How to get minimum value of given function by steepest-descent method with one-dimensional linear search?

given function:

f=exp(-(x1-mul1)^2/(2*sigma1^2))*exp(-(x2-mul2)^2/(2*sigma2^2))*...
   *exp(-(xn-muln)^2/(2*sigman^2));

where X{x1,x2,…,xn}is given by input data set, we can see X as known value. (muli,sigmai) is the parameter corresponding to xi. Looking forward your coding guidance,thank you!

[Torsten]

I don't really know what you are trying to do.

Maximum Likelihood estimation of the parameters of an n-dimensional Gaussian ?

Best wishes

Torsten.