Proving M_mn(F) over F is a vector space

In summary, to show that M_mn(F) over a field F is a vector space, we can define A+B as (a_{ij}+b_{ij}) and \alpha A as (\alpha a_{ij}). We can then show that (a) \alpha A is in M_mn(F) by F being closed under scalar multiplication and (b) \alpha(A+B) is in M_mn(F) due to the distributive property of F.
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Homework Statement



Show M_mn(F) (the collection of mxn matrices over F) over a field F is a vector space.

The Attempt at a Solution



Denote [itex]A=a_{ij},B=b_{ij}[/itex] for elements of [itex]M_{mn}(F)[/itex] . Define [itex]A+B=(a_{ij}+b_{ij})[/itex] and for [itex]a\in F[/itex] denote [itex]\alpha A=(\alpha a_{ij})[/itex]. Then,

(a) If [itex]\alpha\in F[/itex] and [itex]A\in M_{mn}(F)[/itex] ,[itex] \alpha A=(\alpha a_{ij})\in M_{mn}(F)[/itex] since [itex]F[/itex] is closed under scalar multiplication? not sure

(b) If [itex]\alpha\in F and A,B\in M_{mn}(F)[/itex] , [itex]\alpha(A+B)=\alpha(a_{ij}+b_{ij})=\alpha a_{ij}+\alpha b_{ij}\in M_{mn}(F)[/itex] because of distributive property of [itex]F[/itex]?

I'm kind of stuck in justifying the claims.
 
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Any ideas?
 
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The whole problem is more awkward to state than it actually is. Take the first one. alpha*a_ij isn't in F because F is closed under 'scalar multiplication'. It's because F is closed under multiplication in F. alpha is in F, a_ij is in F. So alpha*a_ij is in F. So alpha*A is in M_nm(F). There's nothing deep going on there.
 

FAQ: Proving M_mn(F) over F is a vector space

What is a vector space?

A vector space is a mathematical structure that consists of a set of elements, called vectors, and a set of operations, such as addition and scalar multiplication, that can be performed on these vectors. The elements in a vector space can be any type of mathematical objects, such as numbers, functions, or matrices.

What does it mean for M_mn(F) over F to be a vector space?

M_mn(F) over F is a set of m-by-n matrices with entries from the field F. To say that it is a vector space means that it satisfies the 10 axioms, or properties, that define a vector space. These axioms include closure under addition and scalar multiplication, associativity and commutativity of addition, and the existence of an additive and multiplicative identity.

How do you prove that M_mn(F) over F is a vector space?

To prove that M_mn(F) over F is a vector space, we must show that it satisfies all 10 axioms. This can be done by starting with the definitions of a vector space and a matrix, and then systematically showing that each axiom holds for M_mn(F) over F. This may involve using properties of matrices and operations on matrices, such as matrix addition and multiplication.

Why is it important to prove that M_mn(F) over F is a vector space?

Proving that M_mn(F) over F is a vector space is important because it allows us to use all the tools and techniques of vector spaces to study and solve problems involving matrices. This includes being able to use concepts such as linear independence, basis, and dimension to analyze matrices and their properties. Additionally, it allows us to connect matrix operations to more abstract algebraic concepts and vice versa.

Are there any special cases where M_mn(F) over F may not be a vector space?

Yes, there are a few special cases where M_mn(F) over F may not be a vector space. One case is when the field F is not closed under matrix multiplication, meaning that the product of two matrices with entries from F may not be in F. Another case is when the dimensions m and n are not positive, as this would result in an empty set. Additionally, if m or n is equal to 1, then M_mn(F) over F is essentially a set of scalars and not a vector space in the traditional sense.

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