Last one for a while, I think.  I wish you all a very peaceful Christmas and New Year, and let's hope 2015 brings some more positive energy to the world.

Links here:
https://github.com/Laeeth/d_simplex
https://github.com/Laeeth/d_swisseph



1. D bindings/wrappers for the swiss ephemeris

http://www.astro.com/swisseph/swephinfo_e.htm
"The SWISS EPHEMERIS is the high precision ephemeris developed by Astrodienst, largely based upon the DExxx ephemerides from NASA's JPL . The original release in 1997 was based on the DE405/406 ephemeris. Since release 2.00 in February 2014, it is based on the DE431 ephemeris released by JPL in September 2013".

NB - Swiss Ephemeris is not free for commercial use.
====
2. D port of simple Nelder-Mead simplex minimisation (written by Michael F. Hutt in original C version) here.  With constraints.  From Wiki:

https://en.wikipedia.org/wiki/Nelder-Mead_method
"The Nelder–Mead method or downhill simplex method or amoeba method is a commonly used nonlinear optimization technique, which is a well-defined numerical method for problems for which derivatives may not be known. However, the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points[1] on problems that can be solved by alternative methods".
====

Links here:
https://github.com/Laeeth/d_simplex
https://github.com/Laeeth/d_swisseph

On Monday, 22 December 2014 at 08:43:56 UTC, Laeeth Isharc wrote:
> Last one for a while, I think.  I wish you all a very peaceful Christmas and New Year, and let's hope 2015 brings some more positive energy to the world.
>
> Links here:
> https://github.com/Laeeth/d_simplex
> https://github.com/Laeeth/d_swisseph
>
>
>
> 1. D bindings/wrappers for the swiss ephemeris
>
> http://www.astro.com/swisseph/swephinfo_e.htm
> "The SWISS EPHEMERIS is the high precision ephemeris developed by Astrodienst, largely based upon the DExxx ephemerides from NASA's JPL . The original release in 1997 was based on the DE405/406 ephemeris. Since release 2.00 in February 2014, it is based on the DE431 ephemeris released by JPL in September 2013".
>
> NB - Swiss Ephemeris is not free for commercial use.
> ====
> 2. D port of simple Nelder-Mead simplex minimisation (written by Michael F. Hutt in original C version) here.  With constraints.  From Wiki:
>
> https://en.wikipedia.org/wiki/Nelder-Mead_method
> "The Nelder–Mead method or downhill simplex method or amoeba method is a commonly used nonlinear optimization technique, which is a well-defined numerical method for problems for which derivatives may not be known. However, the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points[1] on problems that can be solved by alternative methods".
> ====
>
> Links here:
> https://github.com/Laeeth/d_simplex
> https://github.com/Laeeth/d_swisseph

It's been ages since I read the paper, but there is a parallel version of Nelder-Mead that is supposed to give very large performance improvements, even when used on a single processor:

http://www.cs.ucsb.edu/~kyleklein/publications/neldermead.pdf

It is not difficult to implement. I may look into modifying your code to implement it when I get some time.

On Monday, 22 December 2014 at 20:46:23 UTC, bachmeier wrote:
> It's been ages since I read the paper, but there is a parallel version of Nelder-Mead that is supposed to give very large performance improvements, even when used on a single processor:
>
> http://www.cs.ucsb.edu/~kyleklein/publications/neldermead.pdf
>
> It is not difficult to implement. I may look into modifying your code to implement it when I get some time.

It will certainly also be advantageous to pass the functions as aliases, so that they can get inlined.

On Monday, 22 December 2014 at 21:39:08 UTC, Marc Schütz wrote:
> On Monday, 22 December 2014 at 20:46:23 UTC, bachmeier wrote:
>> It's been ages since I read the paper, but there is a parallel version of Nelder-Mead that is supposed to give very large performance improvements, even when used on a single processor:
>>
>> http://www.cs.ucsb.edu/~kyleklein/publications/neldermead.pdf
>>
>> It is not difficult to implement. I may look into modifying your code to implement it when I get some time.
>
> It will certainly also be advantageous to pass the functions as aliases, so that they can get inlined.

Thanks, Marc.  I appreciate the pointer, and if you do have time to look at the code.  I confess that it can't really be called my own implementation as I simply ported it to D.  There is some more clever stuff within quantlib (c++ project), but I quite liked the idea of starting with this one as it is simple, and speed is not yet vital at this stage.


Laeeth.

very nice post..



waleeed