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Aug
8
accepted Limitations of ParametricNDSolve family w.r.t objective functions
Aug
8
revised Limitations of ParametricNDSolve family w.r.t objective functions
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Aug
8
revised Limitations of ParametricNDSolve family w.r.t objective functions
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Aug
8
comment Limitations of ParametricNDSolve family w.r.t objective functions
@ruebenko Sorry! I expect -7.86673 for pfun[1.5]. I had a mistake in the previous comment.
Aug
8
revised Peak Signal to Noise Ratio of Images
deleted 7 characters in body
Aug
8
comment Limitations of ParametricNDSolve family w.r.t objective functions
@ruebenko if pfun[1.5] is unevaluated and so is pfun'[1.5] then one needs to be really audacious to expect even anything sensible for the higher derivatives. But M9 in this specific case returns values for the higher derivatives. That is not the kind of behavior one expects from a intelligent system.
Aug
8
revised Limitations of ParametricNDSolve family w.r.t objective functions
edited title
Aug
8
revised Limitations of ParametricNDSolve family w.r.t objective functions
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Aug
8
asked Limitations of ParametricNDSolve family w.r.t objective functions
Aug
6
revised How do I find the best fit?
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Jul
16
comment How to nest the output of FindRoot in a table of Derivative evaluations?
I can not comment on this issue until I see the larger system. However my approach should work seamlessly as far as mathematics is concerned.
Jul
14
answered Create a Matrix out of two matrices
Jul
12
comment Building graph based on the cities connection?
@cormullion you had already done that nicely when I saw this question so I thought to add little randomness to the set of answers...;)
Jul
12
answered Building graph based on the cities connection?
Jul
8
comment Check if an inequality holds among the solutions returned by Solve
Try First@Solve[{a == 3*c, b == 2*a}, {a, b}]; Reduce[(a < 7 b) /. %]
Jul
5
answered How to nest the output of FindRoot in a table of Derivative evaluations?
Jul
4
revised Numerically solving system of partial differential equation
added 40 characters in body
Jul
3
comment Removing of artificial sampling step effects
Also worth trying is smooth = Most@SortBy[ExponentialMovingAverage[curve, 1/12], Last]~ Join~{Last@SortBy[curve, Last]}; ListPlot[{curve, smooth}, Joined -> {False, True}, Frame -> True, PlotRange -> All]
Jul
3
comment Removing of artificial sampling step effects
model = a x^3 + b x^2 + c x + d; fit = FindFit[curve, model, {a, b, c, d}, x]; modelf = Function[{x}, Evaluate[model /. fit]]; Show[ListPlot@curve, Plot[{modelf[x]}, {x, 62700, 72300}, Frame -> True, Axes -> None, PlotStyle -> Red]] does this simple cubic polynomial passes your test of goodness?
Jul
3
revised Removing of artificial sampling step effects
added 4 characters in body