06 October 2025 6:24:14.167 PM minpack_test(): Fortran90 version Test minpack(). CHKDER_TEST CHKDER compares a user supplied jacobian and a finite difference approximation to it and judges whether the jacobian is correct. On the first test, use a correct jacobian. Evaluation point X: 1 0.50000000 2 0.50000000 3 0.50000000 4 0.50000000 5 0.50000000 Sampled function values F(X) and F(XP) 1 -3.00000 -3.00000 2 -3.00000 -3.00000 3 -3.00000 -3.00000 4 -3.00000 -3.00000 5 -0.968750 -0.968750 Computed jacobian 2.00000 1.00000 1.00000 1.00000 1.00000 1.00000 2.00000 1.00000 1.00000 1.00000 1.00000 1.00000 2.00000 1.00000 1.00000 1.00000 1.00000 1.00000 2.00000 1.00000 0.625000E-01 0.625000E-01 0.625000E-01 0.625000E-01 0.625000E-01 CHKDER gradient component error estimates: > 0.5, the component is probably correct. < 0.5, the component is probably incorrect. 1 1.00000 2 1.00000 3 1.00000 4 1.00000 5 1.00000 Repeat the test, but use a "bad" jacobian and see if the routine notices! Evaluation point X: 1 0.50000000 2 0.50000000 3 0.50000000 4 0.50000000 5 0.50000000 Sampled function values F(X) and F(XP) 1 -3.00000 -3.00000 2 -3.00000 -3.00000 3 -3.00000 -3.00000 4 -3.00000 -3.00000 5 -0.968750 -0.968750 Computed jacobian 2.02000 1.00000 1.00000 1.00000 1.00000 1.00000 2.00000 -1.00000 1.00000 1.00000 1.00000 1.00000 2.00000 1.00000 1.00000 1.00000 1.00000 1.00000 2.00000 1.00000 0.625000E-01 0.625000E-01 0.625000E-01 0.625000E-01 0.625000E-01 CHKDER gradient component error estimates: > 0.5, the component is probably correct. < 0.5, the component is probably incorrect. 1 0.354955 2 0.994216E-01 3 1.00000 4 1.00000 5 1.00000 HYBRD1_TEST HYBRD1 solves a nonlinear system of equations. Initial X: 1 3.0000000 2 0.0000000 Initial F(X): 1 -13.000000 2 11.000000 Returned value of INFO = 1 Final X: 1 1.0000000 2 1.0000000 Final F(X): 1 -0.96195052E-10 2 -0.12353851E-09 HYBRJ1_TEST HYBRJ1 solves a nonlinear system of equations. Initial X: 1 3.0000000 2 0.0000000 F(X): 1 -13.000000 2 11.000000 Returned value of INFO = 1 X: 1 1.0000000 2 1.0000000 F(X): 1 -0.96195052E-10 2 -0.12353851E-09 LMDER1_TEST LMDER1 minimizes M functions in N variables. Initial X: 1 0.0000000 2 5.0000000 F(X): 1 3.0000000 2 -6.0000000 3 -23.000000 4 -35.000000 Returned value of INFO = 3 X: 1 6.5500000 2 -12.500000 F(X): 1 -1.4000000 2 2.7000000 3 -1.2000000 4 -0.10000000 LMDER1_2_TEST LMDER1 minimizes M functions in N variables. Initial X: 1 0.0000000 2 5.0000000 3 1.3000000 F(X): 1 1.0000000 2 -0.68855587 3 -7.1441624 4 -18.685669 5 -35.483585 6 -57.646904 7 -85.252351 8 -118.35736 9 -157.00681 10 -201.23688 Returned value of INFO = 2 X: 1 1.0000000 2 3.0000000 3 2.0000000 F(X): 1 0.13233858E-12 2 0.33750780E-13 3 0.23803182E-12 4 0.85975671E-12 5 0.19895197E-11 6 0.36948222E-11 7 0.60822458E-11 8 0.90381036E-11 9 0.12818191E-10 10 0.17280399E-10 LMDIF1_TEST LMDIF1 minimizes M functions in N variables. Initial X: 1 0.0000000 2 5.0000000 F(X): 1 3.0000000 2 -6.0000000 3 -23.000000 4 -35.000000 Returned value of INFO = 3 X: 1 6.5500000 2 -12.500000 F(X): 1 -1.4000000 2 2.7000000 3 -1.2000000 4 -0.10000000 LMDIF1_2_TEST LMDIF1 minimizes M functions in N variables. X: 1 0.0000000 2 5.0000000 3 1.3000000 F(X): 1 1.0000000 2 -0.68855587 3 -7.1441624 4 -18.685669 5 -35.483585 6 -57.646904 7 -85.252351 8 -118.35736 9 -157.00681 10 -201.23688 Returned value of INFO = 2 X: 1 1.0000000 2 3.0000000 3 2.0000000 F(X): 1 0.18918200E-12 2 0.42632564E-13 3 0.26290081E-12 4 0.99475983E-12 5 0.23732127E-11 6 0.44337867E-11 7 0.72759576E-11 8 0.10970780E-10 9 0.15546675E-10 10 0.21032065E-10 LMSTR1_TEST LMSTR1 minimizes M functions in N variables. Initial X: 1 0.0000000 2 5.0000000 F(X): 1 3.0000000 2 -6.0000000 3 -23.000000 4 -35.000000 Returned value of INFO = 2 X: 1 6.5500000 2 -12.500000 F(X): 1 -1.4000000 2 2.7000000 3 -1.2000000 4 -0.10000000 LMSTR1_2_TEST LMSTR1 minimizes M functions in N variables. Initial X: 1 0.0000000 2 5.0000000 3 1.3000000 F(X): 1 1.0000000 2 -0.68855587 3 -7.1441624 4 -18.685669 5 -35.483585 6 -57.646904 7 -85.252351 8 -118.35736 9 -157.00681 10 -201.23688 Returned value of INFO = 2 X: 1 1.0000000 2 3.0000000 3 2.0000000 F(X): 1 0.13322676E-12 2 0.33750780E-13 3 0.24158453E-12 4 0.85975671E-12 5 0.19895197E-11 6 0.36948222E-11 7 0.60822458E-11 8 0.90381036E-11 9 0.12818191E-10 10 0.17280399E-10 QFORM_TEST: QFORM constructs the Q factor explicitly after the use of QRFAC. Matrix A: Col 1 2 3 4 5 Row 1: 0.513982 0.946200 0.792210 0.963104 0.936911 2: 0.334074 0.610125 0.870156 0.588794E-01 0.763703 3: 0.321509 0.707435 0.375608 0.716604 0.907192 4: 0.573223 0.661691 0.582119 0.901903E-01 0.368680 5: 0.980755 0.425162 0.680723 0.347051 0.604379 Col 6 7 Row 1: 0.312808 0.439997 2: 0.860105 0.137163 3: 0.918952 0.407041 4: 0.817953E-01 0.944615 5: 0.773094 0.929817 Matrix R: Col 1 2 3 4 5 Row 1: -1.33027 -1.28837 -1.36810 -0.854830 -1.37750 2: 0. -0.852472 -0.537480 -0.656979 -0.845097 3: 0. 0. 0.409271 -0.367689 0.849500E-01 4: 0. 0. 0. 0.524963 0.264602 5: 0. 0. 0. 0. -0.310356 Col 6 7 Row 1: -1.16418 -1.39539 2: -0.414992 -0.123863E-01 3: 0.243143 -0.273785 4: 0.205157 -0.324228 5: -0.811005 0.872616E-01 Matrix Q: Col 1 2 3 4 5 Row 1: -0.386374 -0.526007 -0.466885E-01 0.514470 0.554231 2: -0.251133 -0.336167 0.845153 -0.125528 -0.306403 3: -0.241687 -0.464592 -0.500289 0.396689E-01 -0.688389 4: -0.430908 -0.124957 -0.182201 -0.813867 0.321136 5: -0.737260 0.615507 0.707828E-02 0.235819 -0.148114 Matrix A2 = Q * R: Col 1 2 3 4 5 Row 1: 0.513982 0.946200 0.792210 0.963104 0.936911 2: 0.334074 0.610125 0.870156 0.588794E-01 0.763703 3: 0.321509 0.707435 0.375608 0.716604 0.907192 4: 0.573223 0.661691 0.582119 0.901903E-01 0.368680 5: 0.980755 0.425162 0.680723 0.347051 0.604379 Col 6 7 Row 1: 0.312808 0.439997 2: 0.860105 0.137163 3: 0.918952 0.407041 4: 0.817953E-01 0.944615 5: 0.773094 0.929817 minpack_test(): Normal end of execution. 06 October 2025 6:24:14.167 PM