• using R version 2.11.1 Patched (2010-07-29 r52657)
• using session charset: ISO8859-1
• checking for file 'optimx/DESCRIPTION' ... OK
• this is package 'optimx' version '0.84'
• checking package name space information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
• checking whether package 'optimx' can be installed ... OK
• checking package directory ... OK
• checking for portable file names ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
• checking index information ... OK
• checking package subdirectories ... OK
• checking R files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
• checking whether the package can be loaded ... OK
• checking whether the package can be loaded with stated dependencies ... OK
• checking whether the package can be unloaded cleanly ... OK
• checking whether the name space can be loaded with stated dependencies ... OK
• checking whether the name space can be unloaded cleanly ... OK
• checking for unstated dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
• checking replacement functions ... OK
• checking foreign function calls ... OK
• checking R code for possible problems ... OK
• checking Rd files ... OK
• checking Rd metadata ... OK
• checking Rd cross-references ... OK
• checking for missing documentation entries ... OK
• checking for code/documentation mismatches ... OK
• checking Rd \usage sections ... OK
• checking Rd contents ... OK
• checking examples ... ERROR
  Running examples in 'optimx-Ex.R' failed.
  The error most likely occurred in:
  
  > ### * optimx
  >
  > flush(stderr()); flush(stdout())
  >
  > ### Name: optimx
  > ### Title: General-purpose optimization
  > ### Aliases: optimx
  > ### Keywords: nonlinear optimize
  >
  > ### ** Examples
  >
  > require(graphics)
  >
  > fr <- function(x) { ## Rosenbrock Banana function
  + x1 <- x[1]
  + x2 <- x[2]
  + 100 * (x2 - x1 * x1)^2 + (1 - x1)^2
  + }
  > grr <- function(x) { ## Gradient of 'fr'
  + x1 <- x[1]
  + x2 <- x[2]
  + c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1),
  + 200 * (x2 - x1 * x1))
  + }
  > ans1<-optimx(c(-1.2,1), fr)
  > print(ans1)
  par fvalues method fns grs itns conv KKT1 KKT2
  1 1.000260, 1.000506 8.825241e-08 Nelder-Mead 195 NA NULL 0 FALSE TRUE
  2 0.9998044, 0.9996084 3.827383e-08 BFGS 118 38 NULL 0 TRUE TRUE
  xtimes
  1 0
  2 0.02
  > print(attr(ans1,"details"))
  [[1]]
  [[1]]$par
  [1] 1.000260 1.000506
  
  [[1]]$value
  [1] 8.825241e-08
  
  [[1]]$convergence
  [1] 0
  
  [[1]]$message
  NULL
  
  [[1]]$conv
  [1] 0
  
  [[1]]$fevals
  function
  195
  
  [[1]]$gevals
  gradient
  NA
  
  [[1]]$kkt1
  [1] FALSE
  
  [[1]]$kkt2
  [1] TRUE
  
  [[1]]$ngatend
  [1] 0.006260098 -0.002869164
  
  [[1]]$nhatend
  [,1] [,2]
  [1,] 802.4220 -400.1041
  [2,] -400.1041 200.0000
  
  [[1]]$evnhatend
  [1] 1002.0216761 0.4003383
  
  [[1]]$systime
  user.self
  0
  
  [[1]]$method
  [1] "Nelder-Mead"
  
  
  [[2]]
  [[2]]$par
  [1] 0.9998044 0.9996084
  
  [[2]]$value
  [1] 3.827383e-08
  
  [[2]]$convergence
  [1] 0
  
  [[2]]$message
  NULL
  
  [[2]]$conv
  [1] 0
  
  [[2]]$fevals
  function
  118
  
  [[2]]$gevals
  gradient
  38
  
  [[2]]$kkt1
  [1] TRUE
  
  [[2]]$kkt2
  [1] TRUE
  
  [[2]]$ngatend
  [1] -0.0001815403 -0.0001048171
  
  [[2]]$nhatend
  [,1] [,2]
  [1,] 801.6873 -399.9218
  [2,] -399.9218 200.0000
  
  [[2]]$evnhatend
  [1] 1001.2878060 0.3995274
  
  [[2]]$systime
  user.self
  0.02
  
  [[2]]$method
  [1] "BFGS"
  
  
  > cat("\n\n")
  
  
  > ans2<-optimx(c(-1.2,1), fr, grr, method = "BFGS")
  > print(ans2)
  par fvalues method fns grs itns conv KKT1 KKT2 xtimes
  1 1, 1 9.594956e-18 BFGS 110 43 NULL 0 TRUE TRUE 0
  > ## The next line will fail if executed because 'hessian = TRUE' no longer allowed
  > # ans3<-optimx(c(-1.2,1), fr, NULL, method = "BFGS", hessian = TRUE)
  > cat("\n\n")
  
  
  > ans4<-optimx(c(-1.2,1), fr, grr, method = "CG",control=list(trace=TRUE))
  fn is fr
  Function has 2 arguments
  Analytic gradient from function grr
  
  Analytic Hessian not made available.
  Looking for method = CG
  Scale check -- log parameter ratio= 0.07918125 log bounds ratio= NA
  Method: CG
  Conjugate gradients function minimizer
  Method: Fletcher Reeves
  tolerance used in gradient test=3.63798e-12
  0 1 24.200000
  parameters -1.20000 1.00000
  **** i< 1 7 4.132161
  parameters -1.02752 1.07040
• i> 2 10 4.126910
  parameters -1.02855 1.06882
  **** i> 3 16 4.121409
  parameters -1.02924 1.06533
  i> 4 18 4.106523
  parameters -1.02586 1.05731
  **** i> 5 24 4.100955
  parameters -1.02261 1.05573
  i> 6 26 4.086136
  parameters -1.01839 1.04818
  **** i> 7 32 4.080524
  parameters -1.01914 1.04464
  i> 8 34 4.065787
  parameters -1.01579 1.03670
  **** i> 9 40 4.060127
  parameters -1.01250 1.03514
  i> 10 42 4.045415
  parameters -1.00824 1.02768
  **** i> 11 48 4.039717
  parameters -1.00900 1.02412
  i> 12 50 4.025073
  parameters -1.00568 1.01621
  **** i> 13 56 4.019328
  parameters -1.00236 1.01467
  i> 14 58 4.004703
  parameters -0.99804 1.00728
  **** i> 15 64 3.998920
  parameters -0.99880 1.00370
  i> 16 66 3.984360
  parameters -0.99552 0.99582
  **** i> 17 72 3.978528
  parameters -0.99217 0.99429
  i> 18 74 3.963986
  parameters -0.98779 0.98699
  **** i> 19 80 3.958118
  parameters -0.98855 0.98339
  i> 20 82 3.943639
  parameters -0.98530 0.97553
  **** i> 21 88 3.937719
  parameters -0.98192 0.97402
  i> 22 90 3.923256
  parameters -0.97749 0.96680
  **** i> 23 96 3.917299
  parameters -0.97824 0.96317
  i> 24 98 3.902898
  parameters -0.97502 0.95534
  **** i> 25 104 3.896888
  parameters -0.97161 0.95384
  i> 26 106 3.882502
  parameters -0.96712 0.94670
  **** i> 27 112 3.876454
  parameters -0.96787 0.94306
  i> 28 114 3.862128
  parameters -0.96469 0.93524
  **** i> 29 120 3.856025
  parameters -0.96125 0.93376
  i> 30 122 3.841712
  parameters -0.95669 0.92669
  **** i> 31 128 3.835572
  parameters -0.95743 0.92303
  i> 32 130 3.821316
  parameters -0.95429 0.91522
  **** i> 33 136 3.815119
  parameters -0.95082 0.91376
  i> 34 138 3.800875
  parameters -0.94618 0.90677
  **** i> 35 144 3.794641
  parameters -0.94692 0.90309
  i> 36 146 3.780452
  parameters -0.94382 0.89530
  **** i> 37 152 3.774158
  parameters -0.94032 0.89385
  i> 38 154 3.759979
  parameters -0.93561 0.88694
  **** i> 39 160 3.753649
  parameters -0.93635 0.88323
  i> 40 162 3.739522
  parameters -0.93327 0.87545
  **** i> 41 168 3.733129
  parameters -0.92975 0.87402
  i> 42 170 3.719010
  parameters -0.92496 0.86719
  **** i> 43 176 3.712582
  parameters -0.92569 0.86346
  i> 44 178 3.698513
  parameters -0.92265 0.85568
  **** i> 45 184 3.692020
  parameters -0.91909 0.85427
  i> 46 186 3.677956
  parameters -0.91422 0.84751
  **** i> 47 192 3.671429
  parameters -0.91495 0.84377
  i> 48 194 3.657411
  parameters -0.91194 0.83598
  **** i> 49 200 3.650816
  parameters -0.90836 0.83459
  i> 50 202 3.636803
  parameters -0.90340 0.82791
  **** i> 51 208 3.630174
  parameters -0.90412 0.82414
  i> 52 210 3.616203
  parameters -0.90115 0.81636
  **** i> 53 216 3.609503
  parameters -0.89754 0.81498
  i> 54 218 3.595534
  parameters -0.89249 0.80838
  **** i> 55 224 3.588802
  parameters -0.89320 0.80459
  i> 56 226 3.574871
  parameters -0.89026 0.79679
  **** i> 57 232 3.568067
  parameters -0.88662 0.79544
  i> 58 234 3.554135
  parameters -0.88148 0.78891
  **** i> 59 240 3.547298
  parameters -0.88217 0.78510
  i> 60 242 3.533401
  parameters -0.87927 0.77729
  **** i> 61 248 3.526489
  parameters -0.87561 0.77595
  i> 62 250 3.512588
  parameters -0.87036 0.76950
  **** i> 63 256 3.505645
  parameters -0.87105 0.76567
  i> 64 258 3.491774
  parameters -0.86818 0.75784
  **** i> 65 264 3.484754
  parameters -0.86448 0.75653
  i> 66 266 3.470875
  parameters -0.85914 0.75015
  **** i> 67 272 3.463826
  parameters -0.85981 0.74630
  i> 68 274 3.449973
  parameters -0.85697 0.73845
  **** i> 69 280 3.442843
  parameters -0.85325 0.73715
  i> 70 282 3.428978
  parameters -0.84779 0.73085
  **** i> 71 288 3.421820
  parameters -0.84844 0.72698
  i> 72 290 3.407976
  parameters -0.84564 0.71910
  **** i> 73 296 3.400736
  parameters -0.84189 0.71782
  i> 74 298 3.386876
  parameters -0.83632 0.71160
  **** i> 75 304 3.379609
  parameters -0.83696 0.70771
  i> 76 306 3.365764
  parameters -0.83418 0.69979
  **** i> 77 312 3.358412
  parameters -0.83041 0.69853
  i> 78 314 3.344546
  parameters -0.82472 0.69239
  **** i> 79 320 3.337170
  parameters -0.82533 0.68848
  i> 80 322 3.323313
  parameters -0.82258 0.68052
  **** i> 81 328 3.315850
  parameters -0.81879 0.67928
  i> 82 330 3.301967
  parameters -0.81297 0.67321
  **** i> 83 336 3.294481
  parameters -0.81356 0.66928
  i> 84 338 3.280600
  parameters -0.81084 0.66128
  **** i> 85 344 3.273027
  parameters -0.80703 0.66006
  i> 86 346 3.259113
  parameters -0.80108 0.65407
  **** i> 87 352 3.251518
  parameters -0.80164 0.65012
  i> 88 354 3.237599
  parameters -0.79894 0.64206
  **** i> 89 360 3.229915
  parameters -0.79510 0.64086
  i> 90 362 3.215957
  parameters -0.78902 0.63495
  **** i> 91 368 3.208254
  parameters -0.78955 0.63098
  i> 92 370 3.194282
  parameters -0.78687 0.62287
  **** i> 93 376 3.186489
  parameters -0.78302 0.62168
  i> 94 378 3.172470
  parameters -0.77678 0.61585
  **** i> 95 384 3.164660
  parameters -0.77729 0.61186
  i> 96 386 3.150619
  parameters -0.77463 0.60368
  **** i> 97 392 3.142719
  parameters -0.77075 0.60252
  i> 98 394 3.128622
  parameters -0.76437 0.59676
  **** i> 99 400 3.120708
  parameters -0.76484 0.59276
  i> 100 402 3.106579
  parameters -0.76218 0.58451
  Post processing for method CG
  Compute gradient approximation at finish of CG
  Compute Hessian approximation at finish of CG
  Save results from method CG
  Assemble the answers
  Sort results
  > print(ans4)
  par fvalues method fns grs itns conv KKT1 KKT2 xtimes
  1 -0.7648373, 0.5927588 3.106579 CG 402 101 NULL 1 FALSE FALSE 0
  > cat("\n\n")
  
  
  > ans5<-optimx(c(-1.2,1), fr, grr, method = "CG", control=list(type=2))
  > print(ans5)
  par fvalues method fns grs itns conv KKT1 KKT2 xtimes
  1 0.9944093, 0.9888229 3.123777e-05 CG 385 101 NULL 1 FALSE TRUE 0
  > cat("\n\n")
  
  
  > ans6<-optimx(c(-1.2,1), fr, grr, method = "L-BFGS-B")
  > print(ans6)
  par fvalues method fns grs itns conv KKT1 KKT2 xtimes
  1 0.9999997, 0.9999995 2.267577e-13 L-BFGS-B 47 47 NULL 0 TRUE TRUE 0
  > cat("\n\n")
  
  
  >
  > flb <- function(x)
  + { p <- length(x); sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) }
  > ## 25-dimensional box constrained
  > optimx(rep(3, 25), flb, NULL, method = "L-BFGS-B",
  + lower=rep(2, 25), upper=rep(4, 25)) # par[24] is *not* at boundary
  par
  1 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.000000, 2.109093, 4.000000
  fvalues method fns grs itns conv KKT1 KKT2 xtimes
  1 368.1059 L-BFGS-B 6 6 NULL 0 FALSE TRUE 0
  >
  > ## "wild" function , global minimum at about -15.81515
  > fw <- function (x)
  + 10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80
  > plot(fw, -50, 50, n=1000, main = "optim() minimising 'wild function'")
  >
  > ## Suppressed for optimx() ans7 <- optimx(50, fw, method="SANN",
  > ## control=list(maxit=20000, temp=20, parscale=20))
  > ## ans7
  > ## Now improve locally {typically only by a small bit}:
  > ## newpar<-unlist(ans7$par) # NOTE: you need to unlist the parameters as optimx() has multiple outputs
  > ##(r2 <- optimx(newpar, fw, method="BFGS"))
  > ##points(r2$par, r2$value, pch = 8, col = "red", cex = 2)
  >
  > ## Show multiple outputs of optimx using all.methods
  > # genrose function code
  > genrose.f<- function(x, gs=NULL){ # objective function
  + ## One generalization of the Rosenbrock banana valley function (n parameters)
  + n <- length(x)
  + if(is.null(gs)) { gs=100.0 }
  + fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2)
  + return(fval)
  + }
  >
  > genrose.g <- function(x, gs=NULL){
  + # vectorized gradient for genrose.f
  + # Ravi Varadhan 2009-04-03
  + n <- length(x)
  + if(is.null(gs)) { gs=100.0 }
  + gg <- as.vector(rep(0, n))
  + tn <- 2:n
  + tn1 <- tn - 1
  + z1 <- x[tn] - x[tn1]^2
  + z2 <- 1 - x[tn]
  + gg[tn] <- 2 * (gs * z1 - z2)
  + gg[tn1] <- gg[tn1] - 4 * gs * x[tn1] * z1
  + return(gg)
  + }
  >
  > genrose.h <- function(x, gs=NULL) { ## compute Hessian
  + if(is.null(gs)) { gs=100.0 }
  + n <- length(x)
  + hh<-matrix(rep(0, n*n),n,n)
  + for (i in 2:n) {
  + z1<-x[i]-x[i-1]*x[i-1]
  + z2<-1.0-x[i]
  + hh[i,i]<-hh[i,i]+2.0*(gs+1.0)
  + hh[i-1,i-1]<-hh[i-1,i-1]-4.0*gs*z1-4.0*gs*x[i-1]*(-2.0*x[i-1])
  + hh[i,i-1]<-hh[i,i-1]-4.0*gs*x[i-1]
  + hh[i-1,i]<-hh[i-1,i]-4.0*gs*x[i-1]
  + }
  + return(hh)
  + }
  >
  > startx<-4*seq(1:10)/3.
  > ans8<-optimx(startx,fn=genrose.f,gr=genrose.g, hess=genrose.h, control=list(all.methods=TRUE, save.failures=TRUE), gs=10)
  all.methods is TRUE -- Using all available methods
  [1] "BFGS" "CG" "Nelder-Mead" "L-BFGS-B" "nlm"
  [6] "nlminb" "spg" "ucminf" "Rcgmin" "Rvmmin"
  [11] "bobyqa" "uobyqa" "newuoa"
  Try function at initial point: [1] 1.333333 2.666667 4.000000 5.333333 6.666667 8.000000 9.333333
  [8] 10.666667 12.000000 13.333333
  f= 382462.7
  > print(ans8)
  par
  3 0.1485254, 0.7219329, 1.1931460, 1.2200314, -1.4280132, 0.7719437, 1.9202220, 2.1584949, 6.0673775, 35.1981635
  5 -0.9723634, 0.9885277, 0.9857010, 0.9787716, 0.9815156, 0.9937781, 0.9994302, 0.9780824, 0.9047627, 0.7204988
  11 0.9999989, 0.9999985, 0.9999983, 0.9999978, 0.9999978, 0.9999975, 0.9999978, 0.9999956, 0.9999925, 0.9999858
  4 -0.9999983, 0.9999979, 0.9999983, 0.9999992, 0.9999992, 0.9999993, 0.9999993, 0.9999983, 0.9999952, 0.9999891
  1 -1.0000000, 0.9999999, 0.9999997, 1.0000002, 1.0000004, 1.0000001, 1.0000002, 0.9999997, 0.9999996, 0.9999993
  13 -1.0000000, 1.0000004, 1.0000002, 1.0000002, 1.0000001, 1.0000000, 0.9999999, 1.0000000, 0.9999996, 0.9999991
  2 0.9999998, 0.9999998, 0.9999997, 0.9999996, 0.9999997, 0.9999996, 0.9999996, 0.9999996, 0.9999995, 0.9999990
  6 0.9999999, 1.0000000, 1.0000000, 1.0000001, 1.0000001, 1.0000001, 0.9999999, 0.9999998, 0.9999997, 0.9999994
  7 1.000000, 1.000000, 1.000000, 1.000000, 1.000000, 1.000000, 1.000000, 1.000000, 1.000000, 1.000000
  12 -1, 1, 1, 1, 1, 1, 1, 1, 1, 1
  8 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
  9 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
  10 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
  fvalues method fns grs itns conv KKT1 KKT2 xtimes
  3 1402.26 Nelder-Mead 501 NA NULL 1 FALSE FALSE 0.01
  5 1.252242 nlm NA NA 100 1 FALSE TRUE 0.03
  11 1 bobyqa 880 NA NULL 0 TRUE TRUE 0.06
  4 1 L-BFGS-B 68 68 NULL 0 TRUE TRUE 0.02
  1 1 BFGS 165 60 NULL 0 TRUE TRUE 0
  13 1 newuoa 1478 NA NULL 0 TRUE TRUE 0.11
  2 1 CG 262 101 NULL 1 TRUE TRUE 0.01
  6 1 nlminb 62 53 52 0 TRUE TRUE 0
  7 1 spg 227 NA 208 0 TRUE TRUE 0.06
  12 1 uobyqa 738 NA NULL 0 TRUE TRUE 0.11
  8 1 ucminf 107 107 NULL 0 TRUE TRUE 0
  9 1 Rcgmin 145 71 NULL 0 TRUE TRUE 0.01
  10 1 Rvmmin 147 85 NULL 0 TRUE TRUE 0.04
  >
  > get.result(ans8, attribute="grs")
  method grs
  12 uobyqa NA
  7 spg NA
  13 newuoa NA
  11 bobyqa NA
  5 nlm NA
  3 Nelder-Mead NA
  8 ucminf 107
  2 CG 101
  10 Rvmmin 85
  9 Rcgmin 71
  4 L-BFGS-B 68
  1 BFGS 60
  6 nlminb 53
  > get.result(ans8, method="spg")
  $par
  [1] 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
  [9] 1.000000 1.000000
  
  $fvalues
  [1] 1
  
  $method
  [1] "spg"
  
  $fns
  [1] 227
  
  $grs
  [1] NA
  
  $itns
  [1] 208
  
  $conv
  [1] 0
  
  $KKT1
  [1] TRUE
  
  $KKT2
  [1] TRUE
  
  $xtimes
  user.self
  0.06
  
  >
  >
  > startx<-4*seq(1:10)/3.
  > cat("Polyalgorithm with 200 steps NM followed by up to 75 of ucminf\n")
  Polyalgorithm with 200 steps NM followed by up to 75 of ucminf
  > ans9<-optimx(startx,fn=genrose.f,gr=genrose.g, hess=genrose.h, method=c("Nelder-Mead","ucminf"),
  + itnmax=c(200,75), control=list(follow.on=TRUE, save.failures=TRUE,trace=TRUE), gs=10)
  fn is genrose.f
  Function has 10 arguments
  Analytic gradient from function genrose.g
  
  Analytic hessian from function genrose.h
  
  Looking for method = Nelder-Mead
  Looking for method = ucminf
  Scale check -- log parameter ratio= 1 log bounds ratio= NA
  Do 200 steps of Nelder-Mead
  Method: Nelder-Mead
  Nelder-Mead direct search function minimizer
  function value for initial parameters = 382462.740741
  Scaled convergence tolerance is 0.00569914
  Stepsize computed as 1.333333
  BUILD 11 479479.530864 379030.740741
  EXTENSION 13 451122.395062 315411.830716
  LO-REDUCTION 15 428928.222222 315411.830716
  LO-REDUCTION 17 412138.493827 315411.830716
  LO-REDUCTION 19 399994.691358 315411.830716
  LO-REDUCTION 21 391738.296296 315411.830716
  EXTENSION 23 386610.790123 280586.182142
  LO-REDUCTION 25 383853.654321 280586.182142
  EXTENSION 27 382652.370370 243537.617869
  LO-REDUCTION 29 382462.740741 243537.617869
  EXTENSION 31 379030.740741 202542.563592
  LO-REDUCTION 33 330891.294958 202542.563592
  LO-REDUCTION 35 327988.072772 202542.563592
  LO-REDUCTION 37 323526.675374 202542.563592
  LO-REDUCTION 39 317293.878632 202542.563592
  LO-REDUCTION 41 315411.830716 202542.563592
  EXTENSION 43 291879.728673 125490.339258
  LO-REDUCTION 45 280586.182142 125490.339258
  LO-REDUCTION 47 256381.017344 125490.339258
  LO-REDUCTION 49 244598.728203 125490.339258
  LO-REDUCTION 51 243537.617869 125490.339258
  LO-REDUCTION 53 233950.044601 125490.339258
  LO-REDUCTION 55 225773.553345 125490.339258
  LO-REDUCTION 57 220120.208605 125490.339258
  LO-REDUCTION 59 217019.591800 125490.339258
  LO-REDUCTION 61 202542.563592 125490.339258
  LO-REDUCTION 63 186379.780089 125490.339258
  EXTENSION 65 178194.844261 118071.799127
  LO-REDUCTION 67 177222.185722 118071.799127
  EXTENSION 69 171997.619095 102622.184082
  LO-REDUCTION 71 166991.034075 102622.184082
  LO-REDUCTION 73 156833.520191 102622.184082
  REFLECTION 75 144807.441972 99034.978668
  LO-REDUCTION 77 139398.762798 99034.978668
  EXTENSION 79 136690.892560 75982.102751
  LO-REDUCTION 81 129924.600377 75982.102751
  EXTENSION 83 128945.770039 62119.591382
  LO-REDUCTION 85 125490.339258 62119.591382
  HI-REDUCTION 87 118071.799127 62119.591382
  LO-REDUCTION 89 107665.616287 62119.591382
  LO-REDUCTION 91 103937.163503 62119.591382
  LO-REDUCTION 93 102622.184082 62119.591382
  LO-REDUCTION 95 101255.539477 62119.591382
  LO-REDUCTION 97 99034.978668 62119.591382
  LO-REDUCTION 99 91503.348497 62119.591382
  LO-REDUCTION 101 83609.636793 62119.591382
  LO-REDUCTION 103 79075.722774 62119.591382
  EXTENSION 105 75982.102751 53129.632893
  EXTENSION 107 75336.726642 46258.611289
  LO-REDUCTION 109 71078.705732 46258.611289
  LO-REDUCTION 111 69019.749391 46258.611289
  LO-REDUCTION 113 68807.758429 46258.611289
  LO-REDUCTION 115 67325.769070 46258.611289
  EXTENSION 117 65674.060299 40784.649171
  LO-REDUCTION 119 64966.248487 40784.649171
  LO-REDUCTION 121 63458.887121 40784.649171
  LO-REDUCTION 123 62119.591382 40784.649171
  LO-REDUCTION 125 58906.190299 40784.649171
  LO-REDUCTION 127 57045.462104 40784.649171
  REFLECTION 129 55778.274942 39861.217896
  REFLECTION 131 53129.632893 38016.965432
  REFLECTION 133 52311.199663 37420.173917
  EXTENSION 135 46258.611289 35036.861827
  EXTENSION 137 46105.209891 30982.255711
  LO-REDUCTION 139 45333.504251 30982.255711
  EXTENSION 141 44026.328226 30017.838292
  EXTENSION 143 43439.914960 23093.315529
  LO-REDUCTION 145 42808.411900 23093.315529
  LO-REDUCTION 147 40784.649171 23093.315529
  LO-REDUCTION 149 39861.217896 23093.315529
  LO-REDUCTION 151 38016.965432 23093.315529
  LO-REDUCTION 153 37420.173917 23093.315529
  LO-REDUCTION 155 35036.861827 23093.315529
  LO-REDUCTION 157 33121.491277 23093.315529
  LO-REDUCTION 159 30982.255711 23093.315529
  LO-REDUCTION 161 30366.862179 23093.315529
  LO-REDUCTION 163 30022.627935 23093.315529
  EXTENSION 165 30017.838292 21124.359868
  LO-REDUCTION 167 28982.543527 21124.359868
  LO-REDUCTION 169 27790.980241 21124.359868
  EXTENSION 171 26739.739991 20636.233168
  EXTENSION 173 25586.819489 18820.882765
  LO-REDUCTION 175 25568.333586 18820.882765
  LO-REDUCTION 177 24651.691042 18820.882765
  LO-REDUCTION 179 24597.631192 18820.882765
  LO-REDUCTION 181 23878.360072 18820.882765
  EXTENSION 183 23600.411306 15928.093725
  LO-REDUCTION 185 23278.054182 15928.093725
  LO-REDUCTION 187 23093.315529 15928.093725
  LO-REDUCTION 189 21278.200811 15928.093725
  LO-REDUCTION 191 21124.359868 15928.093725
  LO-REDUCTION 193 21049.116703 15928.093725
  LO-REDUCTION 195 20851.803526 15928.093725
  EXTENSION 197 20636.233168 12576.034507
  LO-REDUCTION 199 19930.998024 12576.034507
  Exiting from Nelder Mead minimizer
  201 function evaluations used
  Post processing for method Nelder-Mead
  Compute gradient approximation at finish of Nelder-Mead
  Compute Hessian approximation at finish of Nelder-Mead
  Save results from method Nelder-Mead
  Assemble the answers
  FOLLOW ON!
  Do 75 steps of ucminf
  Method: ucminf
  neval = 1 F(x) = 1.258D+04 max|g(x)| = 4.346D+03
  x( 1.. 5) = 1.911D+00 3.019D+00 5.120D+00 4.643D+00 -9.015D-02
  x( 6.. 10) = 1.108D+00 1.450D+00 -4.135D+00 6.368D+00 3.614D+01
  Line search: alpha = 1.000D+00 dphi(0) = -6.071D+03 dphi(alpha) = -3.871D+03
  neval = 2 F(x) = 7.658D+03 max|g(x)| = 2.625D+03
  x( 1.. 5) = 1.903D+00 2.941D+00 4.404D+00 4.050D+00 -1.914D-02
  x( 6.. 10) = 1.106D+00 1.390D+00 -3.821D+00 6.216D+00 3.614D+01
  Line search: alpha = 1.000D+00 dphi(0) = -9.642D+02 dphi(alpha) = 5.446D+02
  neval = 3 F(x) = 7.468D+03 max|g(x)| = 3.432D+03
  x( 1.. 5) = 1.843D+00 2.401D+00 4.701D+00 3.766D+00 1.461D-01
  x( 6.. 10) = 1.086D+00 1.128D+00 -3.244D+00 6.533D+00 3.605D+01
  Line search: alpha = 1.000D+00 dphi(0) = -5.162D+02 dphi(alpha) = 3.922D+02
  neval = 4 F(x) = 7.430D+03 max|g(x)| = 3.645D+03
  x( 1.. 5) = 1.708D+00 1.741D+00 4.776D+00 3.959D+00 4.090D-01
  x( 6.. 10) = 1.039D+00 7.462D-01 -2.941D+00 6.105D+00 3.595D+01
  Line search: alpha = 1.000D+00 dphi(0) = -3.673D+02 dphi(alpha) = 1.218D+02
  neval = 5 F(x) = 7.312D+03 max|g(x)| = 2.958D+03
  x( 1.. 5) = 1.498D+00 1.121D+00 4.505D+00 4.282D+00 7.966D-01
  x( 6.. 10) = 9.612D-01 3.273D-01 -3.018D+00 6.227D+00 3.575D+01
  Line search: alpha = 1.000D+00 dphi(0) = -3.050D+02 dphi(alpha) = -6.570D+01
  neval = 6 F(x) = 7.122D+03 max|g(x)| = 2.931D+03
  x( 1.. 5) = 1.240D+00 5.914D-01 4.478D+00 4.187D+00 1.314D+00
  x( 6.. 10) = 8.603D-01 -4.929D-02 -3.423D+00 6.218D+00 3.551D+01
  Line search: alpha = 1.000D+00 dphi(0) = -1.826D+02 dphi(alpha) = 8.012D+00
  neval = 7 F(x) = 7.026D+03 max|g(x)| = 2.902D+03
  x( 1.. 5) = 8.068D-01 5.599D-01 4.470D+00 4.252D+00 1.984D+00
  x( 6.. 10) = 7.533D-01 2.463D-01 -3.341D+00 6.171D+00 3.501D+01
  Line search: alpha = 1.000D+00 dphi(0) = -1.666D+02 dphi(alpha) = -1.492D+02
  neval = 8 F(x) = 6.869D+03 max|g(x)| = 2.847D+03
  x( 1.. 5) = 3.513D-01 3.768D-01 4.441D+00 4.222D+00 1.910D+00
  x( 6.. 10) = 9.335D-01 4.947D-01 -3.365D+00 6.108D+00 3.420D+01
  Line search: alpha = 1.000D+00 dphi(0) = -5.022D+02 dphi(alpha) = -3.623D+02
  neval = 9 F(x) = 6.434D+03 max|g(x)| = 2.832D+03
  x( 1.. 5) = 8.962D-02 8.466D-01 4.437D+00 4.186D+00 2.003D+00
  x( 6.. 10) = 1.383D+00 -6.341D-02 -3.177D+00 5.878D+00 3.135D+01
  Line search: alpha = 1.000D+00 dphi(0) = -1.510D+03 dphi(alpha) = -8.172D+02
  neval = 10 F(x) = 5.295D+03 max|g(x)| = 2.596D+03
  x( 1.. 5) = 5.739D-01 1.394D+00 4.323D+00 3.990D+00 1.533D+00
  x( 6.. 10) = -4.858D-01 -7.946D-01 -2.835D+00 5.182D+00 2.266D+01
  Line search: alpha = 1.965D-01 dphi(0) = -1.479D+03 dphi(alpha) = -1.040D+03
  neval = 12 F(x) = 5.040D+03 max|g(x)| = 2.484D+03
  x( 1.. 5) = 1.294D+00 1.163D+00 4.261D+00 3.963D+00 1.691D+00
  x( 6.. 10) = -3.895D-01 -5.378D-01 -2.872D+00 5.059D+00 2.110D+01
  Line search: alpha = 1.000D+00 dphi(0) = -4.065D+03 dphi(alpha) = -1.760D+03
  neval = 13 F(x) = 2.195D+03 max|g(x)| = 1.170D+03
  x( 1.. 5) = -1.020D+00 -8.913D-03 3.364D+00 3.160D+00 1.603D+00
  x( 6.. 10) = -3.089D-02 2.204D-01 -2.640D+00 4.883D+00 2.208D+01
  Line search: alpha = 1.000D+00 dphi(0) = -5.877D+02 dphi(alpha) = -4.609D+02
  neval = 14 F(x) = 1.673D+03 max|g(x)| = 9.564D+02
  x( 1.. 5) = -6.316D-01 1.345D-01 3.163D+00 2.974D+00 1.579D+00
  x( 6.. 10) = -1.019D-01 1.051D-01 -2.469D+00 4.576D+00 1.899D+01
  Line search: alpha = 5.210D-01 dphi(0) = -1.320D+03 dphi(alpha) = -2.289D+02
  neval = 16 F(x) = 1.220D+03 max|g(x)| = 6.477D+02
  x( 1.. 5) = -1.803D+00 -5.469D-01 2.819D+00 2.681D+00 1.438D+00
  x( 6.. 10) = 1.088D-01 3.878D-01 -2.390D+00 4.106D+00 1.432D+01
  Line search: alpha = 1.000D+00 dphi(0) = -7.420D+02 dphi(alpha) = -3.864D+02
  neval = 17 F(x) = 6.722D+02 max|g(x)| = 4.223D+02
  x( 1.. 5) = -1.079D+00 -2.929D-01 2.473D+00 2.360D+00 1.344D+00
  x( 6.. 10) = 1.224D-01 2.261D-01 -2.118D+00 3.701D+00 1.118D+01
  Line search: alpha = 1.000D+00 dphi(0) = -7.351D+02 dphi(alpha) = -2.265D+02
  neval = 18 F(x) = 2.217D+02 max|g(x)| = 1.200D+02
  x( 1.. 5) = -9.006D-01 -5.413D-01 1.729D+00 1.691D+00 1.123D+00
  x( 6.. 10) = 4.484D-01 1.369D-01 -1.748D+00 3.212D+00 9.734D+00
  Line search: alpha = 1.000D+00 dphi(0) = -1.085D+02 dphi(alpha) = -3.967D+01
  neval = 19 F(x) = 1.467D+02 max|g(x)| = 1.849D+02
  x( 1.. 5) = -7.727D-01 -5.975D-01 1.505D+00 1.491D+00 1.075D+00
  x( 6.. 10) = 5.968D-01 1.397D-01 -1.612D+00 2.834D+00 6.472D+00
  Line search: alpha = 1.000D+00 dphi(0) = -1.091D+02 dphi(alpha) = -3.746D+01
  neval = 20 F(x) = 7.697D+01 max|g(x)| = 1.054D+02
  x( 1.. 5) = -4.623D-01 -7.257D-01 1.083D+00 1.113D+00 9.558D-01
  x( 6.. 10) = 7.992D-01 1.154D-01 -1.394D+00 2.470D+00 5.171D+00
  Line search: alpha = 1.000D+00 dphi(0) = -2.735D+01 dphi(alpha) = -1.456D+01
  neval = 21 F(x) = 5.654D+01 max|g(x)| = 7.825D+01
  x( 1.. 5) = -2.429D-01 -7.428D-01 8.532D-01 8.967D-01 8.420D-01
  x( 6.. 10) = 6.796D-01 9.171D-02 -1.250D+00 2.231D+00 4.276D+00
  Line search: alpha = 1.000D+00 dphi(0) = -1.924D+01 dphi(alpha) = -9.651D+00
  neval = 22 F(x) = 4.266D+01 max|g(x)| = 5.903D+01
  x( 1.. 5) = 1.672D-02 -6.382D-01 6.062D-01 6.558D-01 7.077D-01
  x( 6.. 10) = 5.045D-01 3.075D-02 -1.064D+00 1.942D+00 3.243D+00
  Line search: alpha = 1.000D+00 dphi(0) = -1.127D+01 dphi(alpha) = -6.628D+00
  neval = 23 F(x) = 3.402D+01 max|g(x)| = 4.364D+01
  x( 1.. 5) = 2.629D-01 -4.693D-01 3.663D-01 4.160D-01 5.618D-01
  x( 6.. 10) = 3.269D-01 -3.522D-02 -8.694D-01 1.653D+00 2.365D+00
  Line search: alpha = 1.000D+00 dphi(0) = -1.088D+01 dphi(alpha) = -4.294D+00
  neval = 24 F(x) = 2.606D+01 max|g(x)| = 3.531D+01
  x( 1.. 5) = 5.859D-01 -1.708D-01 2.159D-02 6.702D-02 3.396D-01
  x( 6.. 10) = 8.750D-02 -1.205D-01 -5.756D-01 1.234D+00 1.183D+00
  Line search: alpha = 1.000D+00 dphi(0) = -9.208D+00 dphi(alpha) = 6.525D+00
  neval = 25 F(x) = 2.347D+01 max|g(x)| = 1.767D+01
  x( 1.. 5) = 8.706D-01 2.507D-01 -3.117D-01 -2.790D-01 1.181D-01
  x( 6.. 10) = -1.365D-01 -2.032D-01 -2.580D-01 8.624D-01 7.224D-01
  Line search: alpha = 1.000D+00 dphi(0) = -5.931D+00 dphi(alpha) = 3.149D-01
  neval = 26 F(x) = 2.098D+01 max|g(x)| = 1.266D+01
  x( 1.. 5) = 6.840D-01 1.748D-01 -1.358D-01 -1.058D-01 2.365D-01
  x( 6.. 10) = 9.976D-03 -1.433D-01 -3.816D-01 1.048D+00 1.229D+00
  Line search: alpha = 1.000D+00 dphi(0) = -3.742D+00 dphi(alpha) = 4.699D-01
  neval = 27 F(x) = 1.934D+01 max|g(x)| = 2.704D+01
  x( 1.. 5) = 6.235D-01 2.684D-01 -1.230D-01 -9.850D-02 2.609D-01
  x( 6.. 10) = 8.764D-02 -9.706D-02 -3.490D-01 9.939D-01 7.462D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.725D+00 dphi(alpha) = -4.601D-01
  neval = 28 F(x) = 1.824D+01 max|g(x)| = 2.073D+01
  x( 1.. 5) = 6.283D-01 3.933D-01 -1.583D-01 -1.420D-01 2.358D-01
  x( 6.. 10) = 9.653D-02 -6.386D-02 -2.845D-01 9.323D-01 7.662D-01
  Line search: alpha = 1.000D+00 dphi(0) = -8.526D-01 dphi(alpha) = -5.343D-01
  neval = 29 F(x) = 1.754D+01 max|g(x)| = 1.735D+01
  x( 1.. 5) = 6.130D-01 4.035D-01 -1.594D-01 -1.487D-01 2.224D-01
  x( 6.. 10) = 1.203D-01 3.019D-03 -2.684D-01 9.067D-01 7.989D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.674D+00 dphi(alpha) = -5.605D-01
  neval = 30 F(x) = 1.643D+01 max|g(x)| = 1.397D+01
  x( 1.. 5) = 5.732D-01 3.766D-01 -1.538D-01 -1.564D-01 1.877D-01
  x( 6.. 10) = 1.722D-01 1.714D-01 -2.455D-01 8.384D-01 7.406D-01
  Line search: alpha = 1.000D+00 dphi(0) = -6.435D-01 dphi(alpha) = -2.678D-01
  neval = 31 F(x) = 1.598D+01 max|g(x)| = 1.370D+01
  x( 1.. 5) = 5.562D-01 3.529D-01 -1.527D-01 -1.644D-01 1.588D-01
  x( 6.. 10) = 1.889D-01 2.580D-01 -2.311D-01 7.805D-01 6.220D-01
  Line search: alpha = 1.000D+00 dphi(0) = -5.976D-01 dphi(alpha) = -3.633D-01
  neval = 32 F(x) = 1.549D+01 max|g(x)| = 1.339D+01
  x( 1.. 5) = 5.471D-01 3.241D-01 -1.464D-01 -1.677D-01 1.275D-01
  x( 6.. 10) = 1.847D-01 3.025D-01 -2.244D-01 7.233D-01 5.062D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.455D+00 dphi(alpha) = -7.334D-01
  neval = 33 F(x) = 1.439D+01 max|g(x)| = 1.203D+01
  x( 1.. 5) = 5.327D-01 2.697D-01 -1.178D-01 -1.615D-01 6.073D-02
  x( 6.. 10) = 1.479D-01 3.281D-01 -2.219D-01 6.054D-01 2.966D-01
  Line search: alpha = 1.000D+00 dphi(0) = -2.080D+00 dphi(alpha) = -9.378D-01
  neval = 34 F(x) = 1.288D+01 max|g(x)| = 8.869D+00
  x( 1.. 5) = 5.075D-01 2.076D-01 -4.745D-02 -1.244D-01 -2.050D-02
  x( 6.. 10) = 7.458D-02 2.832D-01 -2.393D-01 4.649D-01 1.200D-01
  Line search: alpha = 1.000D+00 dphi(0) = -2.000D+00 dphi(alpha) = -7.398D-01
  neval = 35 F(x) = 1.151D+01 max|g(x)| = 5.441D+00
  x( 1.. 5) = 4.699D-01 1.845D-01 5.163D-02 -5.751D-02 -7.027D-02
  x( 6.. 10) = 9.187D-03 1.767D-01 -2.720D-01 3.651D-01 1.001D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.069D+00 dphi(alpha) = -4.765D-01
  neval = 36 F(x) = 1.075D+01 max|g(x)| = 5.654D+00
  x( 1.. 5) = 4.420D-01 2.486D-01 1.135D-01 -1.443D-02 -7.151D-02
  x( 6.. 10) = 9.771D-03 9.769D-02 -2.722D-01 3.060D-01 1.166D-01
  Line search: alpha = 1.000D+00 dphi(0) = -7.086D-01 dphi(alpha) = -2.044D-01
  neval = 37 F(x) = 1.029D+01 max|g(x)| = 5.613D+00
  x( 1.. 5) = 4.305D-01 3.541D-01 1.485D-01 8.155D-03 -5.169D-02
  x( 6.. 10) = 5.340D-02 5.255D-02 -2.458D-01 2.486D-01 1.230D-01
  Line search: alpha = 1.000D+00 dphi(0) = -2.371D-01 dphi(alpha) = -1.299D-01
  neval = 38 F(x) = 1.011D+01 max|g(x)| = 5.568D+00
  x( 1.. 5) = 4.402D-01 3.982D-01 1.610D-01 1.629D-02 -3.566D-02
  x( 6.. 10) = 8.930D-02 4.683D-02 -2.268D-01 2.130D-01 1.343D-01
  Line search: alpha = 1.000D+00 dphi(0) = -4.259D-01 dphi(alpha) = -2.261D-01
  neval = 39 F(x) = 9.781D+00 max|g(x)| = 5.464D+00
  x( 1.. 5) = 4.842D-01 4.488D-01 1.755D-01 2.402D-02 -1.164D-02
  x( 6.. 10) = 1.474D-01 5.079D-02 -1.875D-01 1.303D-01 1.224D-01
  Line search: alpha = 1.000D+00 dphi(0) = -6.542D-01 dphi(alpha) = -2.225D-01
  neval = 40 F(x) = 9.336D+00 max|g(x)| = 4.986D+00
  x( 1.. 5) = 5.892D-01 4.801D-01 1.872D-01 3.280D-02 3.359D-02
  x( 6.. 10) = 2.223D-01 6.821D-02 -1.321D-01 2.014D-02 1.400D-01
  Line search: alpha = 1.000D+00 dphi(0) = -3.527D-01 dphi(alpha) = -5.292D-02
  neval = 41 F(x) = 9.123D+00 max|g(x)| = 4.374D+00
  x( 1.. 5) = 6.761D-01 4.907D-01 1.858D-01 3.235D-02 5.894D-02
  x( 6.. 10) = 2.369D-01 8.372D-02 -9.209D-02 -4.791D-02 1.113D-01
  Line search: alpha = 1.000D+00 dphi(0) = -2.520D-01 dphi(alpha) = -7.151D-02
  neval = 42 F(x) = 8.961D+00 max|g(x)| = 4.328D+00
  x( 1.. 5) = 7.092D-01 4.725D-01 1.900D-01 4.437D-02 8.498D-02
  x( 6.. 10) = 2.156D-01 9.557D-02 -9.281D-02 -1.916D-02 1.527D-01
  Line search: alpha = 1.000D+00 dphi(0) = -4.306D-01 dphi(alpha) = -2.112D-01
  neval = 43 F(x) = 8.641D+00 max|g(x)| = 4.079D+00
  x( 1.. 5) = 7.369D-01 4.850D-01 2.128D-01 7.237D-02 1.150D-01
  x( 6.. 10) = 1.507D-01 1.063D-01 -8.398D-02 5.268D-03 1.053D-01
  Line search: alpha = 1.000D+00 dphi(0) = -5.383D-01 dphi(alpha) = -2.252D-01
  neval = 44 F(x) = 8.260D+00 max|g(x)| = 3.686D+00
  x( 1.. 5) = 7.570D-01 5.435D-01 2.803D-01 1.438D-01 1.818D-01
  x( 6.. 10) = 7.735D-02 1.077D-01 -7.189D-02 4.960D-02 8.210D-02
  Line search: alpha = 1.000D+00 dphi(0) = -4.551D-01 dphi(alpha) = -2.690D-01
  neval = 45 F(x) = 7.901D+00 max|g(x)| = 3.176D+00
  x( 1.. 5) = 8.011D-01 6.445D-01 3.929D-01 2.506D-01 2.723D-01
  x( 6.. 10) = 4.574D-02 1.078D-01 -3.672D-02 3.425D-02 7.437D-02
  Line search: alpha = 1.000D+00 dphi(0) = -9.615D-01 dphi(alpha) = -1.787D-01
  neval = 46 F(x) = 7.187D+00 max|g(x)| = 6.103D+00
  x( 1.. 5) = 8.692D-01 8.546D-01 7.090D-01 5.392D-01 4.715D-01
  x( 6.. 10) = 3.436D-02 1.150D-01 2.534D-02 -3.729D-02 8.898D-02
  Line search: alpha = 6.249D-01 dphi(0) = -7.100D-01 dphi(alpha) = -2.990D-02
  neval = 48 F(x) = 6.924D+00 max|g(x)| = 6.794D+00
  x( 1.. 5) = 9.002D-01 9.247D-01 8.514D-01 6.698D-01 5.585D-01
  x( 6.. 10) = 8.413D-02 1.276D-01 4.532D-02 -7.734D-02 1.154D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.190D+00 dphi(alpha) = -4.437D-01
  neval = 49 F(x) = 6.092D+00 max|g(x)| = 4.682D+00
  x( 1.. 5) = 9.152D-01 9.180D-01 9.003D-01 7.169D-01 5.922D-01
  x( 6.. 10) = 2.842D-01 1.607D-01 4.496D-02 -1.163D-01 1.831D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.248D+00 dphi(alpha) = 6.051D-01
  neval = 50 F(x) = 5.600D+00 max|g(x)| = 5.808D+00
  x( 1.. 5) = 1.028D+00 1.001D+00 9.720D-01 8.097D-01 7.397D-01
  x( 6.. 10) = 5.874D-01 2.417D-01 9.447D-02 -1.227D-01 2.346D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.040D+00 dphi(alpha) = -3.797D-01
  neval = 51 F(x) = 4.888D+00 max|g(x)| = 5.346D+00
  x( 1.. 5) = 9.736D-01 9.539D-01 9.388D-01 7.980D-01 7.395D-01
  x( 6.. 10) = 5.921D-01 3.080D-01 6.199D-02 7.876D-04 1.901D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.229D+00 dphi(alpha) = 6.673D-01
  neval = 52 F(x) = 4.500D+00 max|g(x)| = 5.296D+00
  x( 1.. 5) = 1.006D+00 1.011D+00 1.025D+00 9.251D-01 8.893D-01
  x( 6.. 10) = 6.988D-01 4.828D-01 8.466D-02 1.536D-01 7.879D-02
  Line search: alpha = 1.000D+00 dphi(0) = -4.687D-01 dphi(alpha) = 1.343D-01
  neval = 53 F(x) = 4.344D+00 max|g(x)| = 4.993D+00
  x( 1.. 5) = 1.012D+00 1.000D+00 9.512D-01 8.500D-01 8.283D-01
  x( 6.. 10) = 6.969D-01 4.678D-01 1.013D-01 1.029D-01 6.762D-02
  Line search: alpha = 1.000D+00 dphi(0) = -1.505D-01 dphi(alpha) = 1.257D-03
  neval = 54 F(x) = 4.268D+00 max|g(x)| = 4.149D+00
  x( 1.. 5) = 1.014D+00 1.015D+00 9.906D-01 8.853D-01 8.392D-01
  x( 6.. 10) = 7.099D-01 4.616D-01 1.088D-01 7.636D-02 9.595D-02
  Line search: alpha = 1.000D+00 dphi(0) = -1.001D-01 dphi(alpha) = -4.149D-02
  neval = 55 F(x) = 4.197D+00 max|g(x)| = 4.306D+00
  x( 1.. 5) = 1.028D+00 1.020D+00 1.000D+00 9.038D-01 8.527D-01
  x( 6.. 10) = 7.048D-01 4.848D-01 1.243D-01 8.393D-02 9.890D-02
  Line search: alpha = 1.000D+00 dphi(0) = -2.142D-01 dphi(alpha) = -3.964D-02
  neval = 56 F(x) = 4.067D+00 max|g(x)| = 5.432D+00
  x( 1.. 5) = 1.037D+00 1.029D+00 1.011D+00 9.470D-01 8.842D-01
  x( 6.. 10) = 7.601D-01 5.645D-01 1.620D-01 1.226D-01 9.846D-02
  Line search: alpha = 1.000D+00 dphi(0) = -2.715D-01 dphi(alpha) = -1.050D-01
  neval = 57 F(x) = 3.879D+00 max|g(x)| = 4.951D+00
  x( 1.. 5) = 1.039D+00 1.030D+00 9.930D-01 9.614D-01 8.719D-01
  x( 6.. 10) = 7.447D-01 5.919D-01 2.152D-01 1.250D-01 1.096D-01
  Line search: alpha = 1.000D+00 dphi(0) = -3.070D-01 dphi(alpha) = -1.095D-01
  neval = 58 F(x) = 3.671D+00 max|g(x)| = 4.274D+00
  x( 1.. 5) = 1.035D+00 1.014D+00 9.633D-01 9.791D-01 8.745D-01
  x( 6.. 10) = 7.723D-01 6.103D-01 2.753D-01 1.465D-01 1.326D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.786D-01 dphi(alpha) = -6.107D-02
  neval = 59 F(x) = 3.551D+00 max|g(x)| = 5.692D+00
  x( 1.. 5) = 1.013D+00 1.002D+00 9.426D-01 9.884D-01 8.829D-01
  x( 6.. 10) = 7.868D-01 6.097D-01 3.132D-01 1.723D-01 1.271D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.170D-01 dphi(alpha) = -5.895D-02
  neval = 60 F(x) = 3.463D+00 max|g(x)| = 5.977D+00
  x( 1.. 5) = 1.002D+00 9.863D-01 9.311D-01 9.956D-01 9.055D-01
  x( 6.. 10) = 8.089D-01 6.199D-01 3.456D-01 1.883D-01 1.238D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.622D-01 dphi(alpha) = -6.614D-02
  neval = 61 F(x) = 3.348D+00 max|g(x)| = 6.004D+00
  x( 1.. 5) = 9.818D-01 9.710D-01 9.246D-01 1.003D+00 9.399D-01
  x( 6.. 10) = 8.358D-01 6.501D-01 3.941D-01 2.009D-01 1.158D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.333D-01 dphi(alpha) = -5.489D-02
  neval = 62 F(x) = 3.253D+00 max|g(x)| = 5.470D+00
  x( 1.. 5) = 9.661D-01 9.592D-01 9.251D-01 1.002D+00 9.658D-01
  x( 6.. 10) = 8.584D-01 6.894D-01 4.318D-01 2.047D-01 1.177D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.180D-01 dphi(alpha) = -5.861D-02
  neval = 63 F(x) = 3.164D+00 max|g(x)| = 4.527D+00
  x( 1.. 5) = 9.565D-01 9.566D-01 9.323D-01 9.961D-01 9.769D-01
  x( 6.. 10) = 8.692D-01 7.227D-01 4.578D-01 2.075D-01 1.283D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.703D-01 dphi(alpha) = -9.748D-02
  neval = 64 F(x) = 3.030D+00 max|g(x)| = 3.552D+00
  x( 1.. 5) = 9.534D-01 9.619D-01 9.464D-01 9.891D-01 9.853D-01
  x( 6.. 10) = 8.834D-01 7.630D-01 4.944D-01 2.259D-01 1.507D-01
  Line search: alpha = 1.000D+00 dphi(0) = -3.691D-01 dphi(alpha) = -2.079D-01
  neval = 65 F(x) = 2.740D+00 max|g(x)| = 3.929D+00
  x( 1.. 5) = 9.550D-01 9.742D-01 9.699D-01 9.761D-01 9.915D-01
  x( 6.. 10) = 9.032D-01 8.273D-01 5.758D-01 2.988D-01 2.006D-01
  Line search: alpha = 1.000D+00 dphi(0) = -7.816D-01 dphi(alpha) = -2.808D-01
  neval = 66 F(x) = 2.166D+00 max|g(x)| = 5.652D+00
  x( 1.. 5) = 9.664D-01 9.960D-01 1.006D+00 9.629D-01 1.006D+00
  x( 6.. 10) = 9.530D-01 9.461D-01 7.630D-01 5.210D-01 3.237D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.141D+00 dphi(alpha) = 1.089D+00
  neval = 67 F(x) = 1.915D+00 max|g(x)| = 7.704D+00
  x( 1.. 5) = 9.913D-01 1.014D+00 1.038D+00 9.255D-01 9.781D-01
  x( 6.. 10) = 9.485D-01 9.796D-01 9.390D-01 8.598D-01 5.838D-01
  Line search: alpha = 1.000D+00 dphi(0) = -1.245D+00 dphi(alpha) = 2.392D-01
  neval = 68 F(x) = 1.447D+00 max|g(x)| = 3.752D+00
  x( 1.. 5) = 1.017D+00 1.013D+00 1.025D+00 9.599D-01 9.663D-01
  x( 6.. 10) = 9.522D-01 9.183D-01 8.827D-01 7.328D-01 6.009D-01
  Line search: alpha = 1.000D+00 dphi(0) = -5.832D-01 dphi(alpha) = 2.852D-01
  neval = 69 F(x) = 1.278D+00 max|g(x)| = 2.947D+00
  x( 1.. 5) = 1.011D+00 1.002D+00 1.001D+00 1.014D+00 1.012D+00
  x( 6.. 10) = 1.033D+00 1.002D+00 9.254D-01 8.211D-01 6.634D-01
  Line search: alpha = 1.000D+00 dphi(0) = -3.893D-01 dphi(alpha) = 7.199D-02
  neval = 70 F(x) = 1.123D+00 max|g(x)| = 1.997D+00
  x( 1.. 5) = 9.987D-01 9.937D-01 9.902D-01 9.938D-01 9.813D-01
  x( 6.. 10) = 9.716D-01 9.554D-01 9.172D-01 8.887D-01 7.540D-01
  Line search: alpha = 4.421D-01 dphi(0) = -2.062D-01 dphi(alpha) = -9.090D-03
  neval = 72 F(x) = 1.074D+00 max|g(x)| = 1.214D+00
  x( 1.. 5) = 1.002D+00 9.983D-01 9.950D-01 9.967D-01 9.854D-01
  x( 6.. 10) = 9.759D-01 9.678D-01 9.644D-01 9.228D-01 8.105D-01
  Line search: alpha = 1.000D+00 dphi(0) = -7.227D-02 dphi(alpha) = -1.761D-02
  neval = 73 F(x) = 1.029D+00 max|g(x)| = 1.010D+00
  x( 1.. 5) = 1.007D+00 1.004D+00 1.006D+00 1.001D+00 9.958D-01
  x( 6.. 10) = 9.945D-01 9.774D-01 9.741D-01 9.404D-01 8.759D-01
  Line search: alpha = 1.000D+00 dphi(0) = -4.075D-02 dphi(alpha) = -1.016D-02
  neval = 74 F(x) = 1.004D+00 max|g(x)| = 3.875D-01
  x( 1.. 5) = 1.003D+00 1.000D+00 1.002D+00 1.002D+00 1.001D+00
  x( 6.. 10) = 1.003D+00 9.974D-01 9.948D-01 9.826D-01 9.615D-01
  Line search: alpha = 1.000D+00 dphi(0) = -7.421D-03 dphi(alpha) = -3.292D-06
  Optimization stopped after 75 function evaluations.
  Stopped by function evaluation limit (maxeval)
  maxgradient laststep stepmax neval
  0.08158646 0.03857951 0.40516875 75.00000000
  ucminf message: Stopped by function evaluation limit (maxeval)
  Post processing for method ucminf
  Compute gradient approximation at finish of ucminf
  Compute Hessian approximation at finish of ucminf
  Save results from method ucminf
  Assemble the answers
  Sort results
  > outline-regexp: "\\(> \\)?### [*]+" ***
  Error: unexpected '*' in "outline-regexp: "\\(> \\)?### [*]+" ***"
  Execution halted