Maximize multivariable function with infinite maxima

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To maximize the given 2-variable function with respect to z, the discussion suggests transforming the problem into polar coordinates due to the circular nature of the solutions around the point (2,3). However, a preference for Cartesian coordinates is expressed, as the function may become more complex with additional terms. The second partial derivative test is recommended for identifying local maxima or minima. The Hessian matrix plays a crucial role in determining the nature of critical points, with specific conditions indicating local maxima, minima, or saddle points. Understanding these mathematical concepts is essential for effectively solving the problem.
Patrick94
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Could someone walk me through how to maximize this 2-variable function wrt z?

http://www.wolframalpha.com/input/?...)^2)))+-+100/(1+(root+((x-2)^2+++(y-3)^2))^2)

I know the set of solutions will form a circle around the point (2,3). How do I go about finding the set of maxima that form this circle/the equation of this circle?

(I am a complete math novice)!

Thanks
 
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Well, if you know that the set of solutions form a circle, then you can transform the problem to one dimension by changing to a polar coordinate system, no?
 
I want to be able to solve in Cartesian coordinates, I think, since this is the very simplified form of a function which will contain many more terms.
 
In general, you can try the second partial derivative test.

Let \vec H_k(f(\vec x)) be the Hessian matrix of the function f(\vec x) (evaluated at \vec x) of the k first variables, where k = 1, 2, 3, ... , n.

If you're function is f(\vec x) then the critical point \vec p, i.e. \nabla f(\vec p) = \vec 0, is a local minimum if \forall k : |\vec H_k(f(\vec p))| > 0 and a local maximum if \forall k : (-1)^k |\vec H_k(f(\vec p))| > 0. For all other cases, \vec p is a saddle point unless |\vec H_n(f(\vec p))| = 0, for which the test is inconclusive.
 

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