Maximize multivariable function with infinite maxima

In summary, the conversation discusses finding the set of maxima for a 2-variable function in Cartesian coordinates. The solution is found by transforming the problem to one dimension using polar coordinates and using the second partial derivative test to determine whether the critical point is a local minimum, local maximum, or saddle point.
  • #1
Patrick94
3
0
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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  • #2
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?
 
  • #3
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.
 
  • #4
In general, you can try the second partial derivative test.

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

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

What is a multivariable function?

A multivariable function is a function that takes in more than one variable as inputs and produces an output. It can be written in the form f(x1, x2,..., xn) where x1, x2,..., xn are the input variables and f is the function.

What does it mean to maximize a multivariable function?

To maximize a multivariable function means to find the values of the input variables that produce the highest possible output value. This is often referred to as finding the "peak" or "maxima" of the function.

What is an infinite maxima?

An infinite maxima refers to a situation where the maximum value of a multivariable function cannot be determined due to the function having an infinite number of "peaks". This can occur when the function has a highly complex or oscillating behavior.

Why is it important to maximize multivariable functions?

Maximizing multivariable functions is important because it allows us to find the most optimal solutions in various fields such as economics, engineering, and science. It can also help us understand the behavior of complex systems and make predictions.

What are some methods to maximize a multivariable function?

There are several methods to maximize a multivariable function, such as using calculus techniques like partial derivatives and gradient descent, optimization algorithms, and computer simulations. The choice of method depends on the complexity and nature of the function.

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