R Under development (unstable) (2024-11-22 r87365 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > if (requireNamespace("testthat", quietly = TRUE)) { + library("testthat") + library("mlr3learners") + test_check("mlr3learners") + } Loading required package: mlr3 # weights: 3 (2 variable) initial value 20.794415 final value 19.095425 converged # weights: 3 (2 variable) initial value 20.794415 final value 19.095425 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.794415 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.794415 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.629373 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.629373 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.727699 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.727699 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.020452 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.020452 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.020452 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.020452 converged # weights: 7 (6 variable) initial value 12.274445 iter 10 value 7.536897 iter 20 value 7.461070 final value 7.461070 converged # weights: 7 (6 variable) initial value 12.274445 iter 10 value 7.536897 iter 20 value 7.461070 final value 7.461070 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.020452 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.020452 converged # weights: 9 (4 variable) initial value 32.958369 final value 31.732830 converged # weights: 9 (4 variable) initial value 32.958369 final value 31.732830 converged # weights: 9 (4 variable) initial value 32.958369 final value 32.350660 converged # weights: 9 (4 variable) initial value 32.958369 final value 32.350660 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 30.522063 iter 10 value 30.522063 final value 30.522063 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 30.522063 iter 10 value 30.522063 final value 30.522063 converged # weights: 9 (4 variable) initial value 32.958369 final value 32.555658 converged # weights: 9 (4 variable) initial value 32.958369 final value 32.555658 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 27.984042 final value 27.967204 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 27.984042 final value 27.967204 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 27.984042 final value 27.967204 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 27.984042 final value 27.967204 converged # weights: 21 (12 variable) initial value 19.839853 iter 10 value 15.427895 iter 20 value 15.382471 iter 30 value 15.381849 final value 15.381848 converged # weights: 21 (12 variable) initial value 19.839853 iter 10 value 15.427895 iter 20 value 15.382471 iter 30 value 15.381849 final value 15.381848 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 27.984042 final value 27.967204 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 27.984042 final value 27.967204 converged # weights: 4 (3 variable) initial value 138.629436 iter 10 value 0.097335 iter 20 value 0.008928 iter 30 value 0.003213 iter 40 value 0.002454 iter 50 value 0.002153 iter 60 value 0.001766 iter 70 value 0.001529 iter 80 value 0.001283 iter 90 value 0.001169 iter 100 value 0.000964 final value 0.000964 stopped after 100 iterations # weights: 4 (3 variable) initial value 138.629436 iter 10 value 0.097335 iter 20 value 0.008928 iter 30 value 0.003213 iter 40 value 0.002454 iter 50 value 0.002153 iter 60 value 0.001766 iter 70 value 0.001529 iter 80 value 0.001283 iter 90 value 0.001169 iter 100 value 0.000964 final value 0.000964 stopped after 100 iterations # weights: 4 (3 variable) initial value 138.629436 iter 10 value 0.097335 iter 20 value 0.008928 iter 30 value 0.003213 iter 40 value 0.002454 iter 50 value 0.002153 iter 60 value 0.001766 iter 70 value 0.001529 iter 80 value 0.001283 iter 90 value 0.001169 iter 100 value 0.000964 final value 0.000964 stopped after 100 iterations # weights: 4 (3 variable) initial value 138.629436 iter 10 value 0.097335 iter 20 value 0.008928 iter 30 value 0.003213 iter 40 value 0.002454 iter 50 value 0.002153 iter 60 value 0.001766 iter 70 value 0.001529 iter 80 value 0.001283 iter 90 value 0.001169 iter 100 value 0.000964 final value 0.000964 stopped after 100 iterations # weights: 4 (3 variable) initial value 138.629436 iter 10 value 0.097335 iter 20 value 0.008928 iter 30 value 0.003213 iter 40 value 0.002454 iter 50 value 0.002153 iter 60 value 0.001766 iter 70 value 0.001529 iter 80 value 0.001283 iter 90 value 0.001169 iter 100 value 0.000964 final value 0.000964 stopped after 100 iterations # weights: 4 (3 variable) initial value 138.629436 iter 10 value 0.097335 iter 20 value 0.008928 iter 30 value 0.003213 iter 40 value 0.002454 iter 50 value 0.002153 iter 60 value 0.001766 iter 70 value 0.001529 iter 80 value 0.001283 iter 90 value 0.001169 iter 100 value 0.000964 final value 0.000964 stopped after 100 iterations # weights: 18 (10 variable) initial value 164.791843 iter 10 value 16.177348 iter 20 value 7.111438 iter 30 value 6.182999 iter 40 value 5.984028 iter 50 value 5.961278 iter 60 value 5.954900 iter 70 value 5.951851 iter 80 value 5.950343 iter 90 value 5.949904 iter 100 value 5.949867 final value 5.949867 stopped after 100 iterations optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.072495e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.907582 - variance bounds : 0.1072495 10.89349 - best initial criterion value(s) : -84.67456 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 84.675 |proj g|= 9.9844 At iterate 1 f = 78.732 |proj g|= 1.8207 At iterate 2 f = 73.405 |proj g|= 1.3527 At iterate 3 f = 72.613 |proj g|= 1.1631 At iterate 4 f = 72.196 |proj g|= 0.45545 At iterate 5 f = 72.193 |proj g|= 0.22539 At iterate 6 f = 72.192 |proj g|= 0.0088511 At iterate 7 f = 72.192 |proj g|= 0.00016402 At iterate 8 f = 72.192 |proj g|= 1.2218e-07 iterations 8 function evaluations 12 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 1 norm of the final projected gradient 1.22184e-07 final function value 72.1915 F = 72.1915 final value 72.191543 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.072495e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.907582 - variance bounds : 0.1072495 10.89349 - best initial criterion value(s) : -84.67456 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 84.675 |proj g|= 9.9844 At iterate 1 f = 78.732 |proj g|= 1.8207 At iterate 2 f = 73.405 |proj g|= 1.3527 At iterate 3 f = 72.613 |proj g|= 1.1631 At iterate 4 f = 72.196 |proj g|= 0.45545 At iterate 5 f = 72.193 |proj g|= 0.22539 At iterate 6 f = 72.192 |proj g|= 0.0088511 At iterate 7 f = 72.192 |proj g|= 0.00016402 At iterate 8 f = 72.192 |proj g|= 1.2218e-07 iterations 8 function evaluations 12 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 1 norm of the final projected gradient 1.22184e-07 final function value 72.1915 F = 72.1915 final value 72.191543 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.072495e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.907582 - variance bounds : 0.1072495 10.89349 - best initial criterion value(s) : -150.6013 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 150.6 |proj g|= 10.311 At iterate 1 f = 108.06 |proj g|= 2.0735 At iterate 2 f = 73.05 |proj g|= 8.726 At iterate 3 f = 72.362 |proj g|= 1.0766 At iterate 4 f = 72.223 |proj g|= 0.99842 At iterate 5 f = 72.194 |proj g|= 0.35255 At iterate 6 f = 72.192 |proj g|= 0.036126 At iterate 7 f = 72.192 |proj g|= 0.0010366 At iterate 8 f = 72.192 |proj g|= 3.1612e-06 iterations 8 function evaluations 22 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 3.16119e-06 final function value 72.1915 F = 72.1915 final value 72.191543 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.072495e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.907582 - variance bounds : 0.1072495 10.89349 - best initial criterion value(s) : -150.6013 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 150.6 |proj g|= 10.311 At iterate 1 f = 108.06 |proj g|= 2.0735 At iterate 2 f = 73.05 |proj g|= 8.726 At iterate 3 f = 72.362 |proj g|= 1.0766 At iterate 4 f = 72.223 |proj g|= 0.99842 At iterate 5 f = 72.194 |proj g|= 0.35255 At iterate 6 f = 72.192 |proj g|= 0.036126 At iterate 7 f = 72.192 |proj g|= 0.0010366 At iterate 8 f = 72.192 |proj g|= 3.1612e-06 iterations 8 function evaluations 22 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 3.16119e-06 final function value 72.1915 F = 72.1915 final value 72.191543 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.072495e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.907582 - variance bounds : 0.1072495 10.89349 - best initial criterion value(s) : -32836.94 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 32837 |proj g|= 10.228 At iterate 1 f = 108.06 |proj g|= 2.0735 At iterate 2 f = 74.494 |proj g|= 10.189 At iterate 3 f = 72.853 |proj g|= 1.2277 At iterate 4 f = 72.45 |proj g|= 1.1107 At iterate 5 f = 72.206 |proj g|= 0.77409 At iterate 6 f = 72.193 |proj g|= 0.29766 At iterate 7 f = 72.192 |proj g|= 0.02043 At iterate 8 f = 72.192 |proj g|= 0.00049692 At iterate 9 f = 72.192 |proj g|= 8.5551e-07 iterations 9 function evaluations 25 segments explored during Cauchy searches 12 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 8.55508e-07 final function value 72.1915 F = 72.1915 final value 72.191543 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.072495e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.907582 - variance bounds : 0.1072495 10.89349 - best initial criterion value(s) : -246.2382 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 246.24 |proj g|= 9.9952 At iterate 1 f = 108.06 |proj g|= 2.0735 At iterate 2 f = 73.698 |proj g|= 1.4121 ys=-2.811e+01 -gs= 1.944e+01, BFGS update SKIPPED At iterate 3 f = 72.923 |proj g|= 1.2451 At iterate 4 f = 72.214 |proj g|= 0.95282 At iterate 5 f = 72.203 |proj g|= 0.74543 At iterate 6 f = 72.192 |proj g|= 0.061298 At iterate 7 f = 72.192 |proj g|= 0.003587 At iterate 8 f = 72.192 |proj g|= 1.8615e-05 At iterate 9 f = 72.192 |proj g|= 5.6166e-09 iterations 9 function evaluations 25 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 5.6166e-09 final function value 72.1915 F = 72.1915 final value 72.191543 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 3.435024e-07 - parameters lower bounds : 1e-10 1e-10 - parameters upper bounds : 1.880582 44.27959 - variance bounds : 3.435024 622.0591 - best initial criterion value(s) : -336.0713 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 336.07 |proj g|= 44.232 At iterate 1 f = 321.46 |proj g|= 44.178 At iterate 2 f = 320 |proj g|= 1.5451 At iterate 3 f = 317.22 |proj g|= 15.073 At iterate 4 f = 297.03 |proj g|= 44.177 At iterate 5 f = 290.74 |proj g|= 44.166 At iterate 6 f = 286.58 |proj g|= 44.148 At iterate 7 f = 283.05 |proj g|= 44.114 At iterate 8 f = 278.53 |proj g|= 44.064 At iterate 9 f = 275.29 |proj g|= 44.011 At iterate 10 f = 270.53 |proj g|= 43.917 At iterate 11 f = 262.14 |proj g|= 43.545 At iterate 12 f = 252.89 |proj g|= 1.1141 At iterate 13 f = 245.62 |proj g|= 17.463 At iterate 14 f = 243.88 |proj g|= 8.3488 At iterate 15 f = 243.44 |proj g|= 1.3692 At iterate 16 f = 243.4 |proj g|= 1.7257 At iterate 17 f = 243.4 |proj g|= 0.094388 At iterate 18 f = 243.4 |proj g|= 0.093957 At iterate 19 f = 243.4 |proj g|= 0.24065 At iterate 20 f = 243.4 |proj g|= 1.7252 At iterate 21 f = 243.39 |proj g|= 1.7257 At iterate 22 f = 243.39 |proj g|= 1.7267 At iterate 23 f = 243.37 |proj g|= 1.7282 At iterate 24 f = 243.34 |proj g|= 1.7302 At iterate 25 f = 243.3 |proj g|= 1.7313 At iterate 26 f = 243.28 |proj g|= 1.1893 At iterate 27 f = 243.28 |proj g|= 1.6612 At iterate 28 f = 243.27 |proj g|= 1.729 At iterate 29 f = 243.27 |proj g|= 1.2724 At iterate 30 f = 243.26 |proj g|= 1.7284 At iterate 31 f = 243.26 |proj g|= 0.15268 At iterate 32 f = 243.26 |proj g|= 0.0091747 At iterate 33 f = 243.26 |proj g|= 0.0034398 iterations 33 function evaluations 42 segments explored during Cauchy searches 35 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.00343978 final function value 243.264 F = 243.264 final value 243.264413 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 3.435024e-07 - parameters lower bounds : 1e-10 1e-10 - parameters upper bounds : 1.880582 44.27959 - variance bounds : 3.435024 622.0591 - best initial criterion value(s) : -18081.2 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 18081 |proj g|= 495.47 At iterate 1 f = 408.87 |proj g|= 0.088067 At iterate 2 f = 320.3 |proj g|= 0.61345 At iterate 3 f = 318.22 |proj g|= 0.010031 At iterate 4 f = 318.22 |proj g|= 0.0042899 At iterate 5 f = 318.22 |proj g|= 5.9998e-05 At iterate 6 f = 318.22 |proj g|= 3.5265e-07 iterations 6 function evaluations 27 segments explored during Cauchy searches 8 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 3.52652e-07 final function value 318.222 F = 318.222 final value 318.221782 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 3.435024e-07 - parameters lower bounds : 1e-10 1e-10 - parameters upper bounds : 1.880582 44.27959 - variance bounds : 3.435024 622.0591 - best initial criterion value(s) : -33603.33 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 33603 |proj g|= 579.38 At iterate 1 f = 416.28 |proj g|= 0.075984 At iterate 2 f = 324.91 |proj g|= 0.36081 ys=-1.601e+02 -gs= 4.272e+01, BFGS update SKIPPED At iterate 3 f = 318.23 |proj g|= 0.031225 At iterate 4 f = 318.22 |proj g|= 3.7816e-05 At iterate 5 f = 318.22 |proj g|= 1.5258e-06 iterations 5 function evaluations 20 segments explored during Cauchy searches 7 BFGS updates skipped 1 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 1.52578e-06 final function value 318.222 F = 318.222 final value 318.221782 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 3.435024e-07 - parameters lower bounds : 1e-10 1e-10 - parameters upper bounds : 1.880582 44.27959 - variance bounds : 3.435024 622.0591 - best initial criterion value(s) : -1946.936 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 1946.9 |proj g|= 41.202 At iterate 1 f = 334.21 |proj g|= 0.35219 At iterate 2 f = 319.19 |proj g|= 0.22126 At iterate 3 f = 318.3 |proj g|= 0.076896 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 318.27 |proj g|= 0.06341 At iterate 5 f = 318.22 |proj g|= 0.00871 At iterate 6 f = 318.22 |proj g|= 0.00083012 At iterate 7 f = 318.22 |proj g|= 9.7056e-06 At iterate 8 f = 318.22 |proj g|= 1.0975e-08 iterations 8 function evaluations 20 segments explored during Cauchy searches 12 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 1.09751e-08 final function value 318.222 F = 318.222 final value 318.221782 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 6.874986e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.952191 - variance bounds : 0.06386566 6.874986 - best initial criterion value(s) : -32281.44 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 32281 |proj g|= 6.1202 At iterate 1 f = 96.594 |proj g|= 3.28 At iterate 2 f = 62.477 |proj g|= 0.89679 ys=-2.657e+01 -gs= 1.940e+01, BFGS update SKIPPED At iterate 3 f = 61.952 |proj g|= 0.82502 At iterate 4 f = 61.106 |proj g|= 0.64506 At iterate 5 f = 61.075 |proj g|= 0.15507 At iterate 6 f = 61.074 |proj g|= 0.016808 At iterate 7 f = 61.074 |proj g|= 0.00014181 At iterate 8 f = 61.074 |proj g|= 1.2833e-07 iterations 8 function evaluations 30 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.28333e-07 final function value 61.0745 F = 61.0745 final value 61.074473 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 6.874986e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.952191 - variance bounds : 0.06386566 6.874986 - best initial criterion value(s) : -32281.44 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 32281 |proj g|= 6.1202 At iterate 1 f = 96.594 |proj g|= 3.28 At iterate 2 f = 62.477 |proj g|= 0.89679 ys=-2.657e+01 -gs= 1.940e+01, BFGS update SKIPPED At iterate 3 f = 61.952 |proj g|= 0.82502 At iterate 4 f = 61.106 |proj g|= 0.64506 At iterate 5 f = 61.075 |proj g|= 0.15507 At iterate 6 f = 61.074 |proj g|= 0.016808 At iterate 7 f = 61.074 |proj g|= 0.00014181 At iterate 8 f = 61.074 |proj g|= 1.2833e-07 iterations 8 function evaluations 30 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.28333e-07 final function value 61.0745 F = 61.0745 final value 61.074473 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 6.874986e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.952191 - variance bounds : 0.06386566 6.874986 - best initial criterion value(s) : -239772.6 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2.3977e+05 |proj g|= 6.5165 At iterate 1 f = 96.594 |proj g|= 3.28 At iterate 2 f = 61.308 |proj g|= 0.71113 ys=-5.699e+00 -gs= 2.001e+01, BFGS update SKIPPED At iterate 3 f = 61.195 |proj g|= 0.68052 At iterate 4 f = 61.092 |proj g|= 1.4416 At iterate 5 f = 61.075 |proj g|= 0.28493 At iterate 6 f = 61.074 |proj g|= 0.020568 At iterate 7 f = 61.074 |proj g|= 0.00032252 At iterate 8 f = 61.074 |proj g|= 3.5803e-07 iterations 8 function evaluations 28 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 3.58032e-07 final function value 61.0745 F = 61.0745 final value 61.074473 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 6.874986e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.952191 - variance bounds : 0.06386566 6.874986 - best initial criterion value(s) : -239772.6 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2.3977e+05 |proj g|= 6.5165 At iterate 1 f = 96.594 |proj g|= 3.28 At iterate 2 f = 61.308 |proj g|= 0.71113 ys=-5.699e+00 -gs= 2.001e+01, BFGS update SKIPPED At iterate 3 f = 61.195 |proj g|= 0.68052 At iterate 4 f = 61.092 |proj g|= 1.4416 At iterate 5 f = 61.075 |proj g|= 0.28493 At iterate 6 f = 61.074 |proj g|= 0.020568 At iterate 7 f = 61.074 |proj g|= 0.00032252 At iterate 8 f = 61.074 |proj g|= 3.5803e-07 iterations 8 function evaluations 28 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 3.58032e-07 final function value 61.0745 F = 61.0745 final value 61.074473 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 6.874986e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.952191 - variance bounds : 0.06386566 6.874986 - best initial criterion value(s) : -76.17216 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 76.172 |proj g|= 6.3348 At iterate 1 f = 69.204 |proj g|= 1.1583 At iterate 2 f = 61.643 |proj g|= 0.77661 At iterate 3 f = 61.198 |proj g|= 0.68156 At iterate 4 f = 61.13 |proj g|= 2.6934 At iterate 5 f = 61.077 |proj g|= 0.49602 At iterate 6 f = 61.075 |proj g|= 0.062993 At iterate 7 f = 61.074 |proj g|= 0.0017502 At iterate 8 f = 61.074 |proj g|= 5.9673e-06 iterations 8 function evaluations 11 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 1 norm of the final projected gradient 5.96726e-06 final function value 61.0745 F = 61.0745 final value 61.074473 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 6.874986e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.952191 - variance bounds : 0.06386566 6.874986 - best initial criterion value(s) : -826288.4 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 8.2629e+05 |proj g|= 6.1449 At iterate 1 f = 96.594 |proj g|= 3.28 At iterate 2 f = 61.118 |proj g|= 0.65145 At iterate 3 f = 61.075 |proj g|= 0.19823 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 61.074 |proj g|= 0.0031815 At iterate 5 f = 61.074 |proj g|= 3.4462e-05 iterations 5 function evaluations 23 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 3.44624e-05 final function value 61.0745 F = 61.0745 final value 61.074473 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 3.435024e-07 - parameters lower bounds : 1e-10 1e-10 - parameters upper bounds : 1.880582 44.27959 - variance bounds : 3.435024 622.0591 - best initial criterion value(s) : -275.642 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 275.64 |proj g|= 10.425 At iterate 1 f = 264.04 |proj g|= 2.7608 At iterate 2 f = 260.58 |proj g|= 2.4334 At iterate 3 f = 260.4 |proj g|= 2.5923 At iterate 4 f = 260.15 |proj g|= 2.5699 At iterate 5 f = 252.84 |proj g|= 1.9781 At iterate 6 f = 252.63 |proj g|= 1.7855 At iterate 7 f = 251.31 |proj g|= 1.8116 At iterate 8 f = 250.39 |proj g|= 1.7591 At iterate 9 f = 250.33 |proj g|= 0.36097 At iterate 10 f = 250.33 |proj g|= 0.22395 At iterate 11 f = 250.33 |proj g|= 0.22427 At iterate 12 f = 250.33 |proj g|= 1.6946 At iterate 13 f = 250.32 |proj g|= 1.695 At iterate 14 f = 250.32 |proj g|= 1.6959 At iterate 15 f = 250.3 |proj g|= 1.6973 At iterate 16 f = 250.26 |proj g|= 1.6997 At iterate 17 f = 250.15 |proj g|= 2.4261 At iterate 18 f = 249.84 |proj g|= 4.2598 At iterate 19 f = 248.9 |proj g|= 7.6264 At iterate 20 f = 247.07 |proj g|= 11.085 At iterate 21 f = 246.36 |proj g|= 11.857 At iterate 22 f = 243.42 |proj g|= 4.2343 At iterate 23 f = 243.31 |proj g|= 2.2815 At iterate 24 f = 243.31 |proj g|= 1.9882 At iterate 25 f = 243.3 |proj g|= 1.9318 At iterate 26 f = 243.27 |proj g|= 1.7274 At iterate 27 f = 243.27 |proj g|= 1.7281 At iterate 28 f = 243.26 |proj g|= 0.53757 At iterate 29 f = 243.26 |proj g|= 0.079163 At iterate 30 f = 243.26 |proj g|= 0.013389 At iterate 31 f = 243.26 |proj g|= 0.0012235 At iterate 32 f = 243.26 |proj g|= 0.0009449 iterations 32 function evaluations 42 segments explored during Cauchy searches 33 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.000944899 final function value 243.264 F = 243.264 final value 243.264413 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 3.435024e-07 - parameters lower bounds : 1e-10 1e-10 - parameters upper bounds : 1.880582 44.27959 - variance bounds : 3.435024 622.0591 - best initial criterion value(s) : -534.2055 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 534.21 |proj g|= 18.408 At iterate 1 f = 329.47 |proj g|= 0.36626 At iterate 2 f = 326.46 |proj g|= 0.36624 At iterate 3 f = 319.71 |proj g|= 0.25596 At iterate 4 f = 318.44 |proj g|= 0.15359 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 5 f = 318.34 |proj g|= 0.10833 At iterate 6 f = 318.22 |proj g|= 0.015669 At iterate 7 f = 318.22 |proj g|= 0.0019108 At iterate 8 f = 318.22 |proj g|= 3.9547e-05 At iterate 9 f = 318.22 |proj g|= 1.0245e-07 iterations 9 function evaluations 18 segments explored during Cauchy searches 13 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 1.02452e-07 final function value 318.222 F = 318.222 final value 318.221782 converged [ FAIL 0 | WARN 0 | SKIP 4 | PASS 513 ] ══ Skipped tests (4) ═══════════════════════════════════════════════════════════ • On CRAN (4): 'test_classif_nnet.R:2:1', 'test_classif_xgboost.R:2:1', 'test_regr_nnet.R:2:1', 'test_regr_xgboost.R:2:1' [ FAIL 0 | WARN 0 | SKIP 4 | PASS 513 ] > > proc.time() user system elapsed 43.07 2.34 45.50