Posted by
Dirk Eddelbuettel on
Aug 24, 2011; 2:57pm
URL: http://quantlib.414.s1.nabble.com/Bermudan-LLM-tp9645p9650.html
On 24 August 2011 at 16:17, Ferdinando Ametrano wrote:
| Hi Kakhkhor
|
| I apologize, I completely misinterpreted your question, my fault.
|
| As you wrote ATLAS would be problematic because in Windows it is
| available only if using cygwin, as far as I know. Besides it is plain
| C, isn't it?
Atlas is one of several accelerated implementation of the BLAS. It can
certainly be compiled with different Windows toolchains; there is eg a binary
you can download for R which was built with MinGW (ie very much not Cygwin)
as R on Windows requires the MinGW toolchain.
Commercial software (Matlab comes to mind) often bundles its own accelerated
BLAS; Atlas happens to have the most liberal license (but Goto is now 'Open'
too as the original author moved on).
| What about using uBLAS, the C++ boost implementation?
AFAICT it is a pain to use, and the numerics wrapper for actual linear
algrebra was once again being rewritten when I last checked. It also falls
back to using a BLAS implementations. FWIW I never managed to even cook up a
simple linear regression 'from first principles' (eg using a SVD) with uBlas.
An alternative could be provided by Eigen (
http://eigen.tuxfamily.org) which
is very clever, very fast, very templated C++ --- and does not use BLAS! But
it would add another build dependency which is a clear downside.
Another somewhat lighter alternative is Armadillo (
http://arma.sf.net). Also
templated, can use BLAS and a little simpler than Eigen. I quite like it --
and have written an package 'RcppArmadillo' that makes it a snap to use this
from R, leveraging our Rcpp package also used to tie [parts of] QuantLib to R
via RQuantLib.
[ From all that, I have a number of competing 'FastLm' implementations of
linear model fits (ie. ordinary least squares) using Armadillo, Eigen and
GSL. Armadillo does fine, Eigen does better and GSL is slowest. My blog had
a few posts on that. ]
| As you understand moving to uBLAS would be a major change anyway and
| to do it in a backward compatible way (i.e. not dropping support for
| QuantLib::Matrix, QuantLib::Array, etc.) would be even more
| challenging, so Luigi opinion on this will rule.
| Unfortunately as he gets older he's more and more conservative ;-)
|
| I for one would support a transition to uBLAS and removing all the
| QuantLib code that could be replaced by boost (e.g. math and stat
| functions), maybe on a QuantLib 2.x branch which would be not backward
| compatible with the 1.x branch
That is probably a good design decision in the medium term but you may want
to really check viability of some of the required operations first.
Hth, Dirk
| ciao -- Nando
|
| On Wed, Aug 24, 2011 at 12:37 AM, Kakhkhor Abdijalilov
| <
[hidden email]> wrote:
| > Longstaff-Schwartz method for Bermudan LLM requires OLS for every
| > early exercise opportunity, not just once.
| > I checked "Numerical recipes", Demel's and Golub's books and all
| > recommend not to use normal equations to solve OLS. Equity version of
| > Longstaff-Schwartz in QuantLib uses SVD too.
| >
| > The cost of SVD scales as SAMPLES*FACTORS^3, but the cost of path
| > generation scales as SAMPLES*FACTORS^2. SVD doesn't scale well as more
| > CPU cores are used, but path generation should scale almost perfectly.
| >
| > Regards,
| > Kakhkhor Abdijalilov.
| >
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|
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| The only unified storage solution that offers unified management
| Up to 160% more powerful than alternatives and 25% more efficient.
| Guaranteed.
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Guaranteed.
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