[R] Maximum likelihood with analytical Hessian and
robin at lindinglab.org
Thu Dec 18 23:03:30 CET 2014
Thank you for your suggestions.
I'll have a look at the trust package - the trust zone may be doing what
The tanh transformation could be a good alternative too.
On 18. 12. 14 15:10, Prof J C Nash (U30A) wrote:
> Of the tools I know (and things change every day!), only package trust
> uses the Hessian explicitly.
> It would not be too difficult to include explicit Hessian by modifying
> Rvmmin which is all in R -- I'm currently doing some cleanup on that, so
> ask offline if you choose that route.
> Given that some parameters are between 0 and 1, you could use the
> hyperbolic transformation (section 11.2 of my book Nonlinear parameter
> optimization using R tools) with trust, and I think I'd try that as a
> first attempt. You probably need to adjust the Hessian for the
> transformation carefully.
> Generally the work in computing the Hessian ( # obs * (# parameters)^2
> in size) is not worth the effort, but there are problems for which it
> does make a lot of sense.
> On 14-12-18 06:00 AM, r-help-request at r-project.org wrote:
>> Message: 12
>> Date: Wed, 17 Dec 2014 21:46:16 +0100
>> From: Xavier Robin <robin at lindinglab.org>
>> To: r-help at r-project.org
>> Subject: [R] Maximum likelihood with analytical Hessian and
>> Message-ID: <5491EB98.6090606 at lindinglab.org>
>> Content-Type: text/plain; charset=utf-8
>> Dear list,
>> I have an optimization problem that I would like to solve by Maximum
>> I have analytical functions for the first and second derivatives of my
>> In addition, some parameters are constrained between 0 and 1, while some
>> others can vary freely between -Inf and +Inf.
>> I am looking for an optimization function to solve this problem.
>> I understand that the base optim function doesn't take a Hessian
>> function, it only computes it numerically.
>> I found the maxLik package that takes the function as a "hess" parameter
>> but the maxNR method (the only one that uses the Hessian function) can't
>> be bounded.
>> Surprisingly I couldn't find a function doing both.
>> Any suggestions for a function doing bounded optimization with an
>> analytical Hessian function?
Xavier Robin, PhD
Cellular Signal Integration Group (C-SIG) - Linding Lab
Biotech Research and Innovation Center (BRIC) - University of Copenhagen
Anker Engelundsvej, DTU Campus, Building 301, DK-2800 Lyngby, DENMARK
Mobile: +45 42 799 833
www.lindinglab.org - www.bric.ku.dk
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