1. Linear Algebra
Lecture 41

2. Linear Algebra
Lecture 41

3. Segment VI
Orthogonality and
Least Squares

4. Gram-Schmidt
Process

5. Construct an orthogonal basis {v1, v2} for W.
Example 1
Let W = Span {x1, x2}, where
Construct an orthogonal basis {v1, v2} for W.

6. Example 2
Then {x1, x2, x3} is linearly independent and thus, a basis for a subspace W of R4. Construct an orthogonal basis for W.

7. Given a basis {x1, …, xp} for a subspace W of Rn , define
Theorem
Given a basis {x1, …, xp} for a subspace W of Rn , define …

8. Continued …

9. Then {v1, …,vp} is an orthogonal basis for W. In addition
Continued
Then {v1, …,vp} is an orthogonal basis for W.
In addition
Span {v1, …, vk}= Span {x1,…, xk} for

10. Orthonormal
Bases

11. Example 3

12. Theorem
If A is an m x n matrix with linearly independent columns, then A can be factored as A = QR …

13. Continued
Where Q is an m x n matrix whose columns form an orthonormal basis for Col A and R is an n x n upper triangular invertible matrix with positive entries on its diagonal.

14. Proof of the
Theorem

15. Find a QR factorization of
Example 4
Find a QR factorization of

16. Construct an orthonormal basis for W.
Example 5
Let W = Span {x1, x2}, where
Construct an orthonormal basis for W.

17. Given a basis {x1, …, xp} for a subspace W of Rn , define
Theorem
Given a basis {x1, …, xp} for a subspace W of Rn , define …

18. Continued …

19. Then {v1, …,vp} is an orthogonal basis for W. In addition
Continued
Then {v1, …,vp} is an orthogonal basis for W.
In addition
Span {v1, …, vk}= Span {x1,…, xk} for

20. Decomposition Theorem
The Orthogonal
Decomposition Theorem
Let W be a subspace of Rn. Then each y in Rn can be written uniquely in the form
where is in W and z is in . …

21. In fact, if {u1, …, up} is any orthogonal basis of W, then
Continued
In fact, if {u1, …, up} is any orthogonal basis of W, then
and z = y – . The vector is called the orthogonal projection of y onto W and often is written as projw y.

22. Best Approximation
Theorem
Let W be a subspace of Rn, y any vector in Rn, and the orthogonal projection of y onto W. Then is the closest point in W to y, in the sense that for all v in W distinct from . …

23. Continued
The vector in this theorem is called the best approximation to y by elements of W.

24. Theorem

25. Linear Algebra
Lecture 41