We have a list which is considered to be fitted by a suitable function. The list is given at the end of the text. It maybe important to mention that the first column of the list is not in the same interval I mean:
list[[All, 1]][[1 ;; 50]] has an interval of 2*10^-6
list[[All, 1]][[51 ;; 150]] has an interval of 10^-5
We have exploited of
nlf=NonlinearModelFit[list, a Exp[b g] + c, {a, {b, -1}, c},
g]
But we encountered the message: "Failed to converge to the requested accuracy or precision within 100 iterations."
How can we overcome this problem? Does the problem arise of the not same interval of the first column of data?
list={{0., 0.499999}, {2.*10^-7, 0.499311}, {4.*10^-7,
0.499025}, {6.*10^-7, 0.498806}, {8.*10^-7, 0.498621}, {1.*10^-6,
0.498459}, {1.2*10^-6, 0.498311}, {1.4*10^-6, 0.498176}, {1.6*10^-6,
0.49805}, {1.8*10^-6, 0.497931}, {2.*10^-6, 0.497819}, {2.2*10^-6,
0.497713}, {2.4*10^-6, 0.497611}, {2.6*10^-6, 0.497513}, {2.8*10^-6,
0.497419}, {3.*10^-6, 0.497328}, {3.2*10^-6, 0.49724}, {3.4*10^-6,
0.497155}, {3.6*10^-6, 0.497072}, {3.8*10^-6, 0.496992}, {4.*10^-6,
0.496914}, {4.2*10^-6, 0.496837}, {4.4*10^-6, 0.496752}, {4.6*10^-6,
0.496679}, {4.8*10^-6, 0.496607}, {5.*10^-6, 0.496537}, {5.2*10^-6,
0.496468}, {5.4*10^-6, 0.4964}, {5.6*10^-6, 0.496334}, {5.8*10^-6,
0.496268}, {6.*10^-6, 0.496204}, {6.2*10^-6, 0.496141}, {6.4*10^-6,
0.496079}, {6.6*10^-6, 0.496017}, {6.8*10^-6, 0.495957}, {7.*10^-6,
0.495898}, {7.2*10^-6, 0.495839}, {7.4*10^-6, 0.495781}, {7.6*10^-6,
0.495724}, {7.8*10^-6, 0.495668}, {8.*10^-6, 0.495612}, {8.2*10^-6,
0.495557}, {8.4*10^-6, 0.495503}, {8.6*10^-6,
0.495449}, {8.8*10^-6, 0.495396}, {9.*10^-6, 0.495344}, {9.2*10^-6,
0.495292}, {9.4*10^-6, 0.495241}, {9.6*10^-6, 0.49519}, {9.8*10^-6,
0.49514}, {0.00001, 0.49509}, {0.000011, 0.494848}, {0.000021,
0.492857}, {0.000031, 0.491298}, {0.000041, 0.48997}, {0.000051,
0.488791}, {0.000061, 0.487719}, {0.000071, 0.486728}, {0.000081,
0.485803}, {0.000091, 0.48493}, {0.000101, 0.484102}, {0.000111,
0.483312}, {0.000121, 0.482555}, {0.000131, 0.481827}, {0.000141,
0.481125}, {0.000151, 0.480446}, {0.000161, 0.479788}, {0.000171,
0.479148}, {0.000181, 0.478526}, {0.000191, 0.477919}, {0.000201,
0.477328}, {0.000211, 0.476749}, {0.000221, 0.476184}, {0.000231,
0.47563}, {0.000241, 0.475087}, {0.000251, 0.474555}, {0.000261,
0.474032}, {0.000271, 0.473518}, {0.000281, 0.473013}, {0.000291,
0.472516}, {0.000301, 0.472027}, {0.000311, 0.471545}, {0.000321,
0.471071}, {0.000331, 0.470603}, {0.000341, 0.470141}, {0.000351,
0.469686}, {0.000361, 0.469236}, {0.000371, 0.468792}, {0.000381,
0.468354}, {0.000391, 0.467921}, {0.000401, 0.467492}, {0.000411,
0.467069}, {0.000421, 0.46665}, {0.000431, 0.466236}, {0.000441,
0.465826}, {0.000451, 0.46542}, {0.000461, 0.465018}, {0.000471,
0.46462}, {0.000481, 0.464226}, {0.000491, 0.463836}, {0.000501,
0.463449}, {0.000511, 0.463066}, {0.000521, 0.462685}, {0.000531,
0.462309}, {0.000541, 0.461935}, {0.000551, 0.461564}, {0.000561,
0.461197}, {0.000571, 0.460832}, {0.000581, 0.46047}, {0.000591,
0.460111}, {0.000601, 0.459755}, {0.000611, 0.459401}, {0.000621,
0.45905}, {0.000631, 0.458701}, {0.000641, 0.458355}, {0.000651,
0.458011}, {0.000661, 0.45767}, {0.000671, 0.457331}, {0.000681,
0.456994}, {0.000691, 0.456659}, {0.000701, 0.456326}, {0.000711,
0.455996}, {0.000721, 0.455667}, {0.000731, 0.45534}, {0.000741,
0.455016}, {0.000751, 0.454693}, {0.000761, 0.454373}, {0.000771,
0.454054}, {0.000781, 0.453737}, {0.000791, 0.453421}, {0.000801,
0.453108}, {0.000811, 0.452796}, {0.000821, 0.452486}, {0.000831,
0.452177}, {0.000841, 0.45187}, {0.000851, 0.451565}, {0.000861,
0.451261}, {0.000871, 0.450959}, {0.000881, 0.450658}, {0.000891,
0.450359}, {0.000901, 0.450061}, {0.000911, 0.449765}, {0.000921,
0.44947}, {0.000931, 0.449177}, {0.000941, 0.448885}, {0.000951,
0.448594}, {0.000961, 0.448304}, {0.000971, 0.448016}, {0.000981,
0.44773}, {0.000991, 0.447444}};
MaxIterations -> 2000
is included. You might want to trya - b g^0.5
for the model. $\endgroup$ – JimB May 27 '17 at 6:46