1. vectors - github pages
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1. Vectors
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Outline
Notation
Examples
Addition and scalar multiplication
Inner product
Complexity
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.1
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Vectors
I a vector is an ordered list of numbersI written as 266666664
�1.10.03.6�7.2
377777775or
*....,
�1.10.03.6�7.2
+////-
or (�1.1,0,3.6,�7.2)
I numbers in the list are the elements (entries, coe�cients, components)I number of elements is the size (dimension, length) of the vectorI vector above has dimension 4; its third entry is 3.6
I vector of size n is called an n-vector
I numbers are called scalars
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.2
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Vectors via symbols
I we’ll use symbols to denote vectors, e.g., a, X, p, �, Eaut
I other conventions: g, ~a
I ith element of n-vector a is denoted ai
I if a is vector above, a3 = 3.6
I in ai, i is the index
I for an n-vector, indexes run from i = 1 to i = n
I warning: sometimes ai refers to the ith vector in a list of vectors
I two vectors a and b of the same size are equal if ai = bi for all i
I we overload = and write this as a = b
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.3
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Block vectors
I suppose b, c, and d are vectors with sizes m, n, p
I the stacked vector or concatenation (of b, c, and d) is
a =
2666664b
c
d
3777775I also called a block vector, with (block) entries b, c, d
I a has size m + n + p
a = (b1,b2, . . . ,bm,c1,c2, . . . ,cn,d1,d2, . . . ,dp)
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.4
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Zero, ones, and unit vectors
I n-vector with all entries 0 is denoted 0n or just 0
I n-vector with all entries 1 is denoted 1n or just 1
I a unit vector has one entry 1 and all others 0
I denoted ei where i is entry that is 1
I unit vectors of length 3:
e1 =
2666664100
3777775, e2 =
2666664010
3777775, e3 =
2666664001
3777775
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.5
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Sparsity
I a vector is sparse if many of its entries are 0
I can be stored and manipulated e�ciently on a computer
I nnz(x) is number of entries that are nonzero
I examples: zero vectors, unit vectors
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.6
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Outline
Notation
Examples
Addition and scalar multiplication
Inner product
Complexity
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.7
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Location or displacement in 2-D or 3-D
2-vector (x1,x2) can represent a location or a displacement in 2-D
x
x1
x2
x1
x2x
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.8
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More examples
I color: (R,G,B)
I quantities of n di�erent commodities (or resources), e.g., bill of materialsI portfolio: entries give shares (or $ value or fraction) held in each of n
assets, with negative meaning short positionsI cash flow: xi is payment in period i to usI audio: xi is the acoustic pressure at sample time i
(sample times are spaced 1/44100 seconds apart)I features: xi is the value of ith feature or attribute of an entityI customer purchase: xi is the total $ purchase of product i by a customer
over some periodI word count: xi is the number of times word i appears in a document
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.9
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Word count vectors
I a short document:
Word count vectors are used in computer based documentanalysis. Each entry of the word count vector is the number oftimes the associated dictionary word appears in the document.
I a small dictionary (left) and word count vector (right)
wordinnumberhorsethedocument
26666666666664
321042
37777777777775I dictionaries used in practice are much larger
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.10
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Outline
Notation
Examples
Addition and scalar multiplication
Inner product
Complexity
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.11
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Vector addition
I n-vectors a and b can be added, with sum denoted a + b
I to get sum, add corresponding entries:
2666664073
3777775+
2666664120
3777775=
2666664193
3777775I subtraction is similar
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.12
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Properties of vector addition
I commutative: a + b = b + a
I associative: (a + b) + c = a + (b + c)(so we can write both as a + b + c)
I a + 0 = 0 + a = a
I a � a = 0
these are easy and boring to verify
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.13
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Adding displacements
if 3-vectors a and b are displacements, a + b is the sum displacement
a
ba + b
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.14
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Displacement from one point to another
displacement from point q to point p is p � q
p � q
p
q
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.15
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Scalar-vector multiplication
I scalar � and n-vector a can be multiplied
�a = (�a1, . . . , �an)
I also denoted a�
I example:
(�2)2666664
196
3777775=
2666664�2�18�12
3777775
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.16
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Properties of scalar-vector multiplication
I associative: (��)a = �(�a)
I left distributive: (� + �)a = �a + �a
I right distributive: �(a + b) = �a + �b
these equations look innocent, but be sure you understand them perfectly
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.17
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Linear combinations
I for vectors a1, . . . ,am and scalars �1, . . . , �m,
�1a1 + · · · + �mam
is a linear combination of the vectors
I �1, . . . , �m are the coe�cients
I a very important concept
I a simple identity: for any n-vector b,
b = b1e1 + · · · + bnen
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.18
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Example
two vectors a1 and a2, and linear combination b = 0.75a1 + 1.5a2
a1
a2
0.75a1
1.5a2
b
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.19
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Outline
Notation
Examples
Addition and scalar multiplication
Inner product
Complexity
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.21
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Inner product
I inner product (or dot product) of n-vectors a and b is
aT
b = a1b1 + a2b2 + · · · + anbn
I other notation used: ha,bi, ha|bi, (a,b), a · b
I example:
2666664�1
22
3777775
T 266666410�3
3777775= (�1)(1) + (2)(0) + (2)(�3) = �7
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.22
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Properties of inner product
I aT
b = bT
a
I (�a)Tb = �(aT
b)
I (a + b)Tc = a
Tc + b
Tc
can combine these to get, for example,
(a + b)T (c + d) = aT
c + aT
d + bT
c + bT
d
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.23
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General examples
I eT
ia = ai (picks out ith entry)
I 1Ta = a1 + · · · + an (sum of entries)
I aT
a = a21 + · · · + a
2n
(sum of squares of entries)
Introduction to Applied Linear Algebra Boyd & Vandenberghe 1.24