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Math429Linear Algebra (Lecture 4)

Lecture 4

Office hour after lecture: Cupules I 109

Chapter II Finite Dimensional Subspaces

Span and Linear Independence 2A

Definition 2.2

Linear combination

Given a list (a finite list), of F\mathbb{F} vectors v1,...,vm\vec{v_1},...,\vec{v_m}. A linear combination of v1,...,vm\vec{v_1},...,\vec{v_m} is a vector v=a1v1+a2v2+...+amvm,aiF\vec{v}=a_1\vec{v_1}+a_2\vec{v_2}+...+a_m\vec{v_m},a_i\in \mathbb{F} (Adding vectors with different weights)

Definition 2.4

Span

The set of all linear combinations of v1,...,vm\vec{v_1},...,\vec{v_m} is called the span of {v1,...,vm}\{\vec{v_1},...,\vec{v_m}\}

Span {v1,...,vm}={vV,v=a1v1+a2v2+...+amvm for some aiF}\{\vec{v_1},...,\vec{v_m}\}=\{\vec{v}\in V, \vec{v}=a_1\vec{v_1}+a_2\vec{v_2}+...+a_m\vec{v_m}\textup{ for some }a_i\in \mathbb{F}\}

Note: When there is a nonzero vector in {v1,...,vm}\{\vec{v_1},...,\vec{v_m}\}, the span is a infinite set.

Example:

Consider V=R3V=\mathbb{R}^3, find the span of the vector {(1,2,3),(1,1,1)}\{(1,2,3),(1,1,1)\},

The span is {a1(1,2,3),a2(1,1,1):a1,a2R}={(a1+a2,2a1+a2,3a1+a2):a1,a2R}\{a_1\cdot (1,2,3),a_2\cdot (1,1,1):a_1,a_2\in \mathbb{R}\}=\{(a_1+a_2,2a_1+a_2,3a_1+a_2):a_1,a_2\in \mathbb{R}\}

(1,0,1)Span((1,2,3),(1,1,1))(-1,0,1)\in Span((1,2,3),(1,1,1))

(1,0,1)Span((1,2,3),(1,1,1))(1,0,1)\cancel{\in} Span((1,2,3),(1,1,1))

Theorem 2.6

The span of a list of vectors in VV is the smallest subspace of VV containing this list.

Proof:

  1. Span is a subspace

    Span{v1,...,vm}={a1v1+a2v2+...+amvm for some aiF}Span\{\vec{v_1},...,\vec{v_m}\}=\{a_1\vec{v_1}+a_2\vec{v_2}+...+a_m\vec{v_m}\textup{ for some }a_i\in \mathbb{F}\}

    • The zero vecor is inside the span by letting all the ai=0a_i=0
    • Closure under addition: a1v1+a2v2+...+amvm+b1v1+b2v2+...+bmvm=(a1+b1)v1+(a2+b2)v2+...+(am+bm)vmSpan{v1,...,vm}a_1\vec{v_1}+a_2\vec{v_2}+...+a_m\vec{v_m}+b_1\vec{v_1}+b_2\vec{v_2}+...+b_m\vec{v_m}=(a_1+b_1)\vec{v_1}+(a_2+b_2)\vec{v_2}+...+(a_m+b_m)\vec{v_m}\in Span\{\vec{v_1},...,\vec{v_m}\}
    • Closure under multiplication: c(a1v1+a2v2+...+amvm)=(ca1)v1+(ca2)v2+...+(cam)vmSpan{v1,...,vm}c(a_1\vec{v_1}+a_2\vec{v_2}+...+a_m\vec{v_m})=(ca_1)\vec{v_1}+(ca_2)\vec{v_2}+...+(ca_m)\vec{v_m}\in Span\{\vec{v_1},...,\vec{v_m}\}
  2. Span is the smallest subspace containing the given list.

    For each i{1,...,m}i\in\{1,...,m\}, vi=0v1+...+0vi1+vi+0vi+1+...+0vmSpan{v1,...,vm}\vec{v_i}=0\vec{v_1}+...+0\vec{v_{i-1}}+\vec{v_i}+0\vec{v_{i+1}}+...+0\vec{v_m}\in Span\{\vec{v_1},...,\vec{v_m}\}

    If WW is a subspace of VV containing Span{v1,...,vm}Span\{\vec{v_1},...,\vec{v_m}\}, then WW is closed under addition and scalar multiplication.

    Thus for any a1,...,amF,a1v1+a2v2+...+amvmWa_1,...,a_m\in \mathbb{F},a_1\vec{v_1}+a_2\vec{v_2}+...+a_m\vec{v_m}\in W. So Span{v1,...,vm}WSpan\{\vec{v_1},...,\vec{v_m}\}\subset W

Definition 2.ex.1

Spanning set

If a vector space V=Span{v1,...,vm}V=Span\{\vec{v_1},...,\vec{v_m}\}, then we say {v1,...,vm}\{\vec{v_1},...,\vec{v_m}\} spans VV, which is the spanning set of VV.

A vector space is called finite dimensional if it spanned by a finite list.

Example:

Fn\mathbb{F}^n is finite dimensional

R=Span{(1,0,0),(0,1,0),(0,0,1)}\mathbb{R}=Span\{(1,0,0),(0,1,0),(0,0,1)\}

(a,b,c)=a(1,0,0)+b(0,1,0)+c(0,0,1)(a,b,c)=a(1,0,0)+b(0,1,0)+c(0,0,1)

Definition

Polynomial

A polynomial is a function p:FFp:\mathbb{F}\to \mathbb{F} such that p(Z)=i=0maizi,aiFp(Z)=\sum_{i=0}^{m} a_i z^i,a_i\in \mathbb{F}

Let P(F)\mathbb{P}(\mathbb{F}) be the set of polynomials over F\mathbb{F}, then P(F)\mathbb{P}(\mathbb{F}) has the structure of a vector space.

If we consider the degree of polynomials, then f=a1f1+...+amfmf=a_1f_1+...+a_mf_m, with degree fmax{deg(f1,...,fm)}f\leq max\{deg(f_1,...,f_m)\}

P(F)\mathbb{P}(\mathbb{F}) is a infinite dimensional vector space.

Let Pm(F)\mathbb{P}_m(\mathbb{F}) be the set of polynomials of degree at mote mm, then Pm(F)\mathbb{P}_m(\mathbb{F}) is a finite dimensional vectro space.

Pm(F)=Span{1,z,z2,...zm}\mathbb{P}_m(\mathbb{F})=Span\{1,z,z^2,...z^m\}

Linear independence

How to find a “good” spaning set for a finite dimensional vector space.

Example:

V=R2V=\mathbb{R^2}

R2=Span{(1,0),(0,1)}\mathbb{R^2}=Span\{(1,0),(0,1)\}

R2=Span{(1,0),(0,1),(0,0),(1,1)}\mathbb{R^2}=Span\{(1,0),(0,1),(0,0),(1,1)\}

R2=Span{(1,2),(3,1),(4,25)}\mathbb{R^2}=Span\{(1,2),(3,1),(4,25)\}

Definition 2.15

A list of vector v1,...,vm\vec{v_1},...,\vec{v_m} in VV is called linearly independent if the only choice for a1,...,amFa_1,...,a_m\in \mathbb{F} such that a1v1+...+amvm=0a_1\vec{v_1}+...+a_m\vec{v_m}=\vec{0} is a1=...=am=0a_1=...=a_m=0

If not, then there must vi\exists\vec{v_i} that can be expressed by other vectors in the set.

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