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Definitions and hints for understanding linear combinations, linear independence, column space, and null space in the context of advanced engineering mathematics. It includes examples of how to find linear combinations and determine linear independence, as well as the relationship between these concepts and the column space and null space of a matrix.
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MATH 348 - Advanced Engineering Mathematics April 21, 2008
Homework 9 - Hints
Definition - Linear Combination - Given a set, S, of n vectors,
S = {a 1 , a 2 , a 3 ,... , an}. (1)
We say that b is a linear combination of the vectors from S if there exist scalars x 1 , x 2 , x 3 ,... , xn such that,
b =
∑^ n
i=
xiai. (2)
Definition - Linear Independence - Given a set, S, of n vectors,
S = {v 1 , v 2 , v 3 ,... , vn}. (3)
We say that the vectors of S forms a linearly independent set if and only if the scalars x 1 = x 2 = x 3 = · · · = xn = 0
are the only solution to,
n ∑
i=
xivi. (4)
If a set of vectors does not form a linearly independent set then the vectors are said to be linearly dependent.
Definition - Column Space - Given the matrix A∈ R
m×n we define the set of all linear combinations of the columns
of the matrix A to be the column space of A and we denote this space of vectors as Col(A).
Definition - Null Space - Given the matrix A∈ R
m×n we define the set of vectors x, which satisfies the equation
Ax = 0 to be the Null Space of A and we denote this space of vectors Nul(A).
Note - Straightforward calculations will verify that,
∑^ n
i=
xiai = x 1
a 11
a 21
a 31
. . .
am 1
a 12
a 22
a 32
. . .
am 2
a 13
a 23
a 33
. . .
am 3
a 1 n
a 2 n
a 3 n
. . .
amn
= Ax (5)
Hints:
independent set.
about w?