linear algebra a gentle introduction
DESCRIPTION
Linear Algebra A gentle introduction. Linear Algebra has become as basic and as applicable as calculus, and fortunately it is easier. --Gilbert Strang, MIT. What is a Vector ?. Think of a vector as a directed line segment in N-dimensions ! (has “length” and “direction”) - PowerPoint PPT PresentationTRANSCRIPT
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
1 : “shiv rpi”
Linear Algebra A gentle introduction
Linear Algebra has become as basic and as applicable as calculus, and fortunately it is easier.
--Gilbert Strang, MIT
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
2 : “shiv rpi”
What is a Vector ? Think of a vector as a directed line
segment in N-dimensions! (has “length” and “direction”)
Basic idea: convert geometry in higher dimensions into algebra! Once you define a “nice” basis along
each dimension: x-, y-, z-axis … Vector becomes a N x 1 matrix! v = [a b c]T
Geometry starts to become linear algebra on vectors like v!
c
b
a
v
x
y
v
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
3 : “shiv rpi”
Vector Addition: A+B
A
B
A
B
C
A+B = C(use the head-to-tail method
to combine vectors)
A+B
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
4 : “shiv rpi”
Scalar Product: av
),(),( 2121 axaxxxaa v
vv
avav
Change only the length (“scaling”), but keep direction fixed.
Sneak peek: matrix operation (Av) can change length, direction and also dimensionality!
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
5 : “shiv rpi”
Vectors: Dot Product
T
d
A B A B a b c e ad be cf
f
2 TA A A aa bb cc
)cos(BABA
Think of the dot product as a matrix multiplication
The magnitude is the dot product of a vector with itself
The dot product is also related to the angle between the two vectors
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
6 : “shiv rpi”
Inner (dot) Product: v.w or wTv
vv
ww
22112121 .),).(,(. yxyxyyxxwv
The inner product is a The inner product is a SCALAR!SCALAR!
cos||||||||),).(,(. 2121 wvyyxxwv
wvwv 0.
If vectors v, w are “columns”, then dot product is wTv
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
7 : “shiv rpi”
Projection: Using Inner Products (I)
p = a (aTx)||a|| = aTa = 1
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
8 : “shiv rpi”
Bases & Orthonormal Bases
Basis (or axes): frame of reference
vs
Basis: a space is totally defined by a set of vectors – any point is a linear combination of the basis
Ortho-Normal: orthogonal + normal
[Sneak peek: Orthogonal: dot product is zeroNormal: magnitude is one ]
0
0
0
zy
zx
yx T
T
T
z
y
x
100
010
001
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
9 : “shiv rpi”
What is a Matrix?
A matrix is a set of elements, organized into rows and columns
dc
barows
columns
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
10 : “shiv rpi”
Basic Matrix Operations Addition, Subtraction, Multiplication: creating new matrices (or functions)
hdgc
fbea
hg
fe
dc
ba
hdgc
fbea
hg
fe
dc
ba
dhcfdgce
bhafbgae
hg
fe
dc
ba
Just add elements
Just subtract elements
Multiply each row by each column
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
11 : “shiv rpi”
Matrix Times Matrix
NML
333231
232221
131211
333231
232221
131211
333231
232221
131211
nnn
nnn
nnn
mmm
mmm
mmm
lll
lll
lll
3 21 32 21 21 21 11 2 nmnmnml
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
12 : “shiv rpi”
Multiplication Is AB = BA? Maybe, but maybe not!
Matrix multiplication AB: apply transformation B first, and then again transform using A!
Heads up: multiplication is NOT commutative!
Note: If A and B both represent either pure “rotation” or “scaling” they can be interchanged (i.e. AB = BA)
......
...bgae
hg
fe
dc
ba
......
...fcea
dc
ba
hg
fe
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
13 : “shiv rpi”
Matrix operating on vectors Matrix is like a function that transforms the vectors on a plane Matrix operating on a general point => transforms x- and y-components System of linear equations: matrix is just the bunch of coeffs !
x’ = ax + by y’ = cx + dy
'
'
y
x
dc
ba
y
x
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
14 : “shiv rpi”
Direction Vector Dot Matrix
cbav zyx vvv
0 0 0 1 1
x x x x x
y y y y y
z z z z z
x x x y x z x
y x y y y z y
z x z y z z z
a b c d v
a b c d v
a b c d v
v v a v b v c
v v a v b v c
v v a v b v c
v M v
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
15 : “shiv rpi”
Inverse of a Matrix Identity matrix:
AI = A Inverse exists only for square
matrices that are non-singular Maps N-d space to another
N-d space bijectively Some matrices have an
inverse, such that:AA-1 = I
Inversion is tricky:(ABC)-1 = C-1B-1A-1
Derived from non-commutativity property
100
010
001
I
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
16 : “shiv rpi”
Determinant of a Matrix Used for inversion If det(A) = 0, then A has no inverse
dc
baA bcadA )det(
ac
bd
bcadA
11
http://www.euclideanspace.com/maths/algebra/matrix/functions/inverse/threeD/index.htm
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
17 : “shiv rpi”
Transpose of a Matrix
Written AT (transpose of A)
Keep the diagonal but reflect all other elements about the diagonal
aij = aji where i is the row and j the column in this example, elements c and b were exchanged For orthonormal matrices A-1 = AT
dc
baA T a c
Ab d
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
18 : “shiv rpi”
Vectors: Cross Product The cross product of vectors A and B is a vector C which is
perpendicular to A and B The magnitude of C is proportional to the sin of the angle
between A and B The direction of C follows the right hand rule if we are
working in a right-handed coordinate system
)sin(BABA B
A
A×B
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
19 : “shiv rpi”
MAGNITUDE OF THE CROSS PRODUCT
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
20 : “shiv rpi”
DIRECTION OF THE CROSS PRODUCT
The right hand rule determines the direction of the cross product
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
21 : “shiv rpi”
For more details Prof. Gilbert Strang’s course videos: http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/V
ideoLectures/index.htm
Esp. the lectures on eigenvalues/eigenvectors, singular value decomposition & applications of both. (second half of course)
Online Linear Algebra Tutorials: http://tutorial.math.lamar.edu/AllBrowsers/2318/2318.asp