@Clare So SecondDerivativeTest requires the stationary point to be input. The questioner is asking how to find the stationary point ! I don't see how Student[Calculus1][ExtremePoints] or Student[MultivariateCalculus][SecondDerivativeTest] help in this. Am I missing something ? 

@Clare So SecondDerivativeTest requires the stationary point to be input. The questioner is asking how to find the stationary point ! I don't see how Student[Calculus1][ExtremePoints] or Student[MultivariateCalculus][SecondDerivativeTest] help in this. Am I missing something ? 

@Pseudomodo That's good to know, but I wasn't able to make this work:

v1 := Vector([1, 2, 1, 2],datatype=float[8]):
v2 := Vector([1, 3, 1, 2],datatype=float[8]):
AudioTools:-Convolution(v1,v2);
Error, invalid input: AudioTools:-Convolution expects its 1st argument, audio, to be of type AudioTools:-Audio, but received Vector(4, {(1) = 1.0, (2) = 2.0, (3) = 1.0, (4) = 2.0}, datatype = float[8])

@Pseudomodo That's good to know, but I wasn't able to make this work:

v1 := Vector([1, 2, 1, 2],datatype=float[8]):
v2 := Vector([1, 3, 1, 2],datatype=float[8]):
AudioTools:-Convolution(v1,v2);
Error, invalid input: AudioTools:-Convolution expects its 1st argument, audio, to be of type AudioTools:-Audio, but received Vector(4, {(1) = 1.0, (2) = 2.0, (3) = 1.0, (4) = 2.0}, datatype = float[8])

@Clare So It is a function of two variables. I thought that Student[Calculus1] would only work for one variable ?

@Clare So It is a function of two variables. I thought that Student[Calculus1] would only work for one variable ?

As far as I know, AudioTools [Convolution] acts on audio files, so you would have to save your data as an audio file for that to work.

You appear to have taken the inverse Fourier Transform of the multiplied Fourier Transforms, which of course is another way to achieve the convolution, and should be significantly faster for large data sets. 

As far as I know, AudioTools [Convolution] acts on audio files, so you would have to save your data as an audio file for that to work.

You appear to have taken the inverse Fourier Transform of the multiplied Fourier Transforms, which of course is another way to achieve the convolution, and should be significantly faster for large data sets. 

@lovinash As a workaround, since you only need data, you could export to a csv text file, which should open in excel 2007+ directly,

@lovinash As a workaround, since you only need data, you could export to a csv text file, which should open in excel 2007+ directly,

@lovinash I would check to be sure about it. It was just my assumption, based on the error message. Actually I don't have much experience of exporting to excel. 

@lovinash I would check to be sure about it. It was just my assumption, based on the error message. Actually I don't have much experience of exporting to excel. 

@lovinash Yes, that could be the problem. If you export to column GL and if the matrix has more than 73 columns then it will exceed the maximum size of an excel spreadsheet.

@lovinash Yes, that could be the problem. If you export to column GL and if the matrix has more than 73 columns then it will exceed the maximum size of an excel spreadsheet.

@lovinash  It looks like the way you form the filename for the export is wrong. 

Export(L, sprintf("C:/Validateallslope/NailInc=%a ,cutheight=%a,cutangle=%a, backslopeangle=%a, Maxheight=%a .xlsx", ang, cutheight, angle1, angle2, eq4), "Sheet0", "B11")

I think it should be

Export(L, sprintf("C:/Validateallslope/Nail%a%a%a%a%a.xlsx", ang, cutheight, angle1, angle2, eq4), "Sheet0", "B11")