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
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Patrick Burns
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twitter: @burnsstat @portfolioprobe
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and provide commented, minimal, self-contained, reproducible code.
