Gradient points in the direction of steepest ascent
Builds on GradientDirectional derivativeDerivative gives the directional derivatives
Statement
Let be open, let be differentiable at , and suppose that . Then for each unit vector , the directional derivative satisfies . The upper bound is attained if and only if , and the lower bound if and only if .
Remarks
The proof combines , from the statement that the derivative gives the directional derivatives, with the Cauchy–Schwarz inequality and its case of equality. So the gradient gives the direction of fastest increase, and its norm is the fastest rate of increase per unit of distance. The method of gradient descent uses the opposite direction to look for a minimum of .
Reading path
To understand this page from the ground up, read these 11 pages in order. The foundations come first.
Show the path as a graph
- no evidence level
- Euclidean space object
- Function concept
- Having a directional derivative definition
- Function of several variables concept
- Limit of a function of several variables concept
- Partial derivative concept
- Differentiable function of several variables definition
- Directional derivative definition
- Derivative gives the directional derivatives statement
- Gradient definition
- Gradient points in the direction of steepest ascent statement
Further reading
- Web https://en.wikipedia.org/wiki/Gradient, Gradient is direction of steepest ascent
About this page
| Tag | 0ZC78099PA |
|---|---|
| URI | m2://0ZC78099PA |
| Kind | statement, proposition |
| Writing | W0 stub |
| MSC | 26B12 |
| Status | This copy of the site has no build data. Run m2 status 0ZC78099PA. |
| Uses | Gradient, Directional derivative, Derivative gives the directional derivatives |
Markdown · JSON · Edit on GitHub
What this page needs for W1
- an inventory row: the row of the inventory that the node accounts for (0 of 1)
The writing levels say how complete the text of a page is. They do not measure its truth.
What links here
No page links to this page yet.