Proof: Sufficient condition for local extreme existence

In summary, the proof uses the Taylor expansion to show that if the second derivative of a function at a point is positive definite, then the function has a local minimum at that point.
  • #1
twoflower
368
0
Let [tex]
G \subset \mathbb{R}^{n}\mbox{ open }
[/tex]

[tex]
a \in G
[/tex]

[tex]
f : G \rightarrow \mathbb{R}
[/tex]

[tex]
f \in C^{1}(G)
[/tex]

[tex]
Df(a) = \overrightarrow{0}
[/tex]

Then:

(i) if [itex]D^2f(a)[/itex] is positively definite, then f has local minimum in [itex]a[/itex]

(ii) if [itex]D^2f(a)[/itex] is negatively definite, then f has local maximum in [itex]a[/itex]

(iii) if [itex]D^2f(a)[/itex] is indefinite, then f doesn't have extreme in [itex]a[/itex]


Unfortunately we weren't given an entire proof at the lecture, but here's what we had been told:

Hint of the proof:

(i) First step: Won't tell you neither examinate it.

[tex]
D^2f(a)\mbox{ P.D } \Rightarrow \exists \mbox{ neighbourhood of } a \mbox{ on which it is P.D} \Rightarrow
[/tex]

[tex]
\exists\ \xi > 0:\ \ Df(x)(h,h) \geq \xi \parallel h \parallel^2 \forall x \mbox{ from this neighbourhood }
[/tex]

Then

[tex]
\forall x \mbox{ in this neighbourhood } \exists\ \gamma \in (0,1) \mbox{ such that }
[/tex]

[tex]
\mbox{(*) } f(x)\ -\ f(a)\ -\ Df(a)(x-a) = \frac{1}{2}D^2f(a\ +\ \gamma(x-a))(x-a, x-a) \Rightarrow
[/tex]

[tex]
f(x) = f(a)\ +\ \frac{1}{2}D^2f(a\ +\ \gamma(x-a))(x-a, x-a) > f(a) \Rightarrow \mbox{ local minimum in } a
[/tex]


I don't get the equality denoted by [itex](*)[/itex].

Could someone explain this to me?

Thank you very much.
 
Last edited:
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  • #2


The equality denoted by (*) is the Taylor expansion of a function at a point. It is a way of approximating a function using its derivatives at a specific point. In this case, we are using the Taylor expansion to approximate the function f(x) at a point x in a neighbourhood of a.

The Taylor expansion of a function f(x) at a point a is given by:

f(x) = f(a) + Df(a)(x-a) + \frac{1}{2}D^2f(a)(x-a, x-a) + ...

where Df(a) is the first derivative of f at a, D^2f(a) is the second derivative of f at a, and so on.

In the proof, we are using the Taylor expansion to approximate f(x) at a point x in a neighbourhood of a. This is why we have the term Df(a)(x-a) in the expansion.

The term D^2f(a\ +\ \gamma(x-a))(x-a, x-a) is the second derivative of f at a point a + \gamma(x-a), where \gamma is a small number between 0 and 1. This is because we are approximating f(x) at a point x in a neighbourhood of a, and we are using the second derivative at a point close to a (specifically, a + \gamma(x-a)).

So, the equality denoted by (*) is just the Taylor expansion of f(x) at a point x in a neighbourhood of a. It is a way of approximating f(x) using its derivatives at a specific point.

I hope this explanation helps!
 

1. What is a local extreme in mathematics?

A local extreme is a point on a graph where the function reaches either the highest or lowest value within a small neighborhood of that point.

2. How is a local extreme different from a global extreme?

A global extreme is the highest or lowest value of a function across its entire domain, while a local extreme is the highest or lowest value within a small neighborhood of a specific point on the graph.

3. What is meant by the term "sufficient condition" in this context?

A sufficient condition is a condition that, if met, guarantees the existence of a local extreme. In other words, if the condition is true, then there must be a local extreme present.

4. Are there any other conditions besides sufficiency that are required for the existence of a local extreme?

Yes, there are also necessary conditions that must be met for a local extreme to exist. These conditions are not always sufficient, meaning that they may not guarantee the existence of a local extreme.

5. How is the sufficient condition for local extreme existence used in practical applications?

The sufficient condition is often used in optimization problems, where finding the maximum or minimum value of a function is important. It helps to narrow down the search for possible extreme points, making the problem more manageable.

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