statement theorem
Orthogonal projection is the closest point
Statement
Let be an inner product space over or , let be a finite-dimensional subspace of , and let . Let be the orthogonal projection onto . Then, for all , with equality only for .
Remarks
The proof: is a sum of orthogonal vectors, so the Pythagorean theorem applies. The number is the distance from to . The Hilbert projection theorem gives the same result for closed convex sets in Hilbert spaces. This is the basis of the method of least squares.
Explanations
L1 human
The nearest point of a subspace to a given vector is the foot of the perpendicular. Every other point of the subspace is farther away, by the Pythagorean theorem. This turns problems of best approximation into problems of projection.
What this node 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 node is. They do not measure its truth.
What this needs
To read this node, read these 16 nodes in this order. The foundations come first, and the list ends with this node.
- no evidence level
- Vector space definition
- Inner product definition
- Linear map definition
- Linear subspace definition
- Norm definition
- Inner product space definition
- Orthogonal vectors definition
- Endomorphism definition
- Sum of subspaces definition
- Norm induced by an inner product definition
- Orthogonal complement definition
- Direct sum of subspaces definition
- A space is the direct sum of a subspace and its orthogonal complement statement
- Projection definition
- Orthogonal projection definition
- Orthogonal projection is the closest point statement
What depends on this
1 node uses this node, directly or through other nodes.
- Least squares 0BYTQ4X1HC
Sources
- Web https://en.wikipedia.org/wiki/Hilbert_projection_theorem, Finite dimensional case
What links here
Used by
- Least squares 0BYTQ4X1HC