Understanding Saddle Points in Partial Differentiation - Explained with Examples

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In summary, a saddle point is a type of stationary point in a function of two variables that is neither a maximum nor a minimum point. It is also known as a point of inflection. For a function of one variable, a saddle point is equivalent to an inflection point, but for a function of two variables, the two terms are not interchangeable.
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ZedCar
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I'm working through a partial differentiation problem, to which I have the answer.

The function being 4x^2+4xy-y^3-2x+2 to which the stationary points must be obtained and classified.

At the end of the working out, when partial differentiation is conducted and a number of -32 is obtained, it states that because this is a negative number this is a "saddle point". Is this another term for a "point of inflection"?

How can they make this claim? I thought since the number obtained is negative it would therefore be a maximum turning point.

The second number obtained with the second partial differentiation is 32, and it states since this is a positive number this is a minimum turning point; which I understand.

Thank you.
 
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  • #3
ZedCar said:
I'm working through a partial differentiation problem, to which I have the answer.

The function being 4x^2+4xy-y^3-2x+2 to which the stationary points must be obtained and classified.

At the end of the working out, when partial differentiation is conducted and a number of -32 is obtained, it states that because this is a negative number this is a "saddle point". Is this another term for a "point of inflection"?
Just to add to the other reply. For a function of one variable a saddle point is a point of inflection, but an inflection point need not be a saddle point since the slope need not be zero.

For a function of two variables a saddle point is a stationary point that is neither a max nor a min point. The term "inflection point" is not used for functions of two variables that I know of.
 
  • #4
Thanks very much for the info guys!. Much appreciated, Thank you.
 

What is a saddle point?

A saddle point is a point on a surface where the surface curves up in one direction and down in another direction, resembling the shape of a saddle. It is a critical point where the gradient is equal to zero, but the second derivative test shows that it is neither a maximum nor a minimum.

How is a saddle point different from a local maximum or minimum?

A saddle point differs from a local maximum or minimum because it is a critical point where the surface curves in two opposite directions. In contrast, a local maximum or minimum is a critical point where the surface only curves in one direction.

What is the significance of saddle points in mathematics and science?

Saddle points are important in mathematics and science because they can help us determine the behavior of a surface or function. They can also provide insight into the stability of systems and the existence of equilibrium points.

Can saddle points exist in higher dimensions?

Yes, saddle points can exist in higher dimensions. In fact, they are commonly used in optimization and machine learning algorithms to find the minimum or maximum of a multi-dimensional function.

How are saddle points used in real-world applications?

Saddle points have various applications in real-world problems, such as in economics, physics, and engineering. For example, they can be used to analyze the stability of a financial system or to optimize the design of a structure. They are also important in understanding the behavior of fluids and electric fields.

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