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Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

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Page 1: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

Section 6.3

Orthonormal Bases;Gram-Schmidt Process;QR-Decomposition

Page 2: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

ORTHONORMAL SETS OF VECTORS

A set of vectors in an inner product space is called an orthogonal set if all pairs of distinct vectors in the set are orthogonal. An orthogonal set in which each vector has norm 1 is called orthonormal.

Page 3: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

ORTHOGONAL AND ORTHONORMAL BASES

• In an inner product space, a basis consisting of orthogonal vectors is called an orthogonal basis.

• In an inner product space, a basis consisting of orthonormal vectors is called an orthonormal basis.

Page 4: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

COORDINATES RELATIVE TO AN ORTHONORMAL BASES

Theorem 6.3.1: If S = {v1, v2, . . . , vn} is an orthonormal basis for an inner product space V, and if u is any vector in V, then

u = ‹u, v1› v1 + ‹u, v2› v2 + . . . + ‹u, vn› vn

Page 5: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

THEOREMTheorem 6.3.2: If S is an orthonormal basis for an n-dimensional inner product space, and if

(u)S = (u1, u2, . . . , un) and (v)S = (v1, v2, . . . , vn)

then:

nn

nn

n

vuvuvu

vuvuvud

uuu

2211

2222

211

222

21

,)c(

)()()(),()b(

||||)a(

vu

vu

u

Page 6: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

THEOREM

Theorem 6.3.3: If S = {v1, v2, . . . , vn} is an orthogonal set of nonzero vectors in an inner product space, then S is linearly independent.

Page 7: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

THE PROJECTION THEOREM

Theorem 6.3.4: If W is a finite-dimensional subspace of an inner product space V, then every vector u in V can be expressed inexactly one way as

u = w1 + w2

where w1 is in W and w2 in W┴.

NOTE: w1 is called the orthogonal projection of u on W and is denoted by projW u. The vector w2 is called the complement of u orthogonal to W and is denoted by projW┴ u.

Page 8: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

THEOREM

Theorem 6.3.5: Let W be a finite-dimensional subspace of an inner product space V.

(a) If {v1, v2, . . . , vr} is an orthonormal basis for W, and u is any vector in V, then

projW u = ‹u, v1› v1 + ‹u, v2› v2 + . . . + ‹u, vr› vr

(b) If {v1, v2, . . . , vr} is an orthogonal basis for W, and u is any vector in V, then

rr

rW v

v

vuv

v

vuv

v

vuu

2222

212

1

1

||||

,

||||

,

||||

,proj

Page 9: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

THEOREM

Theorem 6.3.6: Every nonzero finite-dimensional inner product space has an orthonormal basis.

Page 10: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

Let V be a finite-dimensional inner product space, with a basis

{u1, u2, . . . , un}

These steps produce an orthogonal basis.

STEP 1: Let v1 = u1.

STEP 2: .Construct the vector v2 orthogonal to v1 by finding the complement of u2 that is orthogonal to W1 = span{v1}.

STEP 3: Construct the vector v3 which is orthogonal to v1 and v2 by constructing the complement of u3 that is orthogonal to W2 = span{v1, v2}.

THE GRAM-SCHMIDT PROCESS

121

122222 ||||

,proj

1v

v

vuuuuv W

222

2312

1

133333 ||||

,

||||

,proj

2v

v

vuv

v

vuuuuv W

Page 11: Section 6.3 Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition

G-S PROCESS (CONCLUDED)

STEP 4: Construct the vector v4 which is orthogonal to v1, v2, v3 by constructing the complement of u4 that is

orthogonal to W2 = span{v1, v2, v3}.

Continue the process for n steps until all ui are exhausted.

323

3422

2

2412

1

144444 ||||

,

||||

,

||||

,proj

3v

v

vuv

v

vuv

v

vuuuuv W