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Vector Spaces A set V is called a vector space over a set K denoted V(K) if is an Abelian group, is a field, and For every element vV and K there exists an element .v V called the “scalar multiple of v by ” satisfying (i) (ii) (iii) (iv) Notation : 0 K denotes the additive identity under +, K, - denotes the inverse of under + denotes the identity under v denotes the inverse of v under

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**Examples Example 1 (Polynomials of degree n)**

V = set of all polynomials of order n The additive operation on vectors is defined as follows: V(K) is a vector space Proof For v= Now by continuity of addition on the real numbers

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**Examples:1 Proof continued follows from associativity of**

normal addition Closure is trivial Hence, is an Abelian group therefore Similarly for properties (ii)-(iv)

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**Examples:2 Example 2 (n dimensional vectors)**

Example 3 (Complex Numbers) Example 4 (Matrices) is the set of nm matrices is the set of nn matrices

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**Properties of Vector Spaces**

Theorem Proof Identity under + by axiom (ii) of vector spaces But Identity under Therefore by the cancellation law for V K v

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**Properties:2 Theorem (i) = .( v) (ii) (-).( v) =.v Proof (i) Show**

by previous theorem and inverse under + Therefore, by axiom (ii) of vector spaces Also (.v) by inverse under Therefore (.v) (.v) by the cancellation law for

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**Properties:3 Show = .( v) .(v ( v)) by inverse under Therefore,**

by axiom (i) vector spaces Now by previous theorem and axiom (iii) by above theorem .v . v Also .v (.v) by inverse under Therefore, .v . v= .v (.v) . v= (.v) By the cancellation law for (ii) proof ??

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Subspaces Definition Let WV such that W then W(K) is a subspace of V(K) if W is a vector space over K with the same definition of and scalar multiple as V Clearly to show that W(K) is a subspace of V(K) we need only show that <W,> is a sub-group of <V, > and that Characterisation Theorem A non-empty subset W of V is a subspace of V iff .u vW for all K, u,vW Proof () trivial since .uW

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**Subspaces:2 Proof (continued) () Taking =1 then u,vW**

by axiom (iv) Taking =-1 then u,vW and by a previous theorem (-1).u= (1.u)= u by axiom (iv) Therefore u uW by inverse under Therefore K, uW taking gives .uW Therefore, taking =-1 gives (1.u)W uW by a previous theorem Hence, is a subgroup and as required

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**Examples of Subspaces Example 1 Let Then W(K) is a subspace of V(K)**

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**Examples of Subspaces:2**

Example 1 (continued) proof (i) Clearly W and WV (ii) For R and u,vW such that and then Example 2 (i) Clearly W and WV and 2R but W(K) is not a subspace of V(K)

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**Linear Combinations Definition If then where**

is a linear combination of S

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**Linear Combinations:2 Theorem For then is a subspace of V Proof since**

and by a previous theorem and hence If then and for some Then by axiom (i) by axiom (iii) by axiom (ii)

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**Linear Independence Definition A subset of V is linearly independent**

iff Otherwise if there exist one such that then are linearly dependent Example 1 is linearly dependent over R since Example 2 is linearly independent since

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**Linear Dependence in Matrices**

Theorem If and are the n column vectors of A then is linearly dependent over K if and only if Proof If is linearly dependent over K, then there exists (not all zero) such that Without loss of generality assume then Hence, performing a column operation where is added to column 1 gives a matrix with zero first column. Hence,

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**Matrices:2 Proof (continued) If then the system of equations**

has a non-trivial solution, But this is the same as saying that there exist (not all zero) such that By considering the transpose of A we obtain Corollary The n rows of a matrix are linearly dependent if and only if

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**Basis Definition A set is a basis for V iff**

(i) S is linearly independent over K (ii) Condition (ii) means that S is a spanning set for V Definition (Finitely Generated) A vector space V is said to be finitely generated if it has a Basis S with a finite number of elements V S

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**Examples of Basis Example 1 Let is a basis for V Then**

Linear independence: Spanning: is also a basis for V Proof ??

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**Examples of Basis:2 Example 2 Let then is a basis where Example 3 then**

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Dimension Theorem Every Basis of a finitely generated vector space has the same number of elements. Definition (Dimension) The number of elements in a basis for a finitely generated vector space V is called the dimension of V and denoted dim V. Examples then is a basis dim(V) = 3 Let then is a basis and dim(V)=4

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Vectors CHAPTER 7. Ch7_2 Contents 7.1 Vectors in 2-Space 7.1 Vectors in 2-Space 7.2 Vectors in 3-Space 7.2 Vectors in 3-Space 7.3 Dot Product 7.3.

Vectors CHAPTER 7. Ch7_2 Contents 7.1 Vectors in 2-Space 7.1 Vectors in 2-Space 7.2 Vectors in 3-Space 7.2 Vectors in 3-Space 7.3 Dot Product 7.3.

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