This R pacakge is meant to be used for Cross-Entropy optimization, which is a global optimization method for both continuous and discrete parameters. It tends to outperform Differential Evolution in my local tests.
It also uses LauraeParallel load balancing for parallelization, which makes it suitable for long and dynamic optimization tasks.
Cross-Entropy optimization earned 3rd place (thanks to Laurae) in 2017 at the Ecole Nationale Supérieure metachallenge, earning several 1st and 2nd places in several challenges of the metachallenge (did you know Laurae did not receive any gift for such feat because the organizers ran out of gifts? now you know!).
Installation:
devtools::install_github("Laurae2/LauraeParallel")
devtools::install_github("Laurae2/LauraeCE")Original source: https://cran.r-project.org/web/packages/CEoptim/index.html
TO-DO:
- add parallelism
- add load balancing
-
add hot loading (use previous optimization)(this one was stupid because one could just use the previous mean/sd/probs) - add interrupt on the fly while saving data (tcltk?)
- add maximum computation time before cancelling (while returning cleanly)
This is how it currently looks and you will notice it is absurdly SLOW on very small tasks:
> suppressMessages(library(LauraeCE))
> suppressMessages(library(parallel))
>
>
> # Continuous Testing
>
> fun <- function(x){
+ return(3 * (1 - x[1]) ^ 2 * exp(-x[1] ^ 2 - (x[2] + 1) ^ 2) - 10 * (x[1] / 5 - x[1] ^ 3 - x[2] ^ 5) * exp(-x[1] ^ 2 - x[2] ^ 2) - 1 / 3 * exp(-(x[1] + 1) ^ 2 - x[2] ^ 2))
+ }
>
> mu0 <- c(-3, -3)
> sigma0 <- c(10, 10)
>
> system.time({
+ set.seed(11111)
+ res1 <- CEoptim::CEoptim(fun,
+ continuous = list(mean = mu0,
+ sd = sigma0),
+ maximize = TRUE)
+ })
user system elapsed
0.03 0.00 0.03
>
> system.time({
+ set.seed(11111)
+ res2 <- CEoptim(fun,
+ continuous = list(mean = mu0,
+ sd = sigma0),
+ maximize = TRUE)
+ })
user system elapsed
0.42 0.00 0.44
>
> cl <- makeCluster(2)
> system.time({
+ set.seed(11111)
+ res3 <- CEoptim(fun,
+ continuous = list(mean = mu0,
+ sd = sigma0),
+ maximize = TRUE,
+ parallelize = TRUE,
+ cl = cl)
+ })
user system elapsed
0.09 0.02 0.14
> stopCluster(cl)
> closeAllConnections()
>
> all.equal(res1$optimum, res2$optimum)
[1] TRUE
> all.equal(res1$optimum, res3$optimum)
[1] TRUE
>
>
> # Discrete Testing
>
> data(lesmis)
> fmaxcut <- function(x,costs) {
+ v1 <- which(x == 1)
+ v2 <- which(x == 0)
+ return(sum(costs[v1, v2]))
+ }
>
> p0 <- list()
> for (i in 1:77) {
+ p0 <- c(p0, list(rep(0.5, 2)))
+ }
> p0[[1]] <- c(0, 1)
>
> system.time({
+ set.seed(11111)
+ res1 <- CEoptim::CEoptim(fmaxcut,
+ f.arg = list(costs = lesmis),
+ maximize = TRUE,
+ verbose = TRUE,
+ discrete = list(probs = p0),
+ N = 3000L)
+ })
Number of continuous variables: 0
Number of discrete variables: 77
conMat=
NULL
conVec=
NULL
smoothMean: 1 smoothSd: 1 smoothProb: 1
N: 3000 rho: 0.1 iterThr: 10000 sdThr: 0.001 probThr 0.001
iter: 0 opt: 494 maxProbs: 0.5
iter: 1 opt: 501 maxProbs: 0.5
iter: 2 opt: 501 maxProbs: 0.5
iter: 3 opt: 501 maxProbs: 0.4966667
iter: 4 opt: 506 maxProbs: 0.5
iter: 5 opt: 510 maxProbs: 0.5
iter: 6 opt: 514 maxProbs: 0.5
iter: 7 opt: 515 maxProbs: 0.5
iter: 8 opt: 519 maxProbs: 0.4966667
iter: 9 opt: 523 maxProbs: 0.4933333
iter: 10 opt: 526 maxProbs: 0.4966667
iter: 11 opt: 528 maxProbs: 0.4933333
iter: 12 opt: 528 maxProbs: 0.4866667
iter: 13 opt: 530 maxProbs: 0.4966667
iter: 14 opt: 532 maxProbs: 0.49
iter: 15 opt: 532 maxProbs: 0.4733333
iter: 16 opt: 532 maxProbs: 0.4533333
iter: 17 opt: 533 maxProbs: 0.49
iter: 18 opt: 533 maxProbs: 0.4533333
iter: 19 opt: 533 maxProbs: 0.5
iter: 20 opt: 533 maxProbs: 0.4366667
iter: 21 opt: 533 maxProbs: 0.3766667
iter: 22 opt: 533 maxProbs: 0.3633333
user system elapsed
2.40 0.00 2.41
>
> system.time({
+ set.seed(11111)
+ res2 <- CEoptim(fmaxcut,
+ f.arg = list(costs = lesmis),
+ maximize = TRUE,
+ verbose = TRUE,
+ discrete = list(probs = p0),
+ N = 3000L)
+ })
Number of continuous variables: 0
Number of discrete variables: 77
conMat=
NULL
conVec=
NULL
smoothMean: 1 smoothSd: 1 smoothProb: 1
N: 3000 rho: 0.1 iterThr: 10000 sdThr: 0.001 probThr 0.001
Sat Dec 23 2017 02:36:45 PM - iter: 00001 (00s082ms, 36287.07 samples/s) - opt: 494 - maxProbs: 0.5
Sat Dec 23 2017 02:36:45 PM - iter: 00002 (00s066ms, 44985.61 samples/s) - opt: 501 - maxProbs: 0.5
Sat Dec 23 2017 02:36:45 PM - iter: 00003 (00s073ms, 40941.17 samples/s) - opt: 501 - maxProbs: 0.5
Sat Dec 23 2017 02:36:46 PM - iter: 00004 (00s068ms, 43923.99 samples/s) - opt: 501 - maxProbs: 0.4966667
Sat Dec 23 2017 02:36:46 PM - iter: 00005 (00s076ms, 39105.11 samples/s) - opt: 506 - maxProbs: 0.5
Sat Dec 23 2017 02:36:46 PM - iter: 00006 (00s143ms, 20878.71 samples/s) - opt: 510 - maxProbs: 0.5
Sat Dec 23 2017 02:36:46 PM - iter: 00007 (00s065ms, 45805.62 samples/s) - opt: 514 - maxProbs: 0.5
Sat Dec 23 2017 02:36:46 PM - iter: 00008 (00s070ms, 42742.95 samples/s) - opt: 515 - maxProbs: 0.5
Sat Dec 23 2017 02:36:46 PM - iter: 00009 (00s076ms, 39368.59 samples/s) - opt: 519 - maxProbs: 0.4966667
Sat Dec 23 2017 02:36:46 PM - iter: 00010 (00s081ms, 36701.63 samples/s) - opt: 523 - maxProbs: 0.4933333
Sat Dec 23 2017 02:36:46 PM - iter: 00011 (00s068ms, 43615.30 samples/s) - opt: 526 - maxProbs: 0.4966667
Sat Dec 23 2017 02:36:46 PM - iter: 00012 (00s086ms, 34672.88 samples/s) - opt: 528 - maxProbs: 0.4933333
Sat Dec 23 2017 02:36:47 PM - iter: 00013 (00s170ms, 17563.39 samples/s) - opt: 528 - maxProbs: 0.4866667
Sat Dec 23 2017 02:36:47 PM - iter: 00014 (00s060ms, 49344.64 samples/s) - opt: 530 - maxProbs: 0.4966667
Sat Dec 23 2017 02:36:47 PM - iter: 00015 (00s065ms, 45481.46 samples/s) - opt: 532 - maxProbs: 0.49
Sat Dec 23 2017 02:36:47 PM - iter: 00016 (00s157ms, 19045.67 samples/s) - opt: 532 - maxProbs: 0.4733333
Sat Dec 23 2017 02:36:47 PM - iter: 00017 (00s079ms, 37972.81 samples/s) - opt: 532 - maxProbs: 0.4533333
Sat Dec 23 2017 02:36:47 PM - iter: 00018 (00s069ms, 42964.58 samples/s) - opt: 533 - maxProbs: 0.49
Sat Dec 23 2017 02:36:47 PM - iter: 00019 (00s072ms, 41309.76 samples/s) - opt: 533 - maxProbs: 0.4533333
Sat Dec 23 2017 02:36:47 PM - iter: 00020 (00s066ms, 45445.80 samples/s) - opt: 533 - maxProbs: 0.5
Sat Dec 23 2017 02:36:47 PM - iter: 00021 (00s081ms, 36698.12 samples/s) - opt: 533 - maxProbs: 0.4366667
Sat Dec 23 2017 02:36:48 PM - iter: 00022 (00s065ms, 45617.55 samples/s) - opt: 533 - maxProbs: 0.3766667
Sat Dec 23 2017 02:36:48 PM - iter: 00023 (00s072ms, 41595.58 samples/s) - opt: 533 - maxProbs: 0.3633333
Sat Dec 23 2017 02:36:48 PM - iter: 00024 (00s128ms, 23352.47 samples/s) - opt: 533 - maxProbs: 0.3466667
user system elapsed
2.77 0.01 2.77
>
> cl <- makeCluster(2)
> system.time({
+ set.seed(11111)
+ res3 <- CEoptim(fmaxcut,
+ f.arg = list(costs = lesmis),
+ maximize = TRUE,
+ verbose = TRUE,
+ discrete = list(probs = p0),
+ N = 3000L,
+ parallelize = TRUE,
+ cl = cl)
+ })
Number of continuous variables: 0
Number of discrete variables: 77
conMat=
NULL
conVec=
NULL
smoothMean: 1 smoothSd: 1 smoothProb: 1
N: 3000 rho: 0.1 iterThr: 10000 sdThr: 0.001 probThr 0.001
Sat Dec 23 2017 02:36:50 PM - iter: 00001 (02s014ms, 1489.11 samples/s, 744.55 s/s/thread) - opt: 494 - maxProbs: 0.5
Sat Dec 23 2017 02:36:51 PM - iter: 00002 (00s682ms, 4395.58 samples/s, 2197.79 s/s/thread) - opt: 501 - maxProbs: 0.5
Sat Dec 23 2017 02:36:52 PM - iter: 00003 (01s691ms, 1773.06 samples/s, 886.53 s/s/thread) - opt: 501 - maxProbs: 0.5
Sat Dec 23 2017 02:36:54 PM - iter: 00004 (00s440ms, 6811.68 samples/s, 3405.84 s/s/thread) - opt: 501 - maxProbs: 0.4966667
Sat Dec 23 2017 02:36:55 PM - iter: 00005 (01s532ms, 1957.99 samples/s, 978.99 s/s/thread) - opt: 506 - maxProbs: 0.5
Sat Dec 23 2017 02:36:56 PM - iter: 00006 (01s619ms, 1852.11 samples/s, 926.05 s/s/thread) - opt: 510 - maxProbs: 0.5
Sat Dec 23 2017 02:36:57 PM - iter: 00007 (00s571ms, 5247.09 samples/s, 2623.54 s/s/thread) - opt: 514 - maxProbs: 0.5
Sat Dec 23 2017 02:36:59 PM - iter: 00008 (01s551ms, 1933.35 samples/s, 966.68 s/s/thread) - opt: 515 - maxProbs: 0.5
Sat Dec 23 2017 02:37:00 PM - iter: 00009 (01s431ms, 2095.47 samples/s, 1047.74 s/s/thread) - opt: 519 - maxProbs: 0.4966667
Sat Dec 23 2017 02:37:01 PM - iter: 00010 (00s544ms, 5514.17 samples/s, 2757.08 s/s/thread) - opt: 523 - maxProbs: 0.4933333
Sat Dec 23 2017 02:37:02 PM - iter: 00011 (01s449ms, 2070.38 samples/s, 1035.19 s/s/thread) - opt: 526 - maxProbs: 0.4966667
Sat Dec 23 2017 02:37:03 PM - iter: 00012 (01s438ms, 2085.91 samples/s, 1042.95 s/s/thread) - opt: 528 - maxProbs: 0.4933333
Sat Dec 23 2017 02:37:05 PM - iter: 00013 (00s497ms, 6034.04 samples/s, 3017.02 s/s/thread) - opt: 528 - maxProbs: 0.4866667
Sat Dec 23 2017 02:37:06 PM - iter: 00014 (01s495ms, 2006.57 samples/s, 1003.29 s/s/thread) - opt: 530 - maxProbs: 0.4966667
Sat Dec 23 2017 02:37:07 PM - iter: 00015 (01s589ms, 1887.85 samples/s, 943.93 s/s/thread) - opt: 532 - maxProbs: 0.49
Sat Dec 23 2017 02:37:08 PM - iter: 00016 (01s465ms, 2047.09 samples/s, 1023.55 s/s/thread) - opt: 532 - maxProbs: 0.4733333
Sat Dec 23 2017 02:37:10 PM - iter: 00017 (00s530ms, 5649.94 samples/s, 2824.97 s/s/thread) - opt: 532 - maxProbs: 0.4533333
Sat Dec 23 2017 02:37:11 PM - iter: 00018 (01s609ms, 1863.66 samples/s, 931.83 s/s/thread) - opt: 533 - maxProbs: 0.49
Sat Dec 23 2017 02:37:12 PM - iter: 00019 (01s451ms, 2066.34 samples/s, 1033.17 s/s/thread) - opt: 533 - maxProbs: 0.4533333
Sat Dec 23 2017 02:37:13 PM - iter: 00020 (00s557ms, 5377.65 samples/s, 2688.83 s/s/thread) - opt: 533 - maxProbs: 0.5
Sat Dec 23 2017 02:37:15 PM - iter: 00021 (01s630ms, 1839.57 samples/s, 919.78 s/s/thread) - opt: 533 - maxProbs: 0.4366667
Sat Dec 23 2017 02:37:16 PM - iter: 00022 (01s564ms, 1917.31 samples/s, 958.66 s/s/thread) - opt: 533 - maxProbs: 0.3766667
Sat Dec 23 2017 02:37:17 PM - iter: 00023 (01s426ms, 2103.27 samples/s, 1051.64 s/s/thread) - opt: 533 - maxProbs: 0.3633333
Sat Dec 23 2017 02:37:19 PM - iter: 00024 (00s485ms, 6179.45 samples/s, 3089.73 s/s/thread) - opt: 533 - maxProbs: 0.3466667
user system elapsed
17.10 7.63 30.55
> stopCluster(cl)
> closeAllConnections()
>
> all.equal(res1$optimizer$discrete, res2$optimizer$discrete)
[1] TRUE
> all.equal(res1$optimizer$discrete, res3$optimizer$discrete)
[1] TRUE
>
> cl <- makeCluster(2)
> system.time({
+ set.seed(11111)
+ res3 <- CEoptim(fmaxcut,
+ f.arg = list(costs = lesmis),
+ maximize = TRUE,
+ verbose = TRUE,
+ discrete = list(probs = p0),
+ N = 3000L,
+ max_time = 15,
+ parallelize = TRUE,
+ cl = cl)
+ })
Number of continuous variables: 0
Number of discrete variables: 77
conMat=
NULL
conVec=
NULL
smoothMean: 1 smoothSd: 1 smoothProb: 1
N: 3000 rho: 0.1 iterThr: 10000 sdThr: 0.001 probThr 0.001
Sat Dec 23 2017 02:37:21 PM - iter: 00001 (01s909ms, 1571.21 samples/s, 785.61 s/s/thread) - opt: 494 - maxProbs: 0.5
Sat Dec 23 2017 02:37:22 PM - iter: 00002 (01s501ms, 1998.43 samples/s, 999.21 s/s/thread) - opt: 501 - maxProbs: 0.5
Sat Dec 23 2017 02:37:23 PM - iter: 00003 (00s451ms, 6644.30 samples/s, 3322.15 s/s/thread) - opt: 501 - maxProbs: 0.5
Sat Dec 23 2017 02:37:24 PM - iter: 00004 (01s539ms, 1949.02 samples/s, 974.51 s/s/thread) - opt: 501 - maxProbs: 0.4966667
Sat Dec 23 2017 02:37:26 PM - iter: 00005 (01s617ms, 1854.80 samples/s, 927.40 s/s/thread) - opt: 506 - maxProbs: 0.5
Sat Dec 23 2017 02:37:27 PM - iter: 00006 (00s426ms, 7027.42 samples/s, 3513.71 s/s/thread) - opt: 510 - maxProbs: 0.5
Sat Dec 23 2017 02:37:28 PM - iter: 00007 (01s526ms, 1965.72 samples/s, 982.86 s/s/thread) - opt: 514 - maxProbs: 0.5
Sat Dec 23 2017 02:37:29 PM - iter: 00008 (01s603ms, 1870.60 samples/s, 935.30 s/s/thread) - opt: 515 - maxProbs: 0.5
Sat Dec 23 2017 02:37:31 PM - iter: 00009 (00s483ms, 6203.28 samples/s, 3101.64 s/s/thread) - opt: 519 - maxProbs: 0.4966667
Sat Dec 23 2017 02:37:32 PM - iter: 00010 (01s520ms, 1973.57 samples/s, 986.79 s/s/thread) - opt: 523 - maxProbs: 0.4933333
Sat Dec 23 2017 02:37:33 PM - iter: 00011 (01s528ms, 1962.99 samples/s, 981.50 s/s/thread) - opt: 526 - maxProbs: 0.4966667
Sat Dec 23 2017 02:37:34 PM - iter: 00012 (00s511ms, 5869.28 samples/s, 2934.64 s/s/thread) - opt: 528 - maxProbs: 0.4933333
Sat Dec 23 2017 02:37:36 PM - iter: 00013 (01s484ms, 2021.28 samples/s, 1010.64 s/s/thread) - opt: 528 - maxProbs: 0.4866667
user system elapsed
8.96 4.17 16.38
> stopCluster(cl)
> closeAllConnections()
> all.equal(res1$optimizer$discrete, res3$optimizer$discrete)
[1] "Mean relative difference: 1.8"
>
>
> # Mixed Input (Continuous + Discrete) Testing
>
> sumsqrs <- function(theta, rm1, x) {
+ N <- length(x) #without x[0]
+ r <- 1 + sort(rm1) # internal end points of regimes
+ if (r[1] == r[2]) { # test for invalid regime
+ return(Inf);
+ }
+ thetas <- rep(theta, times = c(r, N) - c(1, r + 1) + 1)
+ xhat <- c(0, head(x, -1)) * thetas
+ # Compute sum of squared errors
+ sum((x - xhat) ^ 2)
+ }
>
> data(yt)
> xt <- yt - c(0, yt[-300])
>
> A <- rbind(diag(3), -diag(3))
> b <- rep(1, 6)
>
> system.time({
+ set.seed(11111)
+ res1 <- CEoptim::CEoptim(f = sumsqrs,
+ f.arg = list(xt),
+ continuous = list(mean = c(0, 0,0),
+ sd = rep(1, 0,3),
+ conMat = A,
+ conVec = b),
+ discrete = list(categories = c(298L, 298L),
+ smoothProb = 0.5),
+ N = 10000,
+ rho = 0.001,
+ verbose = TRUE)
+ })
Number of continuous variables: 3
Number of discrete variables: 2
conMat=
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 1 0
[3,] 0 0 1
[4,] -1 0 0
[5,] 0 -1 0
[6,] 0 0 -1
conVec=
[1] 1 1 1 1 1 1
smoothMean: 1 smoothSd: 1 smoothProb: 0.5
N: 10000 rho: 0.001 iterThr: 10000 sdThr: 0.001 probThr 0.001
iter: 0 opt: 3.009517 maxSd: 0.3248419 maxProbs: 0.9483221
iter: 1 opt: 2.70702 maxSd: 0.07449603 maxProbs: 0.7741611
iter: 2 opt: 2.688896 maxSd: 0.04549593 maxProbs: 0.7495805
iter: 3 opt: 2.677602 maxSd: 0.03024808 maxProbs: 0.6247903
iter: 4 opt: 2.675769 maxSd: 0.006000473 maxProbs: 0.4498951
iter: 5 opt: 2.675727 maxSd: 0.000613875 maxProbs: 0.2249476
iter: 6 opt: 2.675727 maxSd: 7.4365e-05 maxProbs: 0.1124738
iter: 7 opt: 2.675727 maxSd: 4.23201e-06 maxProbs: 0.05623689
iter: 8 opt: 2.675727 maxSd: 4.225456e-07 maxProbs: 0.02811845
iter: 9 opt: 2.675727 maxSd: 3.376241e-08 maxProbs: 0.01405922
iter: 10 opt: 2.675727 maxSd: 7.751173e-09 maxProbs: 0.007029611
iter: 11 opt: 2.675727 maxSd: 2.775761e-09 maxProbs: 0.003514806
iter: 12 opt: 2.675727 maxSd: 1.736754e-09 maxProbs: 0.001757403
user system elapsed
9.06 0.02 9.10
>
> system.time({
+ set.seed(11111)
+ res2 <- CEoptim(f = sumsqrs,
+ f.arg = list(xt),
+ continuous = list(mean = c(0, 0,0),
+ sd = rep(1, 0,3),
+ conMat = A,
+ conVec = b),
+ discrete = list(categories = c(298L, 298L),
+ smoothProb = 0.5),
+ N = 10000,
+ rho = 0.001,
+ verbose = TRUE)
+ })
Number of continuous variables: 3
Number of discrete variables: 2
conMat=
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 1 0
[3,] 0 0 1
[4,] -1 0 0
[5,] 0 -1 0
[6,] 0 0 -1
conVec=
[1] 1 1 1 1 1 1
smoothMean: 1 smoothSd: 1 smoothProb: 0.5
N: 10000 rho: 0.001 iterThr: 10000 sdThr: 0.001 probThr 0.001
Sat Dec 23 2017 02:37:45 PM - iter: 00001 (00s578ms, 17290.24 samples/s) - opt: 3.009517 - maxSd: 0.3248419 - maxProbs: 0.9483221
Sat Dec 23 2017 02:37:46 PM - iter: 00002 (00s605ms, 16510.56 samples/s) - opt: 2.70702 - maxSd: 0.07449603 - maxProbs: 0.7741611
Sat Dec 23 2017 02:37:47 PM - iter: 00003 (00s644ms, 15522.24 samples/s) - opt: 2.688896 - maxSd: 0.04549593 - maxProbs: 0.7495805
Sat Dec 23 2017 02:37:47 PM - iter: 00004 (00s559ms, 17870.68 samples/s) - opt: 2.677602 - maxSd: 0.03024808 - maxProbs: 0.6247903
Sat Dec 23 2017 02:37:48 PM - iter: 00005 (00s564ms, 17700.87 samples/s) - opt: 2.675769 - maxSd: 0.006000473 - maxProbs: 0.4498951
Sat Dec 23 2017 02:37:49 PM - iter: 00006 (00s611ms, 16348.60 samples/s) - opt: 2.675727 - maxSd: 0.000613875 - maxProbs: 0.2249476
Sat Dec 23 2017 02:37:49 PM - iter: 00007 (00s651ms, 15338.11 samples/s) - opt: 2.675727 - maxSd: 7.4365e-05 - maxProbs: 0.1124738
Sat Dec 23 2017 02:37:50 PM - iter: 00008 (00s660ms, 15143.02 samples/s) - opt: 2.675727 - maxSd: 4.23201e-06 - maxProbs: 0.05623689
Sat Dec 23 2017 02:37:51 PM - iter: 00009 (00s575ms, 17381.87 samples/s) - opt: 2.675727 - maxSd: 4.225456e-07 - maxProbs: 0.02811845
Sat Dec 23 2017 02:37:51 PM - iter: 00010 (00s572ms, 17476.10 samples/s) - opt: 2.675727 - maxSd: 3.376241e-08 - maxProbs: 0.01405922
Sat Dec 23 2017 02:37:52 PM - iter: 00011 (00s654ms, 15274.31 samples/s) - opt: 2.675727 - maxSd: 7.751173e-09 - maxProbs: 0.007029611
Sat Dec 23 2017 02:37:53 PM - iter: 00012 (00s644ms, 15524.89 samples/s) - opt: 2.675727 - maxSd: 2.775761e-09 - maxProbs: 0.003514806
Sat Dec 23 2017 02:37:53 PM - iter: 00013 (00s669ms, 14946.07 samples/s) - opt: 2.675727 - maxSd: 1.736754e-09 - maxProbs: 0.001757403
Sat Dec 23 2017 02:37:54 PM - iter: 00014 (00s732ms, 13657.75 samples/s) - opt: 2.675727 - maxSd: 1.219703e-09 - maxProbs: 0.0008787014
user system elapsed
9.09 0.00 9.09
>
> cl <- makeCluster(2)
> system.time({
+ set.seed(11111)
+ res3 <- CEoptim(f = sumsqrs,
+ f.arg = list(xt),
+ continuous = list(mean = c(0, 0,0),
+ sd = rep(1, 0,3),
+ conMat = A,
+ conVec = b),
+ discrete = list(categories = c(298L, 298L),
+ smoothProb = 0.5),
+ N = 10000,
+ rho = 0.001,
+ verbose = TRUE,
+ parallelize = TRUE,
+ cl = cl)
+ })
Number of continuous variables: 3
Number of discrete variables: 2
conMat=
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 0 1 0
[3,] 0 0 1
[4,] -1 0 0
[5,] 0 -1 0
[6,] 0 0 -1
conVec=
[1] 1 1 1 1 1 1
smoothMean: 1 smoothSd: 1 smoothProb: 0.5
N: 10000 rho: 0.001 iterThr: 10000 sdThr: 0.001 probThr 0.001
Sat Dec 23 2017 02:37:57 PM - iter: 00001 (02s695ms, 3710.54 samples/s, 1855.27 s/s/thread) - opt: 3.009517 - maxSd: 0.3248419 - maxProbs: 0.9483221
Sat Dec 23 2017 02:37:59 PM - iter: 00002 (01s692ms, 5907.40 samples/s, 2953.70 s/s/thread) - opt: 2.70702 - maxSd: 0.07449603 - maxProbs: 0.7741611
Sat Dec 23 2017 02:38:01 PM - iter: 00003 (02s838ms, 3522.83 samples/s, 1761.42 s/s/thread) - opt: 2.688896 - maxSd: 0.04549593 - maxProbs: 0.7495805
Sat Dec 23 2017 02:38:04 PM - iter: 00004 (01s740ms, 5745.79 samples/s, 2872.89 s/s/thread) - opt: 2.677602 - maxSd: 0.03024808 - maxProbs: 0.6247903
Sat Dec 23 2017 02:38:06 PM - iter: 00005 (02s531ms, 3950.54 samples/s, 1975.27 s/s/thread) - opt: 2.675769 - maxSd: 0.006000473 - maxProbs: 0.4498951
Sat Dec 23 2017 02:38:08 PM - iter: 00006 (02s708ms, 3691.60 samples/s, 1845.80 s/s/thread) - opt: 2.675727 - maxSd: 0.000613875 - maxProbs: 0.2249476
Sat Dec 23 2017 02:38:10 PM - iter: 00007 (01s610ms, 6210.28 samples/s, 3105.14 s/s/thread) - opt: 2.675727 - maxSd: 7.4365e-05 - maxProbs: 0.1124738
Sat Dec 23 2017 02:38:13 PM - iter: 00008 (02s847ms, 3511.41 samples/s, 1755.71 s/s/thread) - opt: 2.675727 - maxSd: 4.23201e-06 - maxProbs: 0.05623689
Sat Dec 23 2017 02:38:15 PM - iter: 00009 (01s692ms, 5908.85 samples/s, 2954.42 s/s/thread) - opt: 2.675727 - maxSd: 4.225456e-07 - maxProbs: 0.02811845
Sat Dec 23 2017 02:38:18 PM - iter: 00010 (02s934ms, 3407.62 samples/s, 1703.81 s/s/thread) - opt: 2.675727 - maxSd: 3.376241e-08 - maxProbs: 0.01405922
Sat Dec 23 2017 02:38:20 PM - iter: 00011 (02s522ms, 3965.07 samples/s, 1982.53 s/s/thread) - opt: 2.675727 - maxSd: 7.751173e-09 - maxProbs: 0.007029611
Sat Dec 23 2017 02:38:22 PM - iter: 00012 (01s564ms, 6390.96 samples/s, 3195.48 s/s/thread) - opt: 2.675727 - maxSd: 2.775761e-09 - maxProbs: 0.003514806
Sat Dec 23 2017 02:38:24 PM - iter: 00013 (02s707ms, 3693.70 samples/s, 1846.85 s/s/thread) - opt: 2.675727 - maxSd: 1.736754e-09 - maxProbs: 0.001757403
Sat Dec 23 2017 02:38:26 PM - iter: 00014 (01s462ms, 6838.37 samples/s, 3419.18 s/s/thread) - opt: 2.675727 - maxSd: 1.219703e-09 - maxProbs: 0.0008787014
user system elapsed
20.54 5.69 32.11
> stopCluster(cl)
> closeAllConnections()
>
> all.equal(res1$optimum, res2$optimum)
[1] TRUE
> all.equal(res1$optimum, res3$optimum)
[1] TRUE