Linear Algebra Flashcards

1
Q

Cosine Rule

A

c^2 = a^2 + b^2 - 2abcosC

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2
Q

What is a vector?

A

An ordered list of numbers

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3
Q

How to find the magnitude of a vector

A
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4
Q

How to find the direction of a 2D vector

A

θ = arctan(u2/u1)

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5
Q

How to add 2 vectors

A

y + z = (y1 + z1,y2 + z2,…yn + zn)

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6
Q

How to find the distance between 2 vectors

A

The distance between their tips (magnitude of difference)

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7
Q

How is the dot product defined (both ways)

A

u.v=(u1v1 +u2v2 +…+unvn)
u.v = |u| |v| cos θ

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8
Q

The dot product is zero

A

Vectors are orthogonal (perpendicular)

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9
Q

The dot product of a vector with its self

A

u . u = |u|^2 , so 1 if u is a unit vector

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10
Q

Convert a vector into a unit vector

A

Divide it by its scalar magnitude

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11
Q

How to project vector v onto vector u

A

Convert u into a unit vector in both instances (hence the mag squared) and multiply by the dot product

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12
Q

How does a projection (v onto u) work

A

Stretches “u” such that it is at its minimum distance from “v”. (makes a right angle triangle)

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13
Q

What is a unit vector

A

A vector with a magnitude of 1

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14
Q

What are standard basis vectors?

A

The standard unit vectors that can represent any other vector in that dimension, through a linear combination.

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15
Q

How do we represent coordinate spaces

A

2D -> R^2
3D -> R^3
ND -> R^N

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16
Q

What are the 2 ways of finding the normal to a line in 2D

A

1) u . v = 0
2) n . p = d

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17
Q

What are the 3 ways of representing vectors as a line in 2D

A

1) P = P_0 +tu (ND)
2) n . p =d (2D)
3) y = mx + c (2D)

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18
Q

What is the cartesian equation (and formula) for a 2D line

A
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19
Q

What is the cartesian equation for a 3D line

A
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20
Q

How are cicles and spheres defined (vector eq)

A
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21
Q

Why is |p-p0| = r for circles and spheres?

A
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22
Q

What are the 3 ways in which planes can be defined?

A

1) p= p_0 + su + tv
2) n . p = d
3) (n_x) x + (n_y) y + (n_z) z = d

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23
Q

What are the 2 things which the cross product can do?

A

1) Find a othogonal vector to a plane
2) The magnitude is the area of the parallelogram

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24
Q

How to compute cross product

A
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25
Q

How are circles defined (parametric eq)

A

p = r cosθ e1 +r sinθ e2

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26
Q

How to find the normal to a circle / sphere

A

P_1 - P_0 (where p_1 is a point and p_0 is the center)
This is simply the line from the center to the point

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27
Q

How to find a tangent to a circle / sphere at a point

A

The projection of the point on the tangent to the unit normal = radius. Or without making it unit (p is any point on the line):

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28
Q

How to compute an intersection with a plane

A
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29
Q

Why does n . p = d work (for a plane)

A
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30
Q

What is a linear combination?

A

Vector u is a linear combination over the vector set V if
~~~
u = a_1 v_1 + a_2 v_2 + … + a_n v_n
~~~
Basically a weighted sum of the vectors

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31
Q

What does it mean to for vectors be lineararly (in)dependent?

A

Dependent: ` a_1 v_1 + … + a_n v_n = 0 Independent: a_1 v_1 + … + a_n v_n /= 0` for all values of a.
Note: can make a dependent set independent by removing a single (or more) vectors

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32
Q

What is a Span of a set of vectors?

A

Span(V) is the set of all possible vectors generated by the linear combiantion of the vectors in V. Eg: 2 independent vectors span a plane. The span is a vector space

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33
Q

What is a vector subspace

A

A subset of vectors of another vector space

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34
Q

What is a basis for vector space V?

A

A linearly independent set of vectors **that span V **through linear combinations. N terms span N dimensions

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35
Q

What are vector coordinates

A

The scalar values of a linear combination (of a basis):

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36
Q

How to determine vector coordinates

A

Either through a system of linear equations or by taking the dot product of an orthonormal basis.

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37
Q

What is an orthonormal basis

A

A basis of vectors which are:
1. Linearly independent
2. Othogonal (perpendicular)
3. Unit vectors

38
Q

How to create an orthonormal basis using the Gram-Schmidt process?

A

Works by projecting the vector onto a subspace and taking the difference (which will be orthogonal to the subspace)

39
Q

How to project a vector onto a subspace (hence finding the closest vector in that space)?

A

You must have an orthonormal basis.

40
Q

What is an mxn matrix (aij)

A

A 2d array of numbers. M rows, N columns. aij referes to row i col j

41
Q

What is the transpose of a matrix

A

Swapping values along diagonal (swapping rows vectors with column vectors).

42
Q

How to add matrices

A
43
Q

Scalar multiple of a matrix

A
44
Q

What is the Zero matrix 0

A

All components zero

45
Q

How does matrix vector multiplication work

A

Works by scaling each of the columns of the matrix (the stretched versions of the basis) by the respective components of the vectors.

46
Q

How do matrices relate to the standard basis

A

Each column of an independent matrix can be defined as where the respective standard basis appears in this new plane of space.

47
Q

What is the transpose of a vector

A
48
Q

How does the dot product relate to a transpose of a vector

A
49
Q

In what vector space does the result of a matrix vector multiplication lie

A

v is domain, v’ is range

50
Q

What is an identity matrix

A
51
Q

Compress a 3D vector into 2D with a matrix

A
52
Q

Rotation matrix counter clockwise by theta

A
53
Q

Sheer matrix

A
54
Q

What is the result of

(other way around to dot product)
A
55
Q

Multiplication of 2 transposed matrices property

A
56
Q

Is matrix multiplication associative and commutative

A
57
Q

How is matrix multiplication expressed as the sum of components

A
58
Q

What is the geometric meaning of matrix multiplication

A

One transformation then another BAx = Ax then B

59
Q

How to create a matrix for a given transformation

A

Find the transformed standard basis i, j k etc.

60
Q

What is a linear system

A

Solution that simultaniously satisfies all equations

61
Q

What are the three solutions categories for a linear system

A
  1. Unique solution
  2. No solution - inconsistent (eg. parallel)
  3. Infinite solutions (line, plane etc.)
62
Q

How to solve a linear system

A
  1. Substitution
  2. Guassian elimination
63
Q

Matrix notation for linear systems

A

This means that x can only be found if b is in the column space spanned by A

64
Q

What is the matrix rank

A

number of independent columns, or dimensions of column span

65
Q

Is a solution unique for a linear system with a) dependent columns b) independent columns

A

a) unique
b) not unique - infinite solutions

66
Q

What are the 3 operations allowed in gaussian elimination (solving a linear system)

A
67
Q

How do you find a solution with Gaussian elimination

A

Forward elimination into triangular form, then back substitution

68
Q

How does Gaussian elimination with matrix notation work

A
69
Q

In what 2 cases does gaussian elimination breakdown

A
  1. Contradiction (eg 1 = 0) -> no solution
  2. Consistent (eg 0 = 0) -> infinite solution. Solve using x_n = t
70
Q

What is an inverse matrix

A

When a matrix is multiplied with its inverse it gives the identity matrix (1s along diagonal, else 0s)

71
Q

What is the formula for the inverse of a 2x2 matrix

A
72
Q

What is the determinant of a 2x2 matrix

A

|A|= ad - bc

73
Q

How to compute the determinant of a 3x3+ matrix

A

|A| = a(ei − fh) − b(di − fg) + c(dh − eg)

74
Q

What determines the dimension of a linear transformation

A

The rank of the matrix

75
Q

What are the 3 requirements for a matrix to have an inverse

A
  1. Square nxn
  2. Determinant not equal to zero
  3. Independent columns
76
Q

What is the premise behind the Gauss-Jordan method

A

A A^(-1) = I
If A^(-1) = [x_1 x_2 ... x_n] (column vectors)

``` Ax_k = e_k ```
77
Q

How to compute the Gauss-Jordan method

A
  1. Left is A right is I
  2. Forward elimination into triangular form
  3. Back elimination into diagonal
  4. Scalar multiple so left becomes identity matrix
  5. Right matrix is now A^(-1)
78
Q

What is cramer’s rule for solving linear systems

A

Replace ith column with b (Av = b) and solve.
Concept of determinant representing area (or coordinates)

79
Q

Determinant of a nxn matrix

A
80
Q

What is an eigen vector?

A
81
Q

What is an eigen value?

A
82
Q

How many eigen vectors exist for each eigen value?

A
83
Q

How many eigen values exist for a NxN matrix

A
84
Q

How to find the eigen values of a matrix

A
85
Q

How to find the eigen vectors of a matrix

A
86
Q

How to raise a matrix to a power using diagonalisation

A
87
Q

What are markov chains

A
88
Q

What are transition matrices

A
89
Q

What is the simplified difference equation and the steady state

A
90
Q

What is the convergence rate dependent upon

A
91
Q

What are the eigen vectors and eigen values of the inverse of A

A

Same eigen vectors
1/eigen values