{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

la11-arips - LINEAR ALGEBRA W W L CHEN c W W L Chen 1997...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
LINEAR ALGEBRA W W L CHEN c W W L Chen, 1997, 2005. This chapter is available free to all individuals, on the understanding that it is not to be used for financial gain, and may be downloaded and/or photocopied, with or without permission from the author. However, this document may not be kept on any information storage and retrieval system without permission from the author, unless such system is not accessible to any individuals other than its owners. Chapter 11 APPLICATIONS OF REAL INNER PRODUCT SPACES 11.1. Least Squares Approximation Given a continuous function f : [ a, b ] R , we wish to approximate f by a polynomial g : [ a, b ] R of degree at most k , such that the error b a | f ( x ) g ( x ) | 2 d x is minimized. The purpose of this section is to study this problem using the theory of real inner product spaces. Our argument is underpinned by the following simple result in the theory. PROPOSITION 11A. Suppose that V is a real inner product space, and that W is a finite-dimensional subspace of V . Given any u V , the inequality u proj W u u w holds for every w W . In other words, the distance from u to any w W is minimized by the choice w = proj W u , the orthogonal projection of u on the subspace W . Alternatively, proj W u can be thought of as the vector in W closest to u . Proof of Proposition 11A. Note that u proj W u W and proj W u w W. Chapter 11 : Applications of Real Inner Product Spaces page 1 of 11
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Linear Algebra c W W L Chen, 1997, 2005 It follows from Pythagoras’s theorem that u w 2 = ( u proj W u ) + (proj W u w ) 2 = u proj W u 2 + proj W u w 2 , so that u w 2 u proj W u 2 = proj W u w 2 0 . The result follows immediately. Let V denote the vector space C [ a, b ] of all continuous real valued functions on the closed interval [ a, b ], with inner product f, g = b a f ( x ) g ( x )d x. Then b a | f ( x ) g ( x ) | 2 d x = f g, f g = f g 2 . It follows that the least squares approximation problem is reduced to one of finding a suitable polynomial g to minimize the norm f g . Now let W = P k [ a, b ] be the collection of all polynomials g : [ a, b ] R with real coefficients and of degree at most k . Note that W is essentially P k , although the variable is restricted to the closed interval [ a, b ]. It is easy to show that W is a subspace of V . In view of Proposition 11A, we conclude that g = proj W f gives the best least squares approximation among polynomials in W = P k [ a, b ]. This subspace is of di- mension k +1. Suppose that { v 0 , v 1 , . . . , v k } is an orthogonal basis of W = P k [ a, b ]. Then by Proposition 9L, we have g = f, v 0 v 0 2 v 0 + f, v 1 v 1 2 v 1 + . . . + f, v k v k 2 v k . Example 11.1.1. Consider the function f ( x ) = x 2 in the interval [0 , 2]. Suppose that we wish to find a least squares approximation by a polynomial of degree at most 1. In this case, we can take V = C [0 , 2], with inner product f, g = 2 0 f ( x ) g ( x )d x, and W = P 1 [0 , 2], with basis { 1 , x } . We now apply the Gram-Schmidt orthogonalization process to this basis to obtain an orthogonal basis { 1 , x 1 } of W , and take g = x 2 , 1 1 2 1 + x 2 , x 1 x 1 2 ( x 1) .
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}