Karl:
My ignorance of optimization makes any further comments hazardous.
Indeed, my initial reply may already have gone too far, as I my not
either understand you or NM. So I'm just going to shut up.
-- Bert
On Sat, Aug 18, 2012 at 9:33 AM, Karl Ove Hufthammer <k...@huftis.org> wrote:
> You’re right that the step size should be effectively adjusted using
> alpha, beta and gamma in later iterations, but the problem is that the
> values used for the first simplex generated depends on the differences
> between the initial values, which makes no sense, as this make
> optimisation problem not invariant to translations.
>
> Here’s an analogy. Think of the function to maximise as a mountain
> placed somewhere on Earth. If you start 1 km east and 1 km north of the
> mountain, and try to find its peak, the values you sample *relative to
> the peak’s position* should not depend on whether the mountain is
> situated on Equator, in Australia or in North America, as long as the
> actual mountain is identical (i.e., there is no *scaling* of the
> function, only a translation). But for optim with method="Nelder-Mead"
> they seem to do so.
>
> Also, the values of parscale seem to have a rather mysterious effect on
> the values chosen for later iterations, while their (absolute) values
> seems to have *no* effect on the initial simplex (but their relative
> values do have an effect, and a correct effect, AFAICS).
>
>
> Karl Ove Hufthammer
>
>
>
> la. den 18. 08. 2012 klokka 07.32 (-0700) skreiv Bert Gunter:
>> Well, I'm no optimization guru, but a quick reading of Wikipedia said
>> tha step size depends on the initial value configuration and is then
>> "adjusted" by the algorithm using alpha, beta and gamma scaling
>> parameters thru the optimization. So it seems that it is supposed to
>> work exactly as you describe. Why do you expect something else?
>>
>> -- Bert
>>
>> On Sat, Aug 18, 2012 at 2:30 AM, Karl Ove Hufthammer <k...@huftis.org> wrote:
>> > Dear all,
>> >
>> > I’m having some problems getting optim with method="Nelder-Mead" to work
>> > properly. It seems like there is no way of controlling the step size,
>> > and the step size seems to depend on the *difference* between the
>> > initial values, which makes no sense. Example:
>> >
>> > f=function(xy, mu1, mu2) {
>> > print(xy)
>> > dnorm(xy[1]-mu1)*dnorm(xy[2]-mu2)
>> > }
>> > f1=function(xy) -f(xy, 0, 0)
>> > optim(c(1,1), f1)
>> >
>> > The first four values evaluated are
>> >
>> > 1.0, 1.0
>> > 1.1, 1.0
>> > 1.0, 1.1
>> > 0.9, 1.1
>> >
>> > which is reasonable (step size of 0.1) for this function. And if I
>> > translate both the function and the initial values
>> >
>> > f2=function(xy) -f(xy, 5000, 5000)
>> > optim(c(5001,5001), f2)
>> >
>> > the first four values are
>> >
>> > 5001.0, 5001.0
>> > 5501.1, 5001.0
>> > 5001.0, 5501.1
>> > 4500.9, 5501.1
>> >
>> > With
>> >
>> > f3=function(xy) -f(xy, 0, 5000)
>> > optim(c(1,5001), f3)
>> >
>> > they are
>> >
>> > 1.0, 5001.0
>> > 501.1, 5001.0
>> > 1.0, 5501.1
>> > -499.1, 5501.1
>> >
>> > and with
>> >
>> > f4=function(xy) -f(xy, -3000, 50000)
>> > optim(c(-2999,50001), f4)
>> >
>> > -2999.0, 50001.0
>> > 2001.1, 50001.0
>> > -2999.0, 55001.1
>> > -7999.1, 55001.1
>> >
>> > However, the function to optimise is the same in all cases, only
>> > translated, not scaled, so the step size *should* be the same. From
>> > reading the documentation, it looks like changing the parscale should
>> > work, and *relative* changes have the intended effect. Example:
>> >
>> > optim(c(1,1), f1, control=list(parscale=c(1,5)))
>> >
>> > gives the function evaluations
>> >
>> > 1.0, 1.0
>> > 1.1, 1.0
>> > 1.0, 1.5
>> > 1.1, 0.5
>> >
>> > But changing both values, e.g.,
>> >
>> > optim(c(1,1), f1, control=list(parscale=c(500,500)))
>> >
>> > gives the same first four values. There *are* eventually some
>> > differences in the values tried, but these don’t seem to correspond to
>> > parscale as described in ?optim. For example, for parscale=c(1,1), the
>> > parameter values tried are
>> >
>> > 1: 1, 1
>> > 2: 1.1, 1
>> > 3: 1, 1.1
>> > 4: 0.9, 1.1
>> > 5: 0.95, 1.075
>> > 6: 0.9, 1
>> > 7: 0.85, 0.95
>> > 8: 0.95, 0.85
>> > 9: 0.9375, 0.9125
>> > 10: 0.8, 0.8
>> > 11: 0.7, 0.7
>> > 12: 0.8, 0.6
>> > 13: 0.8125, 0.6875
>> > 14: 0.55, 0.45
>> >
>> > while for parscale=c(500,500) they are
>> >
>> > 1: 1, 1
>> > 2: 1.1, 1
>> > 3: 1, 1.1
>> > 4: 0.9, 1.1
>> > 5: 0.95, 1.075
>> > 6: 0.9, 1
>> > 7: 0.85, 0.95
>> > 8: 0.95, 0.85
>> > 9: 0.975, 0.725
>> > 10: 0.825, 0.675
>> > 11: 0.7375, 0.5125
>> > 12: 0.8625, 0.2875
>> > 13: 0.859375, 0.453125
>> > 14: 0.625000000000001, 0.0750000000000004
>> >
>> > for parscale=1/c(50000,50000) they are
>> >
>> > 1: 1, 1
>> > 2: 1.1, 1
>> > 3: 1, 1.1
>> > 4: 0.9, 1.1
>> > 5: 0.95, 1.075
>> > 6: 0.9, 1
>> > 7: 0.85, 0.95
>> > 8: 0.95, 0.85
>> > 9: 0.9375, 0.9125
>> > 10: 0.8, 0.8
>> > 11: 0.7, 0.7
>> > 12: 0.8, 0.6
>> > 13: 0.8125, 0.6875
>> > 14: 0.55, 0.45
>> >
>> > And there seems to be no way of actually changing the step size to
>> > reasonable values (i.e., the same values for optimising f1–f4).
>> >
>> > Is there something I have missed in how one is supposed to use optim
>> > with Nelder-Mead? Or is this actually a bug in the implementation?
>> >
>> >
>> > $ sessionInfo()
>> > R version 2.15.1 (2012-06-22)
>> > Platform: x86_64-suse-linux-gnu (64-bit)
>> >
>> > locale:
>> > [1] LC_CTYPE=nn_NO.UTF-8 LC_NUMERIC=C
>> > [3] LC_TIME=nn_NO.UTF-8 LC_COLLATE=nn_NO.UTF-8
>> > [5] LC_MONETARY=nn_NO.UTF-8 LC_MESSAGES=nn_NO.UTF-8
>> > [7] LC_PAPER=C LC_NAME=C
>> > [9] LC_ADDRESS=C LC_TELEPHONE=C
>> > [11] LC_MEASUREMENT=nn_NO.UTF-8 LC_IDENTIFICATION=C
>> >
>> > attached base packages:
>> > [1] stats graphics grDevices utils datasets methods base
>> >
>
--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
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