Saddle points of functions of n variables

In summary, a saddle point is a stationary point that is not a local minimum or maximum. There is some ambiguity in the definition depending on whether strict or non-strict extrema are implied. However, based on further research, it can be concluded that a saddle point is a critical point that is not a local non-strict extreme point. This means that in the example of the constant function f(x, y) = c, all points in the domain are saddle points. Similarly, for the function f(x, y) = x^2, all points along the line x=0 are saddle points.
  • #1
D_Tr
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Hi,

I need some clarification on exactly which critical points are saddle points. The definition I am finding everywhere is that "A saddle point is a stationary point that is neither a local minimum nor a local maximum." My question is: what kind of minimum and maximum is the above definition about? The strict or the non-strict kind?

In case that strict extrema are implied, then, for example, the constant function f(x, y) = c has a saddle point at each point of its domain, whereas in the case of non-strict extrema, the same function has no saddle points, because at every point it has both a local non-strict minimum and a local non-strict maximum. A similar ambiguity arises in the case of, for example, the function f(x, y) = x^2. Are all points that lie on the x = 0 line saddle points since they are non-strict extrema?

Thanks for reading :)
 
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  • #2
As I understand it, a saddle point is a critical (stationary) point that is not a local (non-strict) extreme point.
So you are correct. In your first example, all points in your domain are saddle points. In your second example all points along the line x=0 are saddle points.
 
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  • #3
Thanks :) After a bit more googling I found https://www.math.okstate.edu/~atovstolis/lecture_notes/LN14_7.pdf lecture notes where first a distinction is drawn between strict and non-strict extrema and then a saddle point is defined as you too understand it. So I guess my question is answered!
 

1. What is a saddle point of a function of n variables?

A saddle point of a function of n variables is a point on the surface of the function where the slope or gradient of the function is zero in one direction and non-zero in another direction. This point is neither a local minimum nor a local maximum, but instead resembles the shape of a saddle.

2. How can saddle points be identified?

In order to identify saddle points of a function of n variables, the gradient or slope of the function must be calculated at various points on the surface. A saddle point will have a gradient of zero in one direction and a non-zero gradient in another direction.

3. What is the significance of saddle points in optimization problems?

Saddle points play a crucial role in optimization problems, as they represent critical points where the function is neither decreasing nor increasing. These points can affect the convergence of optimization algorithms, and their presence must be carefully considered when finding the optimal solution.

4. Can a function have multiple saddle points?

Yes, a function can have multiple saddle points in the case of functions of n variables. These points can be identified by calculating the second-order partial derivatives of the function and determining where they are equal to zero.

5. How do saddle points differ from local minima and maxima?

Saddle points differ from local minima and maxima in that they do not represent the lowest or highest point on the surface of the function. While local minima and maxima have negative and positive curvature, respectively, saddle points have both positive and negative curvature, making them neither a minimum nor a maximum.

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