R Under development (unstable) (2024-10-22 r87265 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 20.727699 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.727699 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.694248 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.694248 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.593420 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.593420 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 20.416235 converged # weights: 6 (5 variable) initial value 20.794415 final value 20.416235 converged # weights: 6 (5 variable) initial value 20.794415 final value 20.416235 converged # weights: 6 (5 variable) initial value 20.794415 final value 20.416235 converged # weights: 7 (6 variable) initial value 10.418283 iter 10 value 10.131785 final value 10.131729 converged # weights: 7 (6 variable) initial value 10.418283 iter 10 value 10.131785 final value 10.131729 converged # weights: 6 (5 variable) initial value 20.794415 final value 20.416235 converged # weights: 6 (5 variable) initial value 20.794415 final value 20.416235 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 31.084245 converged # weights: 9 (4 variable) initial value 32.958369 final value 31.084245 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 29.924256 iter 10 value 29.924256 final value 29.924256 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 29.924256 iter 10 value 29.924256 final value 29.924256 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: 18 (10 variable) initial value 32.958369 iter 10 value 23.226888 final value 23.196216 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.226888 final value 23.196216 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.226888 final value 23.196216 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.226888 final value 23.196216 converged # weights: 21 (12 variable) initial value 15.140887 iter 10 value 6.021255 iter 20 value 5.042984 iter 30 value 4.871448 iter 40 value 4.865594 iter 50 value 4.863359 iter 60 value 4.861959 iter 70 value 4.860837 iter 80 value 4.859848 iter 90 value 4.859708 iter 100 value 4.859651 final value 4.859651 stopped after 100 iterations # weights: 21 (12 variable) initial value 15.140887 iter 10 value 6.021255 iter 20 value 5.042984 iter 30 value 4.871448 iter 40 value 4.865594 iter 50 value 4.863359 iter 60 value 4.861959 iter 70 value 4.860837 iter 80 value 4.859848 iter 90 value 4.859708 iter 100 value 4.859651 final value 4.859651 stopped after 100 iterations # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.226888 final value 23.196216 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.226888 final value 23.196216 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 : 9.267963e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.884757 - variance bounds : 0.09267963 9.96869 - best initial criterion value(s) : -2225.728 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2225.7 |proj g|= 8.8485 At iterate 1 f = 105.71 |proj g|= 2.2794 At iterate 2 f = 68.684 |proj g|= 3.3834 At iterate 3 f = 68.544 |proj g|= 0.42644 At iterate 4 f = 68.541 |proj g|= 0.083412 At iterate 5 f = 68.541 |proj g|= 0.0027104 At iterate 6 f = 68.541 |proj g|= 1.6548e-05 iterations 6 function evaluations 20 segments explored during Cauchy searches 9 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.65485e-05 final function value 68.5413 F = 68.5413 final value 68.541322 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 9.267963e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.884757 - variance bounds : 0.09267963 9.96869 - best initial criterion value(s) : -2225.728 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2225.7 |proj g|= 8.8485 At iterate 1 f = 105.71 |proj g|= 2.2794 At iterate 2 f = 68.684 |proj g|= 3.3834 At iterate 3 f = 68.544 |proj g|= 0.42644 At iterate 4 f = 68.541 |proj g|= 0.083412 At iterate 5 f = 68.541 |proj g|= 0.0027104 At iterate 6 f = 68.541 |proj g|= 1.6548e-05 iterations 6 function evaluations 20 segments explored during Cauchy searches 9 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.65485e-05 final function value 68.5413 F = 68.5413 final value 68.541322 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 9.267963e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.884757 - variance bounds : 0.09267963 9.96869 - best initial criterion value(s) : -90.37259 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 90.373 |proj g|= 8.6874 At iterate 1 f = 84.188 |proj g|= 3.8547 At iterate 2 f = 69.586 |proj g|= 1.1378 ys=-3.631e+00 -gs= 1.082e+01, BFGS update SKIPPED At iterate 3 f = 68.757 |proj g|= 4.2954 At iterate 4 f = 68.618 |proj g|= 0.89051 At iterate 5 f = 68.546 |proj g|= 0.51227 At iterate 6 f = 68.541 |proj g|= 0.093071 At iterate 7 f = 68.541 |proj g|= 0.0036028 At iterate 8 f = 68.541 |proj g|= 2.4168e-05 At iterate 9 f = 68.541 |proj g|= 6.3289e-09 iterations 9 function evaluations 12 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 6.32893e-09 final function value 68.5413 F = 68.5413 final value 68.541322 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 9.267963e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.884757 - variance bounds : 0.09267963 9.96869 - best initial criterion value(s) : -90.37259 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 90.373 |proj g|= 8.6874 At iterate 1 f = 84.188 |proj g|= 3.8547 At iterate 2 f = 69.586 |proj g|= 1.1378 ys=-3.631e+00 -gs= 1.082e+01, BFGS update SKIPPED At iterate 3 f = 68.757 |proj g|= 4.2954 At iterate 4 f = 68.618 |proj g|= 0.89051 At iterate 5 f = 68.546 |proj g|= 0.51227 At iterate 6 f = 68.541 |proj g|= 0.093071 At iterate 7 f = 68.541 |proj g|= 0.0036028 At iterate 8 f = 68.541 |proj g|= 2.4168e-05 At iterate 9 f = 68.541 |proj g|= 6.3289e-09 iterations 9 function evaluations 12 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 6.32893e-09 final function value 68.5413 F = 68.5413 final value 68.541322 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 9.267963e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.884757 - variance bounds : 0.09267963 9.96869 - best initial criterion value(s) : -72.87087 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 72.871 |proj g|= 8.2132 At iterate 1 f = 71.73 |proj g|= 1.2616 At iterate 2 f = 68.27 |proj g|= 1.8842 At iterate 3 f = 68.247 |proj g|= 0.67645 At iterate 4 f = 68.247 |proj g|= 1.511 At iterate 5 f = 68.247 |proj g|= 0.67107 At iterate 6 f = 68.247 |proj g|= 0.67142 At iterate 7 f = 68.247 |proj g|= 0.6723 At iterate 8 f = 68.246 |proj g|= 0.67383 At iterate 9 f = 68.246 |proj g|= 0.67616 At iterate 10 f = 68.244 |proj g|= 0.67839 At iterate 11 f = 68.243 |proj g|= 0.68088 At iterate 12 f = 68.239 |proj g|= 0.6868 At iterate 13 f = 68.201 |proj g|= 1.4034 At iterate 14 f = 67.851 |proj g|= 1.8842 At iterate 15 f = 67.845 |proj g|= 0.038248 At iterate 16 f = 67.845 |proj g|= 0.042559 At iterate 17 f = 67.845 |proj g|= 0.043413 At iterate 18 f = 67.845 |proj g|= 0.23505 At iterate 19 f = 67.845 |proj g|= 0.49216 At iterate 20 f = 67.845 |proj g|= 1.8841 At iterate 21 f = 67.845 |proj g|= 1.8841 At iterate 22 f = 67.844 |proj g|= 1.8841 At iterate 23 f = 67.844 |proj g|= 1.8841 At iterate 24 f = 67.842 |proj g|= 1.8842 At iterate 25 f = 67.84 |proj g|= 1.8842 At iterate 26 f = 67.839 |proj g|= 1.8843 At iterate 27 f = 67.838 |proj g|= 0.039026 At iterate 28 f = 67.837 |proj g|= 0.0014729 At iterate 29 f = 67.837 |proj g|= 0.25277 At iterate 30 f = 67.837 |proj g|= 1.8843 At iterate 31 f = 67.837 |proj g|= 1.8843 At iterate 32 f = 67.837 |proj g|= 1.8843 At iterate 33 f = 67.837 |proj g|= 1.8843 At iterate 34 f = 67.837 |proj g|= 1.8843 At iterate 35 f = 67.837 |proj g|= 0.010064 At iterate 36 f = 67.837 |proj g|= 0.27683 At iterate 37 f = 67.837 |proj g|= 0.11745 iterations 37 function evaluations 45 segments explored during Cauchy searches 39 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.117448 final function value 67.837 F = 67.837 final value 67.836981 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 9.267963e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.884757 - variance bounds : 0.09267963 9.96869 - best initial criterion value(s) : -866.6129 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 866.61 |proj g|= 8.6084 At iterate 1 f = 105.71 |proj g|= 2.2794 At iterate 2 f = 70.052 |proj g|= 1.2209 ys=-3.110e+01 -gs= 1.973e+01, BFGS update SKIPPED At iterate 3 f = 69.401 |proj g|= 1.1021 At iterate 4 f = 68.572 |proj g|= 0.86195 At iterate 5 f = 68.542 |proj g|= 0.10843 At iterate 6 f = 68.541 |proj g|= 0.011478 At iterate 7 f = 68.541 |proj g|= 9.1243e-05 At iterate 8 f = 68.541 |proj g|= 7.602e-08 iterations 8 function evaluations 26 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 7.60203e-08 final function value 68.5413 F = 68.5413 final value 68.541322 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) : -6785.209 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 6785.2 |proj g|= 207.53 At iterate 1 f = 372.92 |proj g|= 0.17912 At iterate 2 f = 318.23 |proj g|= 0.030746 At iterate 3 f = 318.22 |proj g|= 0.0021029 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 318.22 |proj g|= 0.0016506 At iterate 5 f = 318.22 |proj g|= 4.7518e-06 At iterate 6 f = 318.22 |proj g|= 1.0699e-08 iterations 6 function evaluations 26 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 1.06991e-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 : 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) : -263.3952 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 263.4 |proj g|= 1.2398 At iterate 1 f = 249.52 |proj g|= 13.935 At iterate 2 f = 245.24 |proj g|= 10.924 At iterate 3 f = 243.48 |proj g|= 4.8004 At iterate 4 f = 243.33 |proj g|= 1.7271 At iterate 5 f = 243.32 |proj g|= 0.15488 At iterate 6 f = 243.32 |proj g|= 0.069073 At iterate 7 f = 243.32 |proj g|= 0.065575 At iterate 8 f = 243.32 |proj g|= 0.11005 At iterate 9 f = 243.32 |proj g|= 0.15477 At iterate 10 f = 243.32 |proj g|= 0.15487 At iterate 11 f = 243.32 |proj g|= 0.15504 At iterate 12 f = 243.32 |proj g|= 0.1553 At iterate 13 f = 243.32 |proj g|= 0.32214 At iterate 14 f = 243.32 |proj g|= 0.15618 At iterate 15 f = 243.31 |proj g|= 0.39845 At iterate 16 f = 243.29 |proj g|= 0.9325 At iterate 17 f = 243.27 |proj g|= 0.88485 At iterate 18 f = 243.26 |proj g|= 0.24157 At iterate 19 f = 243.26 |proj g|= 0.011643 At iterate 20 f = 243.26 |proj g|= 0.031573 iterations 20 function evaluations 26 segments explored during Cauchy searches 22 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.0315732 final function value 243.264 F = 243.264 final value 243.264414 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) : -626.0596 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 626.06 |proj g|= 6.9759 At iterate 1 f = 328.16 |proj g|= 0.36753 At iterate 2 f = 320.98 |proj g|= 0.30659 At iterate 3 f = 318.22 |proj g|= 0.0058755 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 318.22 |proj g|= 0.0045873 At iterate 5 f = 318.22 |proj g|= 3.6022e-05 At iterate 6 f = 318.22 |proj g|= 2.2302e-07 iterations 6 function evaluations 17 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 2.23024e-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) : -301.5254 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 301.53 |proj g|= 2.9232 At iterate 1 f = 253.22 |proj g|= 2.0255 At iterate 2 f = 245.48 |proj g|= 8.2179 At iterate 3 f = 243.87 |proj g|= 1.722 At iterate 4 f = 243.85 |proj g|= 1.7221 At iterate 5 f = 243.85 |proj g|= 0.17717 At iterate 6 f = 243.85 |proj g|= 0.16395 At iterate 7 f = 243.85 |proj g|= 0.5594 At iterate 8 f = 243.85 |proj g|= 1.5459 At iterate 9 f = 243.84 |proj g|= 1.7219 At iterate 10 f = 243.84 |proj g|= 1.723 At iterate 11 f = 243.81 |proj g|= 1.725 At iterate 12 f = 243.76 |proj g|= 1.728 At iterate 13 f = 243.65 |proj g|= 1.7318 At iterate 14 f = 243.51 |proj g|= 1.7365 At iterate 15 f = 243.41 |proj g|= 1.422 At iterate 16 f = 243.3 |proj g|= 1.7306 At iterate 17 f = 243.27 |proj g|= 1.7289 At iterate 18 f = 243.26 |proj g|= 0.59316 At iterate 19 f = 243.26 |proj g|= 0.15464 At iterate 20 f = 243.26 |proj g|= 0.0059245 At iterate 21 f = 243.26 |proj g|= 4.306e-05 iterations 21 function evaluations 31 segments explored during Cauchy searches 23 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 4.30604e-05 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 : 8.488289e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.918073 - variance bounds : 0.08488289 8.623877 - best initial criterion value(s) : -846848.8 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 8.4685e+05 |proj g|= 7.9752 At iterate 1 f = 102.22 |proj g|= 2.6193 At iterate 2 f = 66.647 |proj g|= 0.89099 ys=-8.882e+00 -gs= 2.003e+01, BFGS update SKIPPED At iterate 3 f = 66.502 |proj g|= 0.84787 At iterate 4 f = 66.377 |proj g|= 1.6361 At iterate 5 f = 66.346 |proj g|= 0.3606 At iterate 6 f = 66.344 |proj g|= 0.035594 At iterate 7 f = 66.344 |proj g|= 0.00088334 At iterate 8 f = 66.344 |proj g|= 2.0984e-06 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 2.09842e-06 final function value 66.3444 F = 66.3444 final value 66.344418 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.488289e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.918073 - variance bounds : 0.08488289 8.623877 - best initial criterion value(s) : -846848.8 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 8.4685e+05 |proj g|= 7.9752 At iterate 1 f = 102.22 |proj g|= 2.6193 At iterate 2 f = 66.647 |proj g|= 0.89099 ys=-8.882e+00 -gs= 2.003e+01, BFGS update SKIPPED At iterate 3 f = 66.502 |proj g|= 0.84787 At iterate 4 f = 66.377 |proj g|= 1.6361 At iterate 5 f = 66.346 |proj g|= 0.3606 At iterate 6 f = 66.344 |proj g|= 0.035594 At iterate 7 f = 66.344 |proj g|= 0.00088334 At iterate 8 f = 66.344 |proj g|= 2.0984e-06 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 2.09842e-06 final function value 66.3444 F = 66.3444 final value 66.344418 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.488289e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.918073 - variance bounds : 0.08488289 8.623877 - best initial criterion value(s) : -76.71526 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 76.715 |proj g|= 7.8323 At iterate 1 f = 71.829 |proj g|= 1.4521 At iterate 2 f = 67.636 |proj g|= 1.0575 At iterate 3 f = 66.589 |proj g|= 5.041 At iterate 4 f = 66.349 |proj g|= 0.56786 At iterate 5 f = 66.345 |proj g|= 0.14089 At iterate 6 f = 66.344 |proj g|= 0.0056589 At iterate 7 f = 66.344 |proj g|= 5.3663e-05 At iterate 8 f = 66.344 |proj g|= 2.0199e-08 iterations 8 function evaluations 11 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 1 norm of the final projected gradient 2.01995e-08 final function value 66.3444 F = 66.3444 final value 66.344418 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.488289e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.918073 - variance bounds : 0.08488289 8.623877 - best initial criterion value(s) : -76.71526 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 76.715 |proj g|= 7.8323 At iterate 1 f = 71.829 |proj g|= 1.4521 At iterate 2 f = 67.636 |proj g|= 1.0575 At iterate 3 f = 66.589 |proj g|= 5.041 At iterate 4 f = 66.349 |proj g|= 0.56786 At iterate 5 f = 66.345 |proj g|= 0.14089 At iterate 6 f = 66.344 |proj g|= 0.0056589 At iterate 7 f = 66.344 |proj g|= 5.3663e-05 At iterate 8 f = 66.344 |proj g|= 2.0199e-08 iterations 8 function evaluations 11 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 1 norm of the final projected gradient 2.01995e-08 final function value 66.3444 F = 66.3444 final value 66.344418 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.488289e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.918073 - variance bounds : 0.08488289 8.623877 - best initial criterion value(s) : -62143.78 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 62144 |proj g|= 7.6846 At iterate 1 f = 102.22 |proj g|= 2.6193 At iterate 2 f = 66.737 |proj g|= 0.91374 ys=-1.191e+01 -gs= 1.997e+01, BFGS update SKIPPED At iterate 3 f = 66.628 |proj g|= 0.88593 At iterate 4 f = 66.439 |proj g|= 2.9258 At iterate 5 f = 66.354 |proj g|= 0.77042 At iterate 6 f = 66.345 |proj g|= 0.13373 At iterate 7 f = 66.344 |proj g|= 0.0077361 At iterate 8 f = 66.344 |proj g|= 6.9578e-05 At iterate 9 f = 66.344 |proj g|= 3.5798e-08 iterations 9 function evaluations 31 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 3.57977e-08 final function value 66.3444 F = 66.3444 final value 66.344418 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.488289e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.918073 - variance bounds : 0.08488289 8.623877 - best initial criterion value(s) : -1187.155 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 1187.2 |proj g|= 7.8061 At iterate 1 f = 102.22 |proj g|= 2.6193 At iterate 2 f = 66.777 |proj g|= 7.1148 At iterate 3 f = 66.388 |proj g|= 0.79793 At iterate 4 f = 66.349 |proj g|= 0.54933 At iterate 5 f = 66.344 |proj g|= 0.07312 At iterate 6 f = 66.344 |proj g|= 0.0027843 At iterate 7 f = 66.344 |proj g|= 1.347e-05 iterations 7 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 1.34696e-05 final function value 66.3444 F = 66.3444 final value 66.344418 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) : -10061.85 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 10062 |proj g|= 213.09 At iterate 1 f = 376.42 |proj g|= 0.16731 At iterate 2 f = 319.31 |proj g|= 0.4023 At iterate 3 f = 318.25 |proj g|= 0.044523 At iterate 4 f = 318.22 |proj g|= 0.015601 At iterate 5 f = 318.22 |proj g|= 0.0010528 At iterate 6 f = 318.22 |proj g|= 2.2912e-05 At iterate 7 f = 318.22 |proj g|= 3.2754e-08 iterations 7 function evaluations 25 segments explored during Cauchy searches 9 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 3.27543e-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 : 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) : -564.9758 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 564.98 |proj g|= 5.0959 At iterate 1 f = 330.71 |proj g|= 0.36376 At iterate 2 f = 319.32 |proj g|= 0.23099 At iterate 3 f = 318.3 |proj g|= 0.076763 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 318.27 |proj g|= 0.063294 At iterate 5 f = 318.22 |proj g|= 0.0086742 At iterate 6 f = 318.22 |proj g|= 0.00082508 At iterate 7 f = 318.22 |proj g|= 9.6074e-06 At iterate 8 f = 318.22 |proj g|= 1.0798e-08 iterations 8 function evaluations 19 segments explored during Cauchy searches 12 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 1.0798e-08 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 28.25 2.43 30.15