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Euclidean Vector Space
In general, euclidean space is defined by any
is Euclidean length of a 2D vector.
Arbitrary:
Scalar Multiplication
Multiply all components by scaling factor
scales vector
- stays parallel
→ result vector is same direction as original
→ result vector is in opposite direction.
Arbitrary:
Vector Addition and Subtraction
Apply operation to corresponding components
Arbitrary:
Matrix Space
represents space of
matrices
Let
be a set with the operation of addition and scalar multiplication:
Closure Properties


Axioms
(commutativity of addition)
(associativity of addition)
(additive identity)
(additive inverse;
denoted
)
(distributive 1)
(distributive 2)
(associativity of multiplication)
(multiplicative identity)
Examples
Sets
represents the line
.
is not space since it does not satisfy the closure properties: let
;
Continuous Functions
represents the space of continuous functions on the interval
. All axioms and closure properties are satsified for this space.
Polynomials
Focus on
for
.
A polynomial
degree of
(denoted
) is
(max power of
)
Let
represent all polynomials of degree <
.
satisfies all properties and axioms and is therefore spaaaaaace!
Theorem
is vector space,
, then

(additive inverse is unique)

Proof



Subspace
For a space
,
is a subspace of
if it also satisfies the closure properties
Example
is a subspace of
since it satisfies the closure properties