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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 20.727699 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.727699 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.391705 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.391705 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.074510 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.074510 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.190350 converged # weights: 3 (2 variable) initial value 20.794415 final value 20.190350 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.073580 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.073580 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.073580 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.073580 converged # weights: 6 (5 variable) initial value 10.642033 iter 10 value 8.931420 final value 8.931417 converged # weights: 6 (5 variable) initial value 10.642033 iter 10 value 8.931420 final value 8.931417 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.073580 converged # weights: 6 (5 variable) initial value 20.794415 final value 19.073580 converged # weights: 9 (4 variable) initial value 32.958369 final value 31.312711 converged # weights: 9 (4 variable) initial value 32.958369 final value 31.312711 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 29.298529 final value 29.298496 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 29.298529 final value 29.298496 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 27.148934 final value 27.148924 converged # weights: 9 (4 variable) initial value 32.958369 iter 10 value 27.148934 final value 27.148924 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.727070 iter 20 value 23.627332 final value 23.627268 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.727070 iter 20 value 23.627332 final value 23.627268 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.727070 iter 20 value 23.627332 final value 23.627268 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.727070 iter 20 value 23.627332 final value 23.627268 converged # weights: 18 (10 variable) initial value 15.606263 iter 10 value 11.044523 iter 20 value 10.976210 iter 30 value 10.975506 final value 10.975505 converged # weights: 18 (10 variable) initial value 15.606263 iter 10 value 11.044523 iter 20 value 10.976210 iter 30 value 10.975506 final value 10.975505 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.727070 iter 20 value 23.627332 final value 23.627268 converged # weights: 18 (10 variable) initial value 32.958369 iter 10 value 23.727070 iter 20 value 23.627332 final value 23.627268 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 : 8.317792e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.949717 - variance bounds : 0.08317792 8.41726 - best initial criterion value(s) : -3739.061 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 3739.1 |proj g|= 7.5877 At iterate 1 f = 101.63 |proj g|= 2.6825 At iterate 2 f = 67.233 |proj g|= 1.078 ys=-2.709e+01 -gs= 1.946e+01, BFGS update SKIPPED At iterate 3 f = 66.695 |proj g|= 0.98889 At iterate 4 f = 65.868 |proj g|= 0.77355 At iterate 5 f = 65.837 |proj g|= 0.12053 At iterate 6 f = 65.837 |proj g|= 0.012749 At iterate 7 f = 65.837 |proj g|= 0.0001011 At iterate 8 f = 65.837 |proj g|= 8.396e-08 iterations 8 function evaluations 32 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 8.39596e-08 final function value 65.8372 F = 65.8372 final value 65.837153 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.317792e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.949717 - variance bounds : 0.08317792 8.41726 - best initial criterion value(s) : -3739.061 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 3739.1 |proj g|= 7.5877 At iterate 1 f = 101.63 |proj g|= 2.6825 At iterate 2 f = 67.233 |proj g|= 1.078 ys=-2.709e+01 -gs= 1.946e+01, BFGS update SKIPPED At iterate 3 f = 66.695 |proj g|= 0.98889 At iterate 4 f = 65.868 |proj g|= 0.77355 At iterate 5 f = 65.837 |proj g|= 0.12053 At iterate 6 f = 65.837 |proj g|= 0.012749 At iterate 7 f = 65.837 |proj g|= 0.0001011 At iterate 8 f = 65.837 |proj g|= 8.396e-08 iterations 8 function evaluations 32 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 8.39596e-08 final function value 65.8372 F = 65.8372 final value 65.837153 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.317792e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.949717 - variance bounds : 0.08317792 8.41726 - best initial criterion value(s) : -2582989 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2.583e+06 |proj g|= 7.2363 At iterate 1 f = 101.63 |proj g|= 2.6825 At iterate 2 f = 67.144 |proj g|= 1.064 ys=-2.636e+01 -gs= 1.950e+01, BFGS update SKIPPED At iterate 3 f = 66.592 |proj g|= 0.97007 At iterate 4 f = 65.859 |proj g|= 0.76732 At iterate 5 f = 65.849 |proj g|= 0.97673 At iterate 6 f = 65.837 |proj g|= 0.079735 At iterate 7 f = 65.837 |proj g|= 0.0047363 At iterate 8 f = 65.837 |proj g|= 2.4798e-05 At iterate 9 f = 65.837 |proj g|= 7.6619e-09 iterations 9 function evaluations 36 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 7.66192e-09 final function value 65.8372 F = 65.8372 final value 65.837153 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.317792e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.949717 - variance bounds : 0.08317792 8.41726 - best initial criterion value(s) : -2582989 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2.583e+06 |proj g|= 7.2363 At iterate 1 f = 101.63 |proj g|= 2.6825 At iterate 2 f = 67.144 |proj g|= 1.064 ys=-2.636e+01 -gs= 1.950e+01, BFGS update SKIPPED At iterate 3 f = 66.592 |proj g|= 0.97007 At iterate 4 f = 65.859 |proj g|= 0.76732 At iterate 5 f = 65.849 |proj g|= 0.97673 At iterate 6 f = 65.837 |proj g|= 0.079735 At iterate 7 f = 65.837 |proj g|= 0.0047363 At iterate 8 f = 65.837 |proj g|= 2.4798e-05 At iterate 9 f = 65.837 |proj g|= 7.6619e-09 iterations 9 function evaluations 36 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 7.66192e-09 final function value 65.8372 F = 65.8372 final value 65.837153 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.317792e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.949717 - variance bounds : 0.08317792 8.41726 - best initial criterion value(s) : -9650.725 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 9650.7 |proj g|= 7.6181 At iterate 1 f = 101.63 |proj g|= 2.6825 At iterate 2 f = 65.982 |proj g|= 3.8097 At iterate 3 f = 65.839 |proj g|= 0.39877 At iterate 4 f = 65.837 |proj g|= 0.078216 At iterate 5 f = 65.837 |proj g|= 0.0021164 At iterate 6 f = 65.837 |proj g|= 1.0862e-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 1.08624e-05 final function value 65.8372 F = 65.8372 final value 65.837153 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 8.317792e-09 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.949717 - variance bounds : 0.08317792 8.41726 - best initial criterion value(s) : -99828.59 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 99829 |proj g|= 7.6392 At iterate 1 f = 101.63 |proj g|= 2.6825 At iterate 2 f = 65.86 |proj g|= 0.76778 At iterate 3 f = 65.837 |proj g|= 0.10928 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 65.837 |proj g|= 0.0073206 At iterate 5 f = 65.837 |proj g|= 5.2669e-05 At iterate 6 f = 65.837 |proj g|= 2.5158e-08 iterations 6 function evaluations 23 segments explored during Cauchy searches 11 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 2.51584e-08 final function value 65.8372 F = 65.8372 final value 65.837153 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) : -824.7499 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 824.75 |proj g|= 15.894 At iterate 1 f = 323.87 |proj g|= 0.35382 At iterate 2 f = 318.47 |proj g|= 0.12858 At iterate 3 f = 318.22 |proj g|= 0.014093 At iterate 4 f = 318.22 |proj g|= 0.0032792 At iterate 5 f = 318.22 |proj g|= 6.4049e-05 At iterate 6 f = 318.22 |proj g|= 2.8415e-07 iterations 6 function evaluations 17 segments explored during Cauchy searches 8 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 2.84148e-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) : -331.8992 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 331.9 |proj g|= 44.226 At iterate 1 f = 318.55 |proj g|= 44.168 At iterate 2 f = 317.32 |proj g|= 1.7351 At iterate 3 f = 316.94 |proj g|= 1.7283 At iterate 4 f = 315.12 |proj g|= 44.157 At iterate 5 f = 311.09 |proj g|= 44.171 At iterate 6 f = 303.37 |proj g|= 44.183 At iterate 7 f = 297.34 |proj g|= 44.177 At iterate 8 f = 288.16 |proj g|= 44.11 At iterate 9 f = 284.05 |proj g|= 44.048 At iterate 10 f = 278.22 |proj g|= 3.397 At iterate 11 f = 268.59 |proj g|= 43.743 At iterate 12 f = 267.86 |proj g|= 43.722 At iterate 13 f = 263.68 |proj g|= 39.749 At iterate 14 f = 257.74 |proj g|= 32.434 At iterate 15 f = 247.74 |proj g|= 5.1224 At iterate 16 f = 247.05 |proj g|= 0.6707 At iterate 17 f = 247.05 |proj g|= 0.24014 At iterate 18 f = 247.05 |proj g|= 0.24105 At iterate 19 f = 247.05 |proj g|= 0.70222 At iterate 20 f = 247.04 |proj g|= 1.4913 At iterate 21 f = 247.04 |proj g|= 1.7053 At iterate 22 f = 247.02 |proj g|= 1.7049 At iterate 23 f = 247.02 |proj g|= 1.8989 At iterate 24 f = 246.98 |proj g|= 2.8033 At iterate 25 f = 246.75 |proj g|= 4.6103 At iterate 26 f = 245.46 |proj g|= 9.625 At iterate 27 f = 244.43 |proj g|= 10.507 At iterate 28 f = 243.85 |proj g|= 4.4068 At iterate 29 f = 243.75 |proj g|= 1.403 At iterate 30 f = 243.31 |proj g|= 1.7319 At iterate 31 f = 243.26 |proj g|= 0.92199 At iterate 32 f = 243.26 |proj g|= 0.15254 At iterate 33 f = 243.26 |proj g|= 0.043201 At iterate 34 f = 243.26 |proj g|= 0.0034936 iterations 34 function evaluations 46 segments explored during Cauchy searches 36 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.00349364 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) : -490.3505 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 490.35 |proj g|= 4.8177 At iterate 1 f = 324.91 |proj g|= 0.36081 At iterate 2 f = 318.79 |proj g|= 0.18057 At iterate 3 f = 318.23 |proj g|= 0.018527 At iterate 4 f = 318.22 |proj g|= 0.0070173 At iterate 5 f = 318.22 |proj g|= 0.00018042 At iterate 6 f = 318.22 |proj g|= 1.7024e-06 iterations 6 function evaluations 14 segments explored during Cauchy searches 8 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 1.70241e-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) : -320.4305 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 320.43 |proj g|= 2.9104 At iterate 1 f = 261.82 |proj g|= 2.5699 At iterate 2 f = 261.36 |proj g|= 2.5551 At iterate 3 f = 261.15 |proj g|= 2.5349 At iterate 4 f = 261.05 |proj g|= 2.5224 At iterate 5 f = 260.22 |proj g|= 2.4048 At iterate 6 f = 258.17 |proj g|= 2.9269 At iterate 7 f = 256.87 |proj g|= 9.6135 At iterate 8 f = 253.56 |proj g|= 6.0863 At iterate 9 f = 252.75 |proj g|= 1.8389 At iterate 10 f = 252.66 |proj g|= 1.1121 At iterate 11 f = 252.66 |proj g|= 0.29918 At iterate 12 f = 252.66 |proj g|= 0.20327 At iterate 13 f = 252.65 |proj g|= 0.20058 At iterate 14 f = 252.65 |proj g|= 0.22786 At iterate 15 f = 252.64 |proj g|= 0.4172 At iterate 16 f = 252.61 |proj g|= 0.68925 At iterate 17 f = 252.54 |proj g|= 1.0414 At iterate 18 f = 252.34 |proj g|= 1.2523 At iterate 19 f = 251.85 |proj g|= 0.62299 At iterate 20 f = 250.84 |proj g|= 2.2612 At iterate 21 f = 249.32 |proj g|= 7.9577 At iterate 22 f = 248.92 |proj g|= 25.469 At iterate 23 f = 247.8 |proj g|= 20.758 At iterate 24 f = 247.56 |proj g|= 17.561 At iterate 25 f = 245.64 |proj g|= 15.245 At iterate 26 f = 243.6 |proj g|= 7.1287 At iterate 27 f = 243.28 |proj g|= 1.0398 At iterate 28 f = 243.27 |proj g|= 1.728 At iterate 29 f = 243.26 |proj g|= 0.15289 At iterate 30 f = 243.26 |proj g|= 0.24615 At iterate 31 f = 243.26 |proj g|= 0.056983 At iterate 32 f = 243.26 |proj g|= 0.0029003 At iterate 33 f = 243.26 |proj g|= 1.1645e-05 iterations 33 function evaluations 39 segments explored during Cauchy searches 34 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 1.16455e-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 : 1.13036e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.973471 - variance bounds : 0.1079209 11.3036 - best initial criterion value(s) : -322202.6 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 3.222e+05 |proj g|= 10.687 At iterate 1 f = 109.02 |proj g|= 1.9949 At iterate 2 f = 73.906 |proj g|= 5.0971 At iterate 3 f = 73.539 |proj g|= 1.0591 At iterate 4 f = 73.508 |proj g|= 0.35038 At iterate 5 f = 73.505 |proj g|= 0.04022 At iterate 6 f = 73.505 |proj g|= 0.0012936 At iterate 7 f = 73.505 |proj g|= 4.591e-06 iterations 7 function evaluations 25 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 4.59104e-06 final function value 73.5053 F = 73.5053 final value 73.505265 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.13036e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.973471 - variance bounds : 0.1079209 11.3036 - best initial criterion value(s) : -322202.6 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 3.222e+05 |proj g|= 10.687 At iterate 1 f = 109.02 |proj g|= 1.9949 At iterate 2 f = 73.906 |proj g|= 5.0971 At iterate 3 f = 73.539 |proj g|= 1.0591 At iterate 4 f = 73.508 |proj g|= 0.35038 At iterate 5 f = 73.505 |proj g|= 0.04022 At iterate 6 f = 73.505 |proj g|= 0.0012936 At iterate 7 f = 73.505 |proj g|= 4.591e-06 iterations 7 function evaluations 25 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 4.59104e-06 final function value 73.5053 F = 73.5053 final value 73.505265 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.13036e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.973471 - variance bounds : 0.1079209 11.3036 - best initial criterion value(s) : -12547.85 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 12548 |proj g|= 10.359 At iterate 1 f = 109.02 |proj g|= 1.9949 At iterate 2 f = 74.772 |proj g|= 1.4424 ys=-2.544e+01 -gs= 1.946e+01, BFGS update SKIPPED At iterate 3 f = 74.234 |proj g|= 1.3166 At iterate 4 f = 73.525 |proj g|= 0.85256 At iterate 5 f = 73.515 |proj g|= 0.65771 At iterate 6 f = 73.505 |proj g|= 0.050968 At iterate 7 f = 73.505 |proj g|= 0.0027855 At iterate 8 f = 73.505 |proj g|= 1.2657e-05 At iterate 9 f = 73.505 |proj g|= 3.1254e-09 iterations 9 function evaluations 34 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 3.12536e-09 final function value 73.5053 F = 73.5053 final value 73.505265 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.13036e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.973471 - variance bounds : 0.1079209 11.3036 - best initial criterion value(s) : -12547.85 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 12548 |proj g|= 10.359 At iterate 1 f = 109.02 |proj g|= 1.9949 At iterate 2 f = 74.772 |proj g|= 1.4424 ys=-2.544e+01 -gs= 1.946e+01, BFGS update SKIPPED At iterate 3 f = 74.234 |proj g|= 1.3166 At iterate 4 f = 73.525 |proj g|= 0.85256 At iterate 5 f = 73.515 |proj g|= 0.65771 At iterate 6 f = 73.505 |proj g|= 0.050968 At iterate 7 f = 73.505 |proj g|= 0.0027855 At iterate 8 f = 73.505 |proj g|= 1.2657e-05 At iterate 9 f = 73.505 |proj g|= 3.1254e-09 iterations 9 function evaluations 34 segments explored during Cauchy searches 12 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 3.12536e-09 final function value 73.5053 F = 73.5053 final value 73.505265 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.13036e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.973471 - variance bounds : 0.1079209 11.3036 - best initial criterion value(s) : -1373816 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 1.3738e+06 |proj g|= 9.7415 At iterate 1 f = 109.02 |proj g|= 1.9949 At iterate 2 f = 74.229 |proj g|= 1.3154 ys=-1.877e+01 -gs= 1.971e+01, BFGS update SKIPPED At iterate 3 f = 73.708 |proj g|= 1.1539 At iterate 4 f = 73.515 |proj g|= 0.5976 At iterate 5 f = 73.506 |proj g|= 0.1944 At iterate 6 f = 73.505 |proj g|= 0.010787 At iterate 7 f = 73.505 |proj g|= 0.00018214 At iterate 8 f = 73.505 |proj g|= 1.7434e-07 iterations 8 function evaluations 34 segments explored during Cauchy searches 11 BFGS updates skipped 1 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 1.74337e-07 final function value 73.5053 F = 73.5053 final value 73.505265 converged optimisation start ------------------ * estimation method : MLE * optimisation method : BFGS * analytical gradient : used * trend model : ~1 * covariance model : - type : matern5_2 - nugget : 1.13036e-08 - parameters lower bounds : 1e-10 1e-10 1e-10 - parameters upper bounds : 4 2 1.973471 - variance bounds : 0.1079209 11.3036 - best initial criterion value(s) : -204162.3 N = 4, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 2.0416e+05 |proj g|= 10.688 At iterate 1 f = 109.02 |proj g|= 1.9949 At iterate 2 f = 74.585 |proj g|= 9.658 At iterate 3 f = 73.738 |proj g|= 1.1657 At iterate 4 f = 73.557 |proj g|= 1.0743 At iterate 5 f = 73.512 |proj g|= 0.53276 At iterate 6 f = 73.505 |proj g|= 0.06946 At iterate 7 f = 73.505 |proj g|= 0.0031208 At iterate 8 f = 73.505 |proj g|= 1.9366e-05 At iterate 9 f = 73.505 |proj g|= 5.358e-09 iterations 9 function evaluations 28 segments explored during Cauchy searches 12 BFGS updates skipped 0 active bounds at final generalized Cauchy point 3 norm of the final projected gradient 5.358e-09 final function value 73.5053 F = 73.5053 final value 73.505265 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) : -4394.519 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 4394.5 |proj g|= 130.08 At iterate 1 f = 357.04 |proj g|= 0.24265 At iterate 2 f = 318.55 |proj g|= 0.14459 At iterate 3 f = 318.22 |proj g|= 0.01471 Nonpositive definiteness in Cholesky factorization in formk; refresh the lbfgs memory and restart the iteration. At iterate 4 f = 318.22 |proj g|= 0.011644 At iterate 5 f = 318.22 |proj g|= 0.00024392 At iterate 6 f = 318.22 |proj g|= 3.94e-06 iterations 6 function evaluations 18 segments explored during Cauchy searches 10 BFGS updates skipped 0 active bounds at final generalized Cauchy point 2 norm of the final projected gradient 3.94004e-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) : -264.3123 N = 3, M = 5 machine precision = 2.22045e-16 At X0, 0 variables are exactly at the bounds At iterate 0 f= 264.31 |proj g|= 6.6478 At iterate 1 f = 257.62 |proj g|= 2.7932 At iterate 2 f = 253.92 |proj g|= 4.9939 At iterate 3 f = 253.85 |proj g|= 5.4887 At iterate 4 f = 251.44 |proj g|= 11.568 At iterate 5 f = 247.1 |proj g|= 13.462 At iterate 6 f = 244.75 |proj g|= 1.5656 At iterate 7 f = 244.28 |proj g|= 1.7192 At iterate 8 f = 244.27 |proj g|= 0.63968 At iterate 9 f = 244.27 |proj g|= 0.19556 At iterate 10 f = 244.27 |proj g|= 0.19489 At iterate 11 f = 244.27 |proj g|= 0.19324 At iterate 12 f = 244.27 |proj g|= 0.27995 At iterate 13 f = 244.27 |proj g|= 0.51507 At iterate 14 f = 244.26 |proj g|= 0.90758 At iterate 15 f = 244.25 |proj g|= 1.4858 At iterate 16 f = 244.21 |proj g|= 1.4965 At iterate 17 f = 244.14 |proj g|= 1.5111 At iterate 18 f = 244.01 |proj g|= 1.5145 At iterate 19 f = 243.76 |proj g|= 1.5099 At iterate 20 f = 243.58 |proj g|= 1.4449 At iterate 21 f = 243.3 |proj g|= 1.732 At iterate 22 f = 243.27 |proj g|= 0.15302 At iterate 23 f = 243.26 |proj g|= 0.063747 At iterate 24 f = 243.26 |proj g|= 0.007939 At iterate 25 f = 243.26 |proj g|= 0.00010823 iterations 25 function evaluations 37 segments explored during Cauchy searches 26 BFGS updates skipped 0 active bounds at final generalized Cauchy point 0 norm of the final projected gradient 0.000108232 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 48.32 2.71 51.29