blob: df88ea4cdde535c8775d997a6b695667e2c7c890 [file] [log] [blame]
Dataset Name: MGH10 (MGH10.dat)
File Format: ASCII
Starting Values (lines 41 to 43)
Certified Values (lines 41 to 48)
Data (lines 61 to 76)
Procedure: Nonlinear Least Squares Regression
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: Meyer, R. R. (1970).
Theoretical and computational aspects of nonlinear
regression. In Nonlinear Programming, Rosen,
Mangasarian and Ritter (Eds).
New York, NY: Academic Press, pp. 465-486.
Data: 1 Response (y)
1 Predictor (x)
16 Observations
Higher Level of Difficulty
Generated Data
Model: Exponential Class
3 Parameters (b1 to b3)
y = b1 * exp[b2/(x+b3)] + e
Starting values Certified Values
Start 1 Start 2 Parameter Standard Deviation
b1 = 2 0.02 5.6096364710E-03 1.5687892471E-04
b2 = 400000 4000 6.1813463463E+03 2.3309021107E+01
b3 = 25000 250 3.4522363462E+02 7.8486103508E-01
Residual Sum of Squares: 8.7945855171E+01
Residual Standard Deviation: 2.6009740065E+00
Degrees of Freedom: 13
Number of Observations: 16
Data: y x
3.478000E+04 5.000000E+01
2.861000E+04 5.500000E+01
2.365000E+04 6.000000E+01
1.963000E+04 6.500000E+01
1.637000E+04 7.000000E+01
1.372000E+04 7.500000E+01
1.154000E+04 8.000000E+01
9.744000E+03 8.500000E+01
8.261000E+03 9.000000E+01
7.030000E+03 9.500000E+01
6.005000E+03 1.000000E+02
5.147000E+03 1.050000E+02
4.427000E+03 1.100000E+02
3.820000E+03 1.150000E+02
3.307000E+03 1.200000E+02
2.872000E+03 1.250000E+02