Math 2.0

statement theorem

Orthogonal projection is the closest point

Statement

Let VV be an inner product space over R\R or C\C, let WW be a finite-dimensional subspace of VV, and let v∈Vv \in V. Let PWP_W be the orthogonal projection onto WW. Then, for all w∈Ww \in W, ∥v−PW(v)∥≤∥v−w∥,\norm{v - P_W(v)} \le \norm{v - w}, with equality only for w=PW(v)w = P_W(v).

Remarks

The proof: v−w=(v−PW(v))+(PW(v)−w)v - w = (v - P_W(v)) + (P_W(v) - w) is a sum of orthogonal vectors, so the Pythagorean theorem applies. The number ∥v−PW(v)∥\norm{v - P_W(v)} is the distance from vv to WW. 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.

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  1. Vector space definition
  2. Inner product definition
  3. Linear map definition
  4. Linear subspace definition
  5. Norm definition
  6. Inner product space definition
  7. Orthogonal vectors definition
  8. Endomorphism definition
  9. Sum of subspaces definition
  10. Norm induced by an inner product definition
  11. Orthogonal complement definition
  12. Direct sum of subspaces definition
  13. A space is the direct sum of a subspace and its orthogonal complement statement
  14. Projection definition
  15. Orthogonal projection definition
  16. Orthogonal projection is the closest point statement

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Sources

  1. Web https://en.wikipedia.org/wiki/Hilbert_projection_theorem, Finite dimensional case