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Sameer Agarwalea117042012-08-29 18:18:48 -07001NIST/ITL StRD
2Dataset Name: MGH09 (MGH09.dat)
3
4File Format: ASCII
5 Starting Values (lines 41 to 44)
6 Certified Values (lines 41 to 49)
7 Data (lines 61 to 71)
8
9Procedure: Nonlinear Least Squares Regression
10
11Description: This problem was found to be difficult for some very
12 good algorithms. There is a local minimum at (+inf,
13 -14.07..., -inf, -inf) with final sum of squares
14 0.00102734....
15
16 See More, J. J., Garbow, B. S., and Hillstrom, K. E.
17 (1981). Testing unconstrained optimization software.
18 ACM Transactions on Mathematical Software. 7(1):
19 pp. 17-41.
20
21Reference: Kowalik, J.S., and M. R. Osborne, (1978).
22 Methods for Unconstrained Optimization Problems.
23 New York, NY: Elsevier North-Holland.
24
25Data: 1 Response (y)
26 1 Predictor (x)
27 11 Observations
28 Higher Level of Difficulty
29 Generated Data
30
31Model: Rational Class (linear/quadratic)
32 4 Parameters (b1 to b4)
33
34 y = b1*(x**2+x*b2) / (x**2+x*b3+b4) + e
35
36
37
38 Starting values Certified Values
39
40 Start 1 Start 2 Parameter Standard Deviation
41 b1 = 25 0.25 1.9280693458E-01 1.1435312227E-02
42 b2 = 39 0.39 1.9128232873E-01 1.9633220911E-01
43 b3 = 41.5 0.415 1.2305650693E-01 8.0842031232E-02
44 b4 = 39 0.39 1.3606233068E-01 9.0025542308E-02
45
46Residual Sum of Squares: 3.0750560385E-04
47Residual Standard Deviation: 6.6279236551E-03
48Degrees of Freedom: 7
49Number of Observations: 11
50
51
52
53
54
55
56
57
58
59
60Data: y x
61 1.957000E-01 4.000000E+00
62 1.947000E-01 2.000000E+00
63 1.735000E-01 1.000000E+00
64 1.600000E-01 5.000000E-01
65 8.440000E-02 2.500000E-01
66 6.270000E-02 1.670000E-01
67 4.560000E-02 1.250000E-01
68 3.420000E-02 1.000000E-01
69 3.230000E-02 8.330000E-02
70 2.350000E-02 7.140000E-02
71 2.460000E-02 6.250000E-02