How Does Quadratic Approximation Determine Local Max/Min?

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Quadratic approximation is used to determine local maxima and minima by analyzing the behavior of a function near a point using its second derivatives. For multivariable functions, the Hessian matrix, which consists of second partial derivatives, is crucial in identifying the nature of critical points. A positive definite Hessian indicates a local minimum, while a negative definite Hessian suggests a local maximum. The concept of visualizing the function as a 3-D surface helps in understanding the location of bumps or dips that correspond to these extrema. Completing the square for quadratic functions can also aid in finding the vertex, which represents the maximum or minimum point.
Chadlee88
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hey can som1 please help, i know how to find the quadratic approximation for a given function but i don't know how the quadratic approximation determines a local max/min :confused: This is with regard to multivariable functions. thanks
 
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If you have a quadratic function of one variable, how do you find any maxima and minima of that function? How do you determine if any points you find are maxima or minima? How are derivatives involved?

Now for multivariable functions, you do much the same thing. But what special kind of derivative do you need to use when dealing with multivariable equations? Do you need to do anything special when finding local maxima and minima of multivariable equations?

Hint -- when thinking about multivariable functions, I like to think of a 3-D plot of a function z = f(x,y). Picture a 3-D surface running through the x-y-z cube. Then think about when there are bumps and such in that surface -- how do you find where they are?
 
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It might help to recall that a graph of a quadratic function is a parabola.

Do you remember completing the square to find the vertex of a parabola?
 

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