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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 20.190350 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.190350 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.391705 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.391705 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.741900 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.741900 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 19.808330 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.808330 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.808330 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.808330 converged # weights: 6 (5 variable) initial value 10.550507 iter 10 value 9.296525 final value 9.296524 converged # weights: 6 (5 variable) initial value 10.550507 iter 10 value 9.296525 final value 9.296524 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.808330 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.808330 converged # weights: 9 (4 variable) initial value 32.958369 final value 27.632069 converged # weights: 9 (4 variable) initial value 32.958369 final value 27.632069 converged # weights: 9 (4 variable) initial value 32.958369 final value 30.921052 converged # weights: 9 (4 variable) initial value 32.958369 final value 30.921052 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 32.036573 final value 32.036552 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 32.036573 final value 32.036552 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 23.514276 final value 23.481804 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.514276 final value 23.481804 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.514276 final value 23.481804 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.514276 final value 23.481804 converged # weights: 18 (10 variable) initial value 14.634762 iter 10 value 9.915967 iter 20 value 9.845569 iter 30 value 9.845548 final value 9.845548 converged # weights: 18 (10 variable) initial value 14.634762 iter 10 value 9.915967 iter 20 value 9.845569 iter 30 value 9.845548 final value 9.845548 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.514276 final value 23.481804 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.514276 final value 23.481804 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 : 8.543499e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.896718 - variance bounds : 0.08511851 8.543499 - best initial criterion value(s) : -1314.99 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 1315 |proj g|= 7.8226 At iterate 1 f = 102.03 |proj g|= 2.6394 At iterate 2 f = 68.199 |proj g|= 1.154 ys=-2.851e+01 -gs= 1.928e+01, BFGS update SKIPPED At iterate 3 f = 67.448 |proj g|= 1.0312 At iterate 4 f = 66.537 |proj g|= 0.79526 At iterate 5 f = 66.507 |proj g|= 0.12731 At iterate 6 f = 66.507 |proj g|= 0.013608 At iterate 7 f = 66.506 |proj g|= 0.00011716 At iterate 8 f = 66.506 |proj g|= 1.0666e-07 iterations 8 function evaluations 23 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.06662e-07 final function value 66.5065 F = 66.5065 final value 66.506498 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.543499e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.896718 - variance bounds : 0.08511851 8.543499 - best initial criterion value(s) : -1314.99 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 1315 |proj g|= 7.8226 At iterate 1 f = 102.03 |proj g|= 2.6394 At iterate 2 f = 68.199 |proj g|= 1.154 ys=-2.851e+01 -gs= 1.928e+01, BFGS update SKIPPED At iterate 3 f = 67.448 |proj g|= 1.0312 At iterate 4 f = 66.537 |proj g|= 0.79526 At iterate 5 f = 66.507 |proj g|= 0.12731 At iterate 6 f = 66.507 |proj g|= 0.013608 At iterate 7 f = 66.506 |proj g|= 0.00011716 At iterate 8 f = 66.506 |proj g|= 1.0666e-07 iterations 8 function evaluations 23 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.06662e-07 final function value 66.5065 F = 66.5065 final value 66.506498 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.543499e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.896718 - variance bounds : 0.08511851 8.543499 - best initial criterion value(s) : -2367.911 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2367.9 |proj g|= 7.9027 At iterate 1 f = 102.03 |proj g|= 2.6394 At iterate 2 f = 66.563 |proj g|= 0.8113 At iterate 3 f = 66.507 |proj g|= 0.18517 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 66.507 |proj g|= 0.013768 At iterate 5 f = 66.506 |proj g|= 0.00017367 At iterate 6 f = 66.506 |proj g|= 1.6034e-07 iterations 6 function evaluations 21 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.60342e-07 final function value 66.5065 F = 66.5065 final value 66.506498 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.543499e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.896718 - variance bounds : 0.08511851 8.543499 - best initial criterion value(s) : -2367.911 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2367.9 |proj g|= 7.9027 At iterate 1 f = 102.03 |proj g|= 2.6394 At iterate 2 f = 66.563 |proj g|= 0.8113 At iterate 3 f = 66.507 |proj g|= 0.18517 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 66.507 |proj g|= 0.013768 At iterate 5 f = 66.506 |proj g|= 0.00017367 At iterate 6 f = 66.506 |proj g|= 1.6034e-07 iterations 6 function evaluations 21 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.60342e-07 final function value 66.5065 F = 66.5065 final value 66.506498 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.543499e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.896718 - variance bounds : 0.08511851 8.543499 - best initial criterion value(s) : -76092.88 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 76093 |proj g|= 8.0255 At iterate 1 f = 102.03 |proj g|= 2.6394 At iterate 2 f = 67.903 |proj g|= 7.9335 At iterate 3 f = 66.847 |proj g|= 0.907 At iterate 4 f = 66.6 |proj g|= 0.82872 At iterate 5 f = 66.527 |proj g|= 1.2669 At iterate 6 f = 66.507 |proj g|= 0.21559 At iterate 7 f = 66.507 |proj g|= 0.016842 At iterate 8 f = 66.506 |proj g|= 0.00024804 At iterate 9 f = 66.506 |proj g|= 2.8021e-07 iterations 9 function evaluations 26 segments explored during Cauchy searches 12 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 2.80205e-07 final function value 66.5065 F = 66.5065 final value 66.506498 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.543499e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.896718 - variance bounds : 0.08511851 8.543499 - best initial criterion value(s) : -14633.34 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 14633 |proj g|= 8.0068 At iterate 1 f = 102.03 |proj g|= 2.6394 At iterate 2 f = 66.845 |proj g|= 0.9066 ys=-9.717e+00 -gs= 1.993e+01, BFGS update SKIPPED At iterate 3 f = 66.751 |proj g|= 0.88104 At iterate 4 f = 66.571 |proj g|= 2.3516 At iterate 5 f = 66.512 |proj g|= 0.62124 At iterate 6 f = 66.507 |proj g|= 0.085967 At iterate 7 f = 66.506 |proj g|= 0.0038034 At iterate 8 f = 66.506 |proj g|= 2.2052e-05 At iterate 9 f = 66.506 |proj g|= 5.6161e-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 5.6161e-09 final function value 66.5065 F = 66.5065 final value 66.506498 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) : -280.8626 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 280.86 |proj g|= 1.6091 At iterate 1 f = 252.6 |proj g|= 17.172 At iterate 2 f = 248.46 |proj g|= 9.5208 At iterate 3 f = 244.09 |proj g|= 1.7238 At iterate 4 f = 244.04 |proj g|= 0.1699 At iterate 5 f = 244.03 |proj g|= 0.40736 At iterate 6 f = 244.03 |proj g|= 0.3038 At iterate 7 f = 244.03 |proj g|= 0.2134 At iterate 8 f = 244.03 |proj g|= 0.19948 At iterate 9 f = 244.03 |proj g|= 0.96849 At iterate 10 f = 244.03 |proj g|= 1.7197 At iterate 11 f = 244.02 |proj g|= 1.7198 At iterate 12 f = 244.02 |proj g|= 1.7199 At iterate 13 f = 244 |proj g|= 1.7199 At iterate 14 f = 243.97 |proj g|= 2.5922 At iterate 15 f = 243.89 |proj g|= 4.1024 At iterate 16 f = 243.87 |proj g|= 4.624 At iterate 17 f = 243.7 |proj g|= 5.8005 At iterate 18 f = 243.34 |proj g|= 2.9046 At iterate 19 f = 243.27 |proj g|= 0.33207 At iterate 20 f = 243.26 |proj g|= 0.081819 At iterate 21 f = 243.26 |proj g|= 0.048443 At iterate 22 f = 243.26 |proj g|= 0.0020587 iterations 22 function evaluations 28 segments explored during Cauchy searches 24 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.00205868 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) : -964.539 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 964.54 |proj g|= 13.536 At iterate 1 f = 328.26 |proj g|= 0.36749 At iterate 2 f = 319.59 |proj g|= 0.24897 At iterate 3 f = 318.48 |proj g|= 0.17244 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 318.36 |proj g|= 0.12001 At iterate 5 f = 318.23 |proj g|= 0.018817 At iterate 6 f = 318.22 |proj g|= 0.0024912 At iterate 7 f = 318.22 |proj g|= 6.1539e-05 At iterate 8 f = 318.22 |proj g|= 2.0764e-07 iterations 8 function evaluations 16 segments explored during Cauchy searches 12 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 2.07635e-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) : -310.1606 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 310.16 |proj g|= 10.795 At iterate 1 f = 258.07 |proj g|= 3.2814 At iterate 2 f = 256.83 |proj g|= 2.2353 At iterate 3 f = 254.95 |proj g|= 2.078 At iterate 4 f = 254.21 |proj g|= 2.006 At iterate 5 f = 253.22 |proj g|= 1.5775 At iterate 6 f = 252.85 |proj g|= 0.19884 At iterate 7 f = 252.85 |proj g|= 0.20151 At iterate 8 f = 252.85 |proj g|= 0.2016 At iterate 9 f = 252.85 |proj g|= 0.20048 At iterate 10 f = 252.84 |proj g|= 0.19762 At iterate 11 f = 252.82 |proj g|= 0.35824 At iterate 12 f = 252.76 |proj g|= 0.66226 At iterate 13 f = 252.6 |proj g|= 1.1567 At iterate 14 f = 252.18 |proj g|= 1.6831 At iterate 15 f = 251.16 |proj g|= 1.5429 At iterate 16 f = 249.76 |proj g|= 1.277 At iterate 17 f = 247.55 |proj g|= 0.83253 At iterate 18 f = 245.78 |proj g|= 6.2129 At iterate 19 f = 244.97 |proj g|= 14.629 At iterate 20 f = 244.51 |proj g|= 12.683 At iterate 21 f = 244.25 |proj g|= 10.537 At iterate 22 f = 243.46 |proj g|= 1.8232 At iterate 23 f = 243.27 |proj g|= 0.23152 At iterate 24 f = 243.26 |proj g|= 0.15256 At iterate 25 f = 243.26 |proj g|= 0.077104 At iterate 26 f = 243.26 |proj g|= 0.00096202 At iterate 27 f = 243.26 |proj g|= 0.00050385 iterations 27 function evaluations 31 segments explored during Cauchy searches 28 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.00050385 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) : -315.5626 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 315.56 |proj g|= 42.723 At iterate 1 f = 260.2 |proj g|= 18.807 At iterate 2 f = 250.73 |proj g|= 1.6985 At iterate 3 f = 250.69 |proj g|= 0.44909 At iterate 4 f = 250.68 |proj g|= 0.61768 At iterate 5 f = 250.68 |proj g|= 0.22112 At iterate 6 f = 250.68 |proj g|= 0.22008 At iterate 7 f = 250.68 |proj g|= 0.22223 At iterate 8 f = 250.68 |proj g|= 0.44494 At iterate 9 f = 250.68 |proj g|= 0.79159 At iterate 10 f = 250.67 |proj g|= 1.4015 At iterate 11 f = 250.64 |proj g|= 1.754 At iterate 12 f = 250.56 |proj g|= 1.7757 At iterate 13 f = 250.36 |proj g|= 1.8103 At iterate 14 f = 250.15 |proj g|= 1.8099 At iterate 15 f = 249.42 |proj g|= 1.8216 At iterate 16 f = 246.72 |proj g|= 1.7735 At iterate 17 f = 245.12 |proj g|= 1.744 At iterate 18 f = 244.12 |proj g|= 1.7431 At iterate 19 f = 243.52 |proj g|= 1.7399 At iterate 20 f = 243.27 |proj g|= 0.26828 At iterate 21 f = 243.26 |proj g|= 0.15256 At iterate 22 f = 243.26 |proj g|= 0.29795 At iterate 23 f = 243.26 |proj g|= 0.00092849 At iterate 24 f = 243.26 |proj g|= 0.00098353 iterations 24 function evaluations 34 segments explored during Cauchy searches 25 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.000983531 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 : 1.004006e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.965843 - variance bounds : 0.1004006 10.22279 - best initial criterion value(s) : -151.1834 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 151.18 |proj g|= 8.9354 At iterate 1 f = 106.47 |proj g|= 2.2101 At iterate 2 f = 71.903 |proj g|= 1.2948 ys=-2.712e+01 -gs= 1.951e+01, BFGS update SKIPPED At iterate 3 f = 71.354 |proj g|= 1.1837 At iterate 4 f = 70.569 |proj g|= 0.9306 At iterate 5 f = 70.558 |proj g|= 0.95051 At iterate 6 f = 70.542 |proj g|= 0.084564 At iterate 7 f = 70.542 |proj g|= 0.0058365 At iterate 8 f = 70.542 |proj g|= 3.9199e-05 At iterate 9 f = 70.542 |proj g|= 1.8019e-08 iterations 9 function evaluations 24 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 1.80187e-08 final function value 70.5418 F = 70.5418 final value 70.541816 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.004006e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.965843 - variance bounds : 0.1004006 10.22279 - best initial criterion value(s) : -151.1834 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 151.18 |proj g|= 8.9354 At iterate 1 f = 106.47 |proj g|= 2.2101 At iterate 2 f = 71.903 |proj g|= 1.2948 ys=-2.712e+01 -gs= 1.951e+01, BFGS update SKIPPED At iterate 3 f = 71.354 |proj g|= 1.1837 At iterate 4 f = 70.569 |proj g|= 0.9306 At iterate 5 f = 70.558 |proj g|= 0.95051 At iterate 6 f = 70.542 |proj g|= 0.084564 At iterate 7 f = 70.542 |proj g|= 0.0058365 At iterate 8 f = 70.542 |proj g|= 3.9199e-05 At iterate 9 f = 70.542 |proj g|= 1.8019e-08 iterations 9 function evaluations 24 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 1.80187e-08 final function value 70.5418 F = 70.5418 final value 70.541816 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.004006e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.965843 - variance bounds : 0.1004006 10.22279 - best initial criterion value(s) : -329895.8 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 3.299e+05 |proj g|= 9.3575 At iterate 1 f = 106.47 |proj g|= 2.2101 At iterate 2 f = 70.74 |proj g|= 3.774 At iterate 3 f = 70.548 |proj g|= 0.53497 At iterate 4 f = 70.542 |proj g|= 0.12209 At iterate 5 f = 70.542 |proj g|= 0.005498 At iterate 6 f = 70.542 |proj g|= 5.3453e-05 At iterate 7 f = 70.542 |proj g|= 2.312e-08 iterations 7 function evaluations 24 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 2.31205e-08 final function value 70.5418 F = 70.5418 final value 70.541816 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.004006e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.965843 - variance bounds : 0.1004006 10.22279 - best initial criterion value(s) : -329895.8 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 3.299e+05 |proj g|= 9.3575 At iterate 1 f = 106.47 |proj g|= 2.2101 At iterate 2 f = 70.74 |proj g|= 3.774 At iterate 3 f = 70.548 |proj g|= 0.53497 At iterate 4 f = 70.542 |proj g|= 0.12209 At iterate 5 f = 70.542 |proj g|= 0.005498 At iterate 6 f = 70.542 |proj g|= 5.3453e-05 At iterate 7 f = 70.542 |proj g|= 2.312e-08 iterations 7 function evaluations 24 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 2.31205e-08 final function value 70.5418 F = 70.5418 final value 70.541816 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.004006e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.965843 - variance bounds : 0.1004006 10.22279 - best initial criterion value(s) : -138.966 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 138.97 |proj g|= 9.235 At iterate 1 f = 106.47 |proj g|= 2.2101 At iterate 2 f = 72.648 |proj g|= 1.4283 ys=-3.145e+01 -gs= 1.922e+01, BFGS update SKIPPED At iterate 3 f = 71.825 |proj g|= 1.2799 At iterate 4 f = 70.542 |proj g|= 0.13052 At iterate 5 f = 70.542 |proj g|= 0.0013305 At iterate 6 f = 70.542 |proj g|= 1.3849e-05 iterations 6 function evaluations 23 segments explored during Cauchy searches 9 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.38491e-05 final function value 70.5418 F = 70.5418 final value 70.541816 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.004006e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.965843 - variance bounds : 0.1004006 10.22279 - best initial criterion value(s) : -1012.365 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 1012.4 |proj g|= 9.522 At iterate 1 f = 106.47 |proj g|= 2.2101 At iterate 2 f = 70.708 |proj g|= 1.0064 ys=-2.711e+00 -gs= 2.015e+01, BFGS update SKIPPED At iterate 3 f = 70.651 |proj g|= 0.98172 At iterate 4 f = 70.552 |proj g|= 0.75513 At iterate 5 f = 70.542 |proj g|= 0.14538 At iterate 6 f = 70.542 |proj g|= 0.0081591 At iterate 7 f = 70.542 |proj g|= 9.4802e-05 At iterate 8 f = 70.542 |proj g|= 6.0933e-08 iterations 8 function evaluations 21 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 6.09331e-08 final function value 70.5418 F = 70.5418 final value 70.541816 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) : -250.4086 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 250.41 |proj g|= 11.575 At iterate 1 f = 245.62 |proj g|= 1.7433 At iterate 2 f = 244.94 |proj g|= 1.7311 At iterate 3 f = 244.5 |proj g|= 1.5503 At iterate 4 f = 244.35 |proj g|= 0.2326 At iterate 5 f = 244.35 |proj g|= 0.38271 At iterate 6 f = 244.35 |proj g|= 0.34016 At iterate 7 f = 244.34 |proj g|= 0.48368 At iterate 8 f = 244.3 |proj g|= 1.3361 At iterate 9 f = 244.15 |proj g|= 3.1858 At iterate 10 f = 243.87 |proj g|= 5.0461 At iterate 11 f = 243.84 |proj g|= 6.2272 At iterate 12 f = 243.54 |proj g|= 5.6446 At iterate 13 f = 243.33 |proj g|= 2.6447 At iterate 14 f = 243.27 |proj g|= 0.81639 At iterate 15 f = 243.26 |proj g|= 0.03066 At iterate 16 f = 243.26 |proj g|= 0.030937 At iterate 17 f = 243.26 |proj g|= 0.0051088 iterations 17 function evaluations 26 segments explored during Cauchy searches 18 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.00510878 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) : -254.5814 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 254.58 |proj g|= 7.8551 At iterate 1 f = 249.65 |proj g|= 1.8605 At iterate 2 f = 249.07 |proj g|= 1.8542 At iterate 3 f = 248.35 |proj g|= 1.7829 At iterate 4 f = 247.8 |proj g|= 1.6496 At iterate 5 f = 247.76 |proj g|= 1.7053 At iterate 6 f = 247.75 |proj g|= 0.26106 At iterate 7 f = 247.75 |proj g|= 0.24003 At iterate 8 f = 247.75 |proj g|= 0.23963 At iterate 9 f = 247.75 |proj g|= 0.23871 At iterate 10 f = 247.75 |proj g|= 0.23738 At iterate 11 f = 247.75 |proj g|= 0.31468 At iterate 12 f = 247.75 |proj g|= 0.55195 At iterate 13 f = 247.74 |proj g|= 0.96558 At iterate 14 f = 247.73 |proj g|= 1.6405 At iterate 15 f = 247.7 |proj g|= 1.6585 At iterate 16 f = 247.68 |proj g|= 1.6683 At iterate 17 f = 247.59 |proj g|= 1.6774 At iterate 18 f = 247.24 |proj g|= 1.7127 At iterate 19 f = 246.53 |proj g|= 1.7262 At iterate 20 f = 245.16 |proj g|= 1.7514 At iterate 21 f = 244.2 |proj g|= 1.744 At iterate 22 f = 243.41 |proj g|= 1.736 At iterate 23 f = 243.27 |proj g|= 1.7292 At iterate 24 f = 243.27 |proj g|= 0.47623 At iterate 25 f = 243.26 |proj g|= 0.15257 At iterate 26 f = 243.26 |proj g|= 0.0078955 At iterate 27 f = 243.26 |proj g|= 0.00052162 iterations 27 function evaluations 34 segments explored during Cauchy searches 28 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.000521617 final function value 243.264 F = 243.264 final value 243.264413 converged [ FAIL 0 | WARN 0 | SKIP 4 | PASS 549 ] ══ 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 549 ] > > proc.time() user system elapsed 47.04 3.21 50.17