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1. Vectors in Space 2. Length and Angle Measures II. Linear Geometry of n-Space
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1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Dec 19, 2015

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Page 1: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

1. Vectors in Space

2. Length and Angle Measures

II. Linear Geometry of n-Space

Page 2: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

II.1. Vectors in Space See Apostol, Chap 1.

Descartes: Points in plane ~ ( a1 , a2 ), points in space ~ ( a1 , a2 , a3 ).

Cayley, Grassmann: Points in n-space ~ n-tuples ~ ( a1 , a2 , …, an ) n.

Bound vector: a = ( a1 , a2 , …, an ) = arrow from origin to point a.

Vector algebra in n :For all a, b, c n and α , 1. Vector equality: a = b ai = bi i = 1, …, n.2. Vector addition: c = a + b ci = ai + bi

3. Scalar Multiplication: a = αb ai = α bi

Parallelogram rule:

Closure: n is closed under all linear combinations of its elements:i nv R i

i

i nc v R ic R n is a linear space

Page 3: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Free vector: Object with a magnitude & a direction.

Arrow from point to point .ab

a b

ab

b a 1 1 , , n nb a b a

2 parallel arrows of the same length represent the same free vector.

bound vector of components bi ai .

Comment:

• Free vectors can be defined only in a linear (flat) space.

• In a (non-linear) curved space, parallelism must be defined explicitly.

( Each point has its own linear vector space. )

• Riemannian geometry.

• Differential geometry.

Page 4: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

n is isomorphic to the space Mn1 of n1 column matrices.

Definition: 2 spaces are isomorphic if there exists a bijection (1-1 onto mapping) between them that preserves the algebraic structures.

1, , nna a a R

11n

n

a

M

a

a

1 1, , nn na b a b a b R

1 11n

n n

a b

M

a b

a b

11n

n

a

M

a

a 1, , nna a a R

i ia b i a b

A real vector space is any space ( V, + ; ) isomorphic to n .

Page 5: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Group Isomorphism

I = [ 0, 2π) cos sin; 0,2

sin cos

RR

{ I, } is a group if we define as 2a b a b Mod

{ , } is a group if we define as the matrix multiplication

1 2 1 2 R R R R

{ I, } & { , } are isomorphic with f : I → by θ R(θ)

Let { A, *} & {B, } be 2 groups.

They are isomorphic if there exists a 1-1 onto mapping f s.t.

f(a) = b where aA, bB

Furthermore, f preserves the group operations, i.e.,

a1 * a2 = a3 f( a1 ) f( a2 ) = f( a3 )

Example

Page 6: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Analytic Geometry

Definition: Line

A line spanned by a is the set of points

L( 0 ; a ) = { t a | t } [ L(0 ; a ) passes through 0 ]

A line through point p & parallel to that spanned by a is the set of points

L( p ; a ) = { p + t a | t }

Corollary: A line running through points p & q is the set of points

L( p ; qp ) = { p + t (qp ) | t }

Example: Line through points (1,2) & (3,1) in 2.

1 3 1

2 1 2L t t

R

1 2

2 1t t

R

Line through points (1,2) & parallel to (2,1).

1 2

2

tt

t

R

Page 7: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Definition: Plane

A plane spanned by a and b is the set of points

P( 0 ; a, b ) = { s a + t b | s, t } [ P(0 ; a, b ) passes through 0 ]

A plane through point p and parallel to that spanned by a and b is the set of points

P( p ; a, b ) = { p + s a + t b | s, t }

Corollary: A plane running through points p , q & r is the set of points

P( p ; qp , rp ) = { p + s (qp ) + t (rp ) | s, t }

Example: Plane through points (1, 0, 5), (2, 1, 3), & (2, 4, 0.5) in 3.

1 2 1 2 1

0 1 0 4 0 ,

5 3 5 0.5 5

P s t s t

R

1 1 3

0 1 4 ,

5 8 4.5

s t s t

R

Page 8: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Example: Plane given by 2x + y + z = 4 in 3.

Same as plane through points (2, 0, 0), (0, 4, 0), & (0, 0, 4) in 3.

2 0 2 0 2

0 4 0 0 0 ,

0 0 0 4 0

P s t s t

R

2 2 2

0 4 0 ,

0 0 4

s t s t

R

2 1 1

0 2 0 ,

0 0 2

s t s t

R

Page 9: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Exercises 1.II.1.

1. (a) Describe the plane through (1, 1, 5,1), (2, 2, 2, 0), and (3, 1, 0, 4). (b) Is the origin in that plane?2. A person traveling eastward at a rate of 3 miles per hour finds that the wind

appears to blow directly from the north. On doubling his speed it appears to come from the north east. What was the wind’s velocity?

3. Intersect these planes:

1 0

1 1 ,

1 3

s t s t

R

1 0 2

1 3 0 ,

0 0 4

m n m n

R

Page 10: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

II.2. Length and Angle Measures

Definition 2.1: Euclidean norm (Length)

The Euclidean norm || v || of a vector v n is2 21 nv v v

1/ 2

2

1

n

ii

v

0

Law of Cosines: 2 2 2 2 cosa b c b c

Proof: Pythagorean theorem:

2 22 sin cosa b b c

2 2 2 2sin cos 2 cosb c b c

2 2 2 cosb c b c

Page 11: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Example: Angle between 2 vectors.

Law of Cosines: 2 2 22 cos u v u v u v

2 2 2

1 1

2 cosn n

i i i ii i

u v u v

u v

1

cosn

i ii

u v

u v

11cos

n

i ii

u v

u v

11

2 2

1 1

cos

n

i ii

n n

i ji j

u v

u v

Page 12: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Definition 2.3: Dot/Inner/Scalar Product

1

n

i ii

u v

u v 1 1 n nu v u v

2

1

n

ii

u

u u2 u

1cosu v

u v

2 2 2 cosu v u v u v u v

2u

Law of Cosines:

cosu v u v

1cos

u v

u v

v u

Exercise: Show that

a a a u v u v u v & , na u vR R

( Definition 2.7 )

Page 13: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Theorem 2.5: Triangle Inequality

u v u v u v

Shortest path between 2 points is a straight line.

Geodesics in a Euclidean space are a straight lines.

The sum of 2 edges of a triangle is greater than or equal to its 3rd edge.

Proof: Since all norms are positive, the inequality is equivalent to

22 2u v u v u v

or 2 2u v u v u v u v v u

uv u v ( Cauchy-Schwarz Inequality ; to be proved )

cos 1

Setting v → v gives uv u v cos 1 → cos 1 →

Corollary:

, n u v R

2 2 2u v uv

Page 14: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Theorem 2.6: Cauchy-Schwarz Inequality uv u v u v

Proof:

Let a b z u v where ,a bR

2z a b a b u v u v 2 2 2 2 2a u b v ab u v 0

Let 2a v b u v&

→ 2 24 2 2 22 0v u v v u v u v

22 2v u u v

uv u v

a, b

, n u v R

Definition: Vectors u, v are orthogonal if u · v = 0.

Conclusion: ||w|| 0 u v u v u v u v

Page 15: 1.Vectors in Space 2.Length and Angle Measures II. Linear Geometry of n-Space.

Exercises 1.II.2.