gradient - Newton's method for multivariate optimization in matlab -
how compute gradient , hessian matrix when equation cannot 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 matlab function "diff" compute gradient , hessian. derivations longer 1 can handle. how write code computing hessian or gradient?
why equation cannot solved numerically? mean cannot solved analytically? there appears typo in statement of function c wish optimize. when refer use of matlab's diff() function, mean evaluated function on grid , differenced it? or talking passing function handle matlab's symbolic library?
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