Displaying 4 results from an estimated 4 matches for "kibbitz".
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kibitz
2010 Apr 03
0
Restricting optimisation algorithm's parameter space
...rticularly parameter bounds, on parameter inputs to optimization.
There is quite a lot of activity going on right now with optimization in R. With Ravi
Varadhan, Kate Mullen and Paul Gilbert, I've been putting some codes on R-forge in the
OptimizeR project. Doug Bates has done some important kibbitzing. Stefan Theussl and
others have the R Optimization Infrastructure. There are some differences of focus in
these projects.
For the present poster, I cannot unfortunately help much on GNLM details, but I can say
that there are trial versions of methods that will handle bounds (packages minqa,...
2012 Apr 23
0
Solve an ordinary or generalized eigenvalue problem in R
...e Matrix package, but as a shorter-term solution,
it could be useful to put together a suite for dense matrices. Using existing Fortran
codes would allow this to be done fairly quickly and it could be a nice summer or term
project for a student (Google Summer of Code 2013?). I'll be happy to kibbitz and mentor,
but I'd rather stay on the sidelines of actual package building, as I no longer have a
direct need. My application was the quantum electronic structure of polymers in 1972/3.
However, I think the code is still reasonable.
Best, JN
On 04/23/2012 06:00 AM, r-help-request at r-pr...
2019 Mar 02
2
Support for out-of-tree backend passes?
Hi all,
I've been doing some LLVM development recently, as was curious about
the status/feasibility of allowing developers to write out-of-tree
back-end passes (e.g. `MachineFunctionPass`es) in a matter similar
to middle-end passes.
From the limited resources I can find online[1][2][3], LLVM currently
doesn't support building back-end passes outside of the source tree.
Could anybody more
2009 Aug 20
4
Principle components analysis on a large dataset
Dear Sirs:
Please pardon me I am very new to R. I have been using MATLAB.
I was wondering if R would allow me to do principal components analysis on a
very large
dataset.
Specifically, our dataset has 68800 variables and around 6000 observations.
Matlab gives "out of memory" errors. I have tried also doing princomp in
pieces, but this does not seem to quite work for our approach.