File: MGH17EM.txt

URL Reference: http://www.itl.nist.gov/div898/strd/nls/data/mgh17.shtml

Dataset Name:  MGH17             (MGH17.dat)

Description:   This problem was found to be difficult for some very
               good algorithms.

               See More, J. J., Garbow, B. S., and Hillstrom, K. E.
               (1981).  Testing unconstrained optimization software.
               ACM Transactions on Mathematical Software. 7(1):
               pp. 17-41.

Reference:     Osborne, M. R. (1972).  
               Some aspects of nonlinear least squares 
               calculations.  In Numerical Methods for Nonlinear 
               Optimization, Lootsma (Ed).  
               New York, NY:  Academic Press, pp. 171-189.

Data:  y               x
      8.440000E-01    0.000000E+00
      9.080000E-01    1.000000E+01
      9.320000E-01    2.000000E+01
      9.360000E-01    3.000000E+01
      9.250000E-01    4.000000E+01
      9.080000E-01    5.000000E+01
      8.810000E-01    6.000000E+01
      8.500000E-01    7.000000E+01
      8.180000E-01    8.000000E+01
      7.840000E-01    9.000000E+01
      7.510000E-01    1.000000E+02
      7.180000E-01    1.100000E+02
      6.850000E-01    1.200000E+02
      6.580000E-01    1.300000E+02
      6.280000E-01    1.400000E+02
      6.030000E-01    1.500000E+02
      5.800000E-01    1.600000E+02
      5.580000E-01    1.700000E+02
      5.380000E-01    1.800000E+02
      5.220000E-01    1.900000E+02
      5.060000E-01    2.000000E+02
      4.900000E-01    2.100000E+02
      4.780000E-01    2.200000E+02
      4.670000E-01    2.300000E+02
      4.570000E-01    2.400000E+02
      4.480000E-01    2.500000E+02
      4.380000E-01    2.600000E+02
      4.310000E-01    2.700000E+02
      4.240000E-01    2.800000E+02
      4.200000E-01    2.900000E+02
      4.140000E-01    3.000000E+02
      4.110000E-01    3.100000E+02
      4.060000E-01    3.200000E+02

Mathematica Lists:
xLst = {0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320};

yLst = {0.844, 0.908, 0.932, 0.936, 0.925, 0.908, 0.881, 0.850, 0.818, 0.784, 0.751, 0.718, 0.685, 0.658, 0.628, 0.603, 0.580, 0.558, 0.538, 0.522, 0.506, 0.490, 0.478, 0.467, 0.457, 0.448, 0.438, 0.431, 0.424, 0.420, 0.414, 0.411, 0.406};

Maple Lists:
xLst := [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320]:

yLst := [0.844, 0.908, 0.932, 0.936, 0.925, 0.908, 0.881, 0.850, 0.818, 0.784, 0.751, 0.718, 0.685, 0.658, 0.628, 0.603, 0.580, 0.558, 0.538, 0.522, 0.506, 0.490, 0.478, 0.467, 0.457, 0.448, 0.438, 0.431, 0.424, 0.420, 0.414, 0.411, 0.406]:

MATLAB Row Vectors:
xLst = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320];

yLst = [0.844, 0.908, 0.932, 0.936, 0.925, 0.908, 0.881, 0.850, 0.818, 0.784, 0.751, 0.718, 0.685, 0.658, 0.628, 0.603, 0.580, 0.558, 0.538, 0.522, 0.506, 0.490, 0.478, 0.467, 0.457, 0.448, 0.438, 0.431, 0.424, 0.420, 0.414, 0.411, 0.406];