Newton's method for multivariate optimization in matlab

How to compute the gradient and hessian matrix when the equation cannot be solved numerically?

My minimization equation is:

c=c[(x/y/(1-x)^2)^0.6 + (1-(x/y)/(1-y)^2)^0.6 + 6/y^0

I tried the MATLAB function "diff" to compute the gradient and hessian. 开发者_如何学编程But derivations are much longer than one can handle. How to write the code for computing the hessian or gradient?

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Why do you say the equation cannot be solved numerically? Do you mean it cannot be solved analytically? There appears to be a typo in your statement of the function c that you wish to optimize. When you refer to your use of Matlab's diff() function, do you mean that you evaluated your function on a grid and then differenced it? Or are you talking about passing a function handle to Matlab's symbolic library?