[R] temp seems ineffective in SANN (optim)

Wed Feb 27 11:42:06 CET 2013

I don't have an answer for your real question, but
you might try a different optimizer.  The blog post:

http://www.portfolioprobe.com/2012/07/23/a-comparison-of-some-heuristic-optimization-methods/

shows SANN to not do very well relative to other
heuristic optimizers.  That is for one problem, but
it could well be true for your problem as well.

Pat

On 27/02/2013 00:31, Ross Boylan wrote:
> I am trying to control the behavior of the SANN method in optim (R
> 2.14.1) via control$temp.  In my toy tests it works; in my real use, it
> doesn't.
>
> As far as I can tell my code with different temp values is loaded; I
> even traced into the function that calls optim and verified temp had the
> value I had set.
>
> Could the fact that I have NaN's coming back from the objective function
> be a factor?  Here are the results I've gotten from 20 iterations with
> temp varying from 2 to 9.  The first column is the value of the
> objective function, and the rest are the parameter values (the objective
> function is augmented to leave a trace).  The rows represent SANN's
> different guesses, in sequence.
>> history9
>             [,1]      [,2]        [,3]        [,4]      [,5]        [,6]
>   [1,] -3507.346 -4.500000  1.00000000  1.00000000 1.0000000  0.69314718
>   [2,] -3828.071 -3.942424  0.03090623  0.30739233 1.7062554 -0.01814918
>   [3,] -4007.624 -3.126794  1.79592189  1.41855332 1.2060574  1.54479512
>   [4,]       NaN -4.064653 -0.25017279  1.30476170 0.2559306 -0.31140650
>   [5,] -4222.272 -3.058714 -0.93063613 -0.54296159 0.8287307  1.92103676
>   [6,]       NaN -3.833080  1.00721123  1.66564249 0.7923725 -0.04967723
>   [7,]       NaN -5.050322 -0.45545409  0.83209653 1.4976764 -0.47211795
>   [8,]       NaN -3.717588  0.62400594  0.73424007 0.1359730  1.62073131
>   [9,]       NaN -6.078701  0.10000219  0.36961894 0.2633589  0.67651053
> [10,]       NaN -3.404865  2.92992664  1.45204623 0.2020535  1.49936000
> [11,]       NaN -3.387337  2.17682158  0.06994319 1.1717615  0.68526889
> [12,]       NaN -4.534316  0.88676089  1.34499190 0.9148238  0.98417597
> [13,]       NaN -4.445174  1.06230896  1.51960345 0.4651780  1.14127715
> [14,] -3784.848 -4.007890  0.77866330  1.01243770 1.1957120  1.33305656
> [15,]       NaN -3.707500  1.30038651  1.30480610 0.6210218  0.81355299
> [16,] -3730.219 -4.155193  0.76779830  1.06686987 1.0546294  1.45601474
> [17,] -3524.462 -5.074722  1.21296408  0.59787431 0.9228195  1.07755859
> [18,] -3588.086 -5.146427  1.28721218  0.74634447 1.1107613  0.63009540
> [19,] -3715.411 -4.501889  0.72491408  0.75046935 0.8476556  1.64229603
> [20,] -3711.158 -4.813507  0.88125227  1.10291836 0.1452430  0.07181056
>               [,7]       [,8]      [,9]    [,10]
>   [1,]  0.00000000  0.5493061 -4.500000 4.000000
>   [2,] -1.33969887  2.6881171 -5.797714 4.712738
>   [3,]  1.10373337  1.5164159 -4.666298 4.551507
>   [4,]  0.36425367  0.5755519 -3.558595 3.811114
>   [5,] -0.77555882  0.4863321 -5.060481 4.987640
>   [6,] -1.14686363  0.5164433 -4.759286 3.650409
>   [7,] -0.43179263  1.1326352 -4.611431 3.920483
>   [8,]  1.67696259  0.8754158 -4.352415 3.095768
>   [9,]  1.10927659  0.5779504 -4.952128 4.649442
> [10,] -0.67478207  2.8174240 -4.704395 2.986569
> [11,]  0.45878472  0.6479467 -4.122482 2.934156
> [12,] -0.04871212  0.9457826 -4.617438 4.377056
> [13,] -0.01321339  0.3833625 -4.591240 4.729049
> [14,] -0.49075803  0.3322742 -3.971298 4.357731
> [15,]  0.16922427  0.4820518 -4.683029 3.875409
> [16,] -0.18047923 -0.4957090 -4.492014 4.317694
> [17,] -0.28481705  0.1923373 -4.288773 3.956130
> [18,]  0.12102775 -0.2332984 -4.981987 4.301450
> [19,]  0.15961575  1.1644561 -4.459003 3.777286
> [20,] -0.24130528  0.6126422 -4.075133 3.628426
>> sum(is.nan(history9[,1]))
> [1] 10
>> max(abs(history9-history5), na.rm=TRUE)
> [1] 9.094947e-13
> # historyN has a temp of N
>
> BTW the values of the objective function have their sign reversed to
> make it a maximization problem.
>
> Ross Boylan
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

-- 
Patrick Burns
pburns at pburns.seanet.com
twitter: @burnsstat @portfolioprobe
http://www.portfolioprobe.com/blog
http://www.burns-stat.com
(home of:
  'Impatient R'
  'The R Inferno'
  'Tao Te Programming')