Gradient Vector Proof for Local Minimizer: f(x)=0, Df(x)=0 | R^n --> R

In summary: you define a line with that parameter t, and since, as you say, 0 is an extreme point of the line, the derivative along the line must be zero.
  • #1
bubblesewa
4
0

Homework Statement



Suppose that the function f: Rn --> R has first-order partial derivatives and that the point x in Rn is a local minimizer for f: Rn --> R, meaning that there is a positive number r such that
f(x+h) > f(x) if dist(x,x+h) < r.
Prove that Df(x)=0.

Homework Equations



Df(x)=(df/dx1,df/dx2,...,df/dxn)

The Attempt at a Solution



We know that the function has first-order partial derivatives, which makes finding the gradient vector possible. And the definition for local minimizer is already given in the problem. I just need to prove that all partial derivatives are equal to zero. But how does knowing the local minimizer help me figure out the gradient vector?
 
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  • #2
Welcome to PF!

bubblesewa said:
Suppose that the function f: Rn --> R has first-order partial derivatives and that the point x in Rn is a local minimizer for f: Rn --> R, meaning that there is a positive number r such that
f(x+h) > f(x) if dist(x,x+h) < r.
Prove that Df(x)=0.

I just need to prove that all partial derivatives are equal to zero. But how does knowing the local minimizer help me figure out the gradient vector?

Hi bubblesewa! Welcome to PF! :smile:

Find the directional derivative of f along each coordinate axis (keeping al the other coordinates constant) :wink:
 
  • #3
Is this about what my proof should look like then? And thanks for welcoming me btw.

Since x is an interior point of Rn, we can choose a positive number r such that the open ball Br(x) is contained in Rn. Fix an index i with 1 < i < n. Then, do I suppose that I have some function, let's say q(t). Where q(t) = f(x+th) for |t| < r. Then the point 0 is an extreme point of the function q: (-r,r) --> R, so q'(0) = (df/dxi)(x) = 0.
 
  • #4
bubblesewa said:
Then, do I suppose that I have some function, let's say q(t). Where q(t) = f(x+th) for |t| < r. Then the point 0 is an extreme point of the function q: (-r,r) --> R, so q'(0) = (df/dxi)(x) = 0.

Hi bubblesewa! :smile:

Yes, in principle that's right …

you define a line with that parameter t, and since, as you say, 0 is an extreme point of the line, the derivative along the line must be zero. :smile:

However, you haven't yet defined h so as to get the line that gives you ∂f/∂xi, have you? :wink:
 

FAQ: Gradient Vector Proof for Local Minimizer: f(x)=0, Df(x)=0 | R^n --> R

What is a gradient vector proof?

A gradient vector proof is a mathematical method used to prove the existence of a gradient vector for a given function. The gradient vector represents the direction and magnitude of the steepest increase of a function at a specific point.

How is a gradient vector calculated?

The gradient vector is calculated by taking the partial derivative of a function with respect to each of its independent variables. The resulting vector is then composed of the partial derivatives as its components.

Why is the gradient vector important?

The gradient vector is important because it provides information about the rate of change of a function at a specific point. It can also be used to find the direction in which a function will increase or decrease the fastest.

What is the relationship between the gradient vector and level curves?

The gradient vector is always perpendicular to the level curves of a function. This means that the direction of the gradient vector is always tangent to the level curve at a specific point.

How is the gradient vector used in optimization problems?

The gradient vector is used in optimization problems to find the minimum or maximum of a function. By setting the gradient vector equal to zero, the critical points of the function can be found and used to determine the optimal solution.

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