[ Transpose[X].X + lambda*Transpose[Dsq]. Dsq, Transpose[X].data, Method -> "Krylov"] // N; | |
| S Sep 29, 2013 at 17:37 | history | suggested | Sektor | CC BY-SA 3.0 |
Reformatted
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| Sep 29, 2013 at 17:30 | review | Suggested edits | |||
| S Sep 29, 2013 at 17:37 | |||||
| Sep 29, 2013 at 17:21 | comment | added | jojosthegreat | Actually the problem persists: LinearSolve::luc: "Result for ... of badly conditioned matrix may contain significant numerical errors. | |
| Sep 29, 2013 at 17:09 | comment | added | jojosthegreat | Thank you very much ssch. The algorithm is more stable now. But I am still in doubt for the duplication of the first and the last point as knots. Anyway, you helped me a lot. | |
| Sep 29, 2013 at 17:03 | review | First posts | |||
| Sep 29, 2013 at 17:30 | |||||
| Sep 29, 2013 at 17:00 | comment | added | Dr. belisarius | have you seen this? mathematica.stackexchange.com/… | |
| Sep 29, 2013 at 16:47 | comment | added | ssch |
Always avoid calling Inverse when you can use LinearSolve. Solving a linear system is much faster and stabler than calculating an inverse.
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| Sep 29, 2013 at 16:43 | history | asked | jojosthegreat | CC BY-SA 3.0 |