Hi Ravi!
Ravi Varadhan wrote:
>
> Yes. Most classical optimization methods (e.g. gradient-type,
> Newton-type) are "local", i.e. they do not attempt to locate the
> global optimum.
Ah .. I see.
> The primary difficulty with global optimization is that there are no
> mathematical conditions tha
Berend Hasselman wrote:
If you do
resopt <- optim(-5,f, method="SANN",control=list(fnscale=-1))
you will get the global maximum. SANN: simulated annealing. But starting
in -4 takes you to the local maximum.
So if I understand correctly, this method would also yield the same
sort of result
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
- Original Message -
From: Esmail
Date: Sunday, May 24, 2009 8:27 am
Subject: Re: [R] using optimize() correctly
On 24-05-2009, at 14:24, Esmail wrote:
Hello Berend,
Berend Hasselman wrote:
Your function is not unimodal.
The help for optimize states: "If f is not unimodal, then
optimize() may approximate a local, but perhaps
non-global, minimum to the same accuracy."
Ah ok, I didn't read the manual
Hello Berend,
Berend Hasselman wrote:
Your function is not unimodal.
The help for optimize states:
"If f is not unimodal, then optimize() may approximate a local, but perhaps
non-global, minimum to the same accuracy."
Ah ok, I didn't read the manual page carefully enough.
Do you know if
Esmail Bonakdarian-4 wrote:
>
> Hi,
>
> I am trying to use the optimize function to optimize a function. The
> results I am getting don't agree with what I compute on my own and
> when I look at the graph of
>
> f(x) = 100 + ((x-10)**2 + (x-10)) * cos(x-10), where -10 <= x <= 10
>
> in
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