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没有遇到此类情况，运行了一下，应该是正常的。
>> run C:\gauss8.0\GPE\LESSON7.1;
GPE2 for GAUSS Windows Version 8.0.00 (10-19-2010/15:22:38)
Copyright (C) 2000-2004 Applied Data Associates. All Rights Reserved.
Non-Linear Least Squares Estimation
-----------------------------------
Based on Component Error Function
Estimation Range = 1 30
Number of Observations = 30
Number of Parameters = 4
Maximum Number of Iterations = 100
Step Size Search Method = 0
Convergence Criterion = 1
Tolerance = 1e-005
Initial Result:
Sum of Squares = 37.097
Parameters = 1.0000 0.50000 -1.0000 -1.0000
Using Quadratic Hill-Climbing Algorithm
Iteration = 1 Step Size = 1.0000 Sum of Squares = 9.2983
Parameters = 0.86563 0.46579 -1.1871 -1.1789
Iteration = 2 Step Size = 1.9487 Sum of Squares = 1.9966
Parameters = 0.12598 0.39479 -1.0544 -1.0704
Iteration = 3 Step Size = 1.2100 Sum of Squares = 1.9574
Parameters = 0.11488 0.40450 -1.0854 -1.0168
Iteration = 4 Step Size = 1.0000 Sum of Squares = 1.9396
Parameters = 0.10871 0.39948 -1.1962 -0.90162
Iteration = 5 Step Size = 1.3310 Sum of Squares = 1.9052
Parameters = 0.10891 0.39734 -1.2495 -0.87226
Iteration = 6 Step Size = 1.0000 Sum of Squares = 1.9048
Parameters = 0.10594 0.39178 -1.3913 -0.75848
Iteration = 7 Step Size = 1.1000 Sum of Squares = 1.8623
Parameters = 0.10729 0.39069 -1.4252 -0.75232
Iteration = 8 Step Size = 1.6105 Sum of Squares = 1.8497
Parameters = 0.10597 0.38750 -1.5067 -0.70240
Iteration = 9 Step Size = 2.3579 Sum of Squares = 1.8241
Parameters = 0.10869 0.38236 -1.6533 -0.63656
Iteration = 10 Step Size = 1.2100 Sum of Squares = 1.8122
Parameters = 0.10822 0.37807 -1.7697 -0.58933
Iteration = 11 Step Size = 2.3579 Sum of Squares = 1.8018
Parameters = 0.10971 0.37478 -1.8651 -0.56202
Iteration = 12 Step Size = 1.2100 Sum of Squares = 1.7956
Parameters = 0.10931 0.37316 -1.9095 -0.54443
Iteration = 13 Step Size = 1.6105 Sum of Squares = 1.7875
Parameters = 0.11182 0.36873 -2.0393 -0.50633
Iteration = 14 Step Size = 1.4641 Sum of Squares = 1.7831
Parameters = 0.11187 0.36732 -2.0809 -0.49807
Iteration = 15 Step Size = 1.3310 Sum of Squares = 1.7790
Parameters = 0.11272 0.36442 -2.1652 -0.47537
Iteration = 16 Step Size = 2.3579 Sum of Squares = 1.7754
Parameters = 0.11462 0.36199 -2.2401 -0.46082
Iteration = 17 Step Size = 1.3310 Sum of Squares = 1.7732
Parameters = 0.11424 0.36069 -2.2766 -0.45120
Iteration = 18 Step Size = 2.3579 Sum of Squares = 1.7691
Parameters = 0.11678 0.35681 -2.3946 -0.42773
Iteration = 19 Step Size = 2.3579 Sum of Squares = 1.7666
Parameters = 0.11676 0.35396 -2.4795 -0.41266
Iteration = 20 Step Size = 1.0000 Sum of Squares = 1.7650
Parameters = 0.11869 0.35073 -2.5781 -0.39523
Iteration = 21 Step Size = 1.3310 Sum of Squares = 1.7643
Parameters = 0.11905 0.35015 -2.5964 -0.39323
Iteration = 22 Step Size = 1.7716 Sum of Squares = 1.7634
Parameters = 0.11986 0.34784 -2.6665 -0.38160
Iteration = 23 Step Size = 1.3310 Sum of Squares = 1.7630
Parameters = 0.12026 0.34727 -2.6844 -0.37958
Iteration = 24 Step Size = 1.7716 Sum of Squares = 1.7624
Parameters = 0.12107 0.34496 -2.7548 -0.36879
Iteration = 25 Step Size = 1.3310 Sum of Squares = 1.7621
Parameters = 0.12139 0.34452 -2.7689 -0.36737
Iteration = 26 Step Size = 1.7716 Sum of Squares = 1.7617
Parameters = 0.12200 0.34275 -2.8229 -0.35959
Iteration = 27 Step Size = 1.4641 Sum of Squares = 1.7616
Parameters = 0.12231 0.34224 -2.8391 -0.35781
Iteration = 28 Step Size = 1.9487 Sum of Squares = 1.7614
Parameters = 0.12280 0.34078 -2.8838 -0.35166
Iteration = 29 Step Size = 1.3310 Sum of Squares = 1.7613
Parameters = 0.12303 0.34041 -2.8952 -0.35048
Iteration = 30 Step Size = 1.7716 Sum of Squares = 1.7612
Parameters = 0.12354 0.33900 -2.9387 -0.34483
Iteration = 31 Step Size = 1.3310 Sum of Squares = 1.7611
Parameters = 0.12370 0.33872 -2.9476 -0.34392
Iteration = 32 Step Size = 1.7716 Sum of Squares = 1.7611
Parameters = 0.12409 0.33766 -2.9802 -0.33985
Iteration = 33 Step Size = 1.2100 Sum of Squares = 1.7611
Parameters = 0.12418 0.33749 -2.9858 -0.33929
Iteration = 34 Step Size = 1.1000 Sum of Squares = 1.7611
Parameters = 0.12443 0.33682 -3.0064 -0.33682
Iteration = 35 Step Size = 1.1000 Sum of Squares = 1.7611
Parameters = 0.12447 0.33673 -3.0093 -0.33650
Iteration = 36 Step Size = 1.0000 Sum of Squares = 1.7611
Parameters = 0.12449 0.33667 -3.0110 -0.33630
Iteration = 37 Step Size = 1.0000 Sum of Squares = 1.7611
Parameters = 0.12449 0.33667 -3.0109 -0.33630
Iteration = 38 Step Size = 1.0000 Sum of Squares = 1.7611
Parameters = 0.12449 0.33667 -3.0109 -0.33630
Final Result:
Iterations = 38 Evaluations = 31980
Sum of Squares = 1.7611
Parameters = 0.12449 0.33667 -3.0109 -0.33630
Gradient Vector = -3.0614e-007 -2.2331e-007 -8.8575e-009 -1.3896e-007
Asymptotic Asymptotic
Parameter Std. Error t-Ratio
X1 0.12449 0.073426 1.6955
X2 0.33667 0.10197 3.3018
X3 -3.0109 2.0009 -1.5048
X4 -0.33630 0.23430 -1.4353
Asymptotic Variance-Covariance Matrix
X1 0.0053914
X2 -0.00089092 0.010397
X3 -0.050585 0.12896 4.0035
X4 0.0053646 -0.015114 -0.46833 0.054899
X1 X2 X3 X4
>>
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