entrust/opt03_output.txt at master · jborggaard/entrust · GitHub

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>> opt03_run
---------------------------------------------------------
NEWTON applied to F,G,H optimization system:
---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = 8.000000
Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 8 function/gradient evaluations

Newton produced  (6.9314718e-01)
Value of F(X) = 1.5777218e-30

---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = 3.000000
Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 10 function/gradient evaluations

Newton produced  (4.4004986e-01)
Value of F(X) = 1.6389928e+00

---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = -1.000000
Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 11 function/gradient evaluations

Newton produced  (4.4743984e-02)
Value of F(X) = 6.9764611e+00

---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = -4.000000
Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 12 function/gradient evaluations

Newton produced  (-3.7192873e-01)
Value of F(X) = 1.6434978e+01

---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = -8.000000
Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 13 function/gradient evaluations

Newton produced  (-7.9148634e-01)
Value of F(X) = 4.1144822e+01

---------------------------------------------------------

  Start X0 = 0.500000
  Parameter P = 3.000000
Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 6 function/gradient evaluations

Newton produced  (4.4004986e-01)
Value of F(X) = 1.6389928e+00

---------------------------------------------------------
Gauss-Newton applied to RES, JAC nonlinear least squares system:
---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = 8.000000
Gauss Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 6 function/gradient evaluations

Gauss-Newton produced  (6.9314718e-01)
Norm of RES(X) = 5.8039062e-13

---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = 3.000000
Gauss Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 12 function/gradient evaluations

Gauss-Newton produced  (4.4004986e-01)
Norm of RES(X) = 1.8105208e+00

---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = -1.000000
Gauss Newton:
Used the maximum number of function evaluations.
Optimization required 30 function/gradient evaluations

Gauss-Newton produced  (4.4743984e-02)
Norm of RES(X) = 3.7353611e+00

---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = -4.000000
Gauss Newton:
Used the maximum number of function evaluations.
Optimization required 30 function/gradient evaluations

Gauss-Newton produced  (-3.0438566e-01)
Norm of RES(X) = 5.7365990e+00

---------------------------------------------------------

  Start X0 = 1.000000
  Parameter P = -8.000000
Gauss Newton:
Used the maximum number of function evaluations.
Optimization required 30 function/gradient evaluations

Gauss-Newton produced  (3.2924827e+00)
Norm of RES(X) = 1.9507247e+04

---------------------------------------------------------

  Start X0 = 0.500000
  Parameter P = 3.000000
Gauss Newton:
Gradient is within tolerance, x likely a local optimum.
Optimization required 10 function/gradient evaluations

Gauss-Newton produced  (4.4004986e-01)
Norm of RES(X) = 1.8105208e+00