Is M2 a Vector Space with Modified Scalar Multiplication?

In summary, the conversation discusses whether M2, the set of all 2x2 matrices, is a vector space when using the standard addition of vectors and a new scalar multiplication. The individual asks for help in solving this problem and is reminded to start with a formal definition of a vector space, which includes about 10 axioms.
  • #1
blazelian
2
0
Is this a vector space?

Let M2 denote the set of all matrices of 2 x 2. Determine if M2 is a vector space when considered with the standard addition of vectors, but with scalar multiplication given by
α*(a b) = (αa b)
(c d) (c αd)
In case M2 fails to be a vector space with these definitions, list at least one axiom that fails to hold. justify you answer.

How do you solve this?
 
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  • #2


Start with a good formal definition of a vector space.
 
  • #3


well a vector space is something tht looks like R^n
 
  • #4


blazelian said:
well a vector space is something tht looks like R^n
That's not a formal definition. Your text should have a definition of a vector space, including about 10 axioms.
 
  • #5


Yes, M2 is a vector space with the given definitions of addition and scalar multiplication. To prove this, we need to check if all the axioms of a vector space hold for M2.

1. Closure under addition: For any two matrices A = (a b) and B = (c d) in M2, A+B = (a+c b+d) is also a matrix in M2. Therefore, M2 is closed under addition.

2. Associativity of addition: (A+B)+C = (a+c b+d)+(e+f g+h) = (a+e+c f) + (b+g+d h) = A+(B+C). Therefore, addition is associative in M2.

3. Commutativity of addition: A+B = (a+c b+d) = (c+a d+b) = B+A. Therefore, addition is commutative in M2.

4. Existence of additive identity: The zero matrix O = (0 0) is the additive identity in M2 since A+O = (a+0 b+0) = A for any A = (a b) in M2.

5. Existence of additive inverse: For any A = (a b) in M2, the additive inverse is -A = (-a -b) since A+(-A) = (a+(-a) b+(-b)) = (0 0) = O.

6. Closure under scalar multiplication: For any scalar α and matrix A = (a b) in M2, α*A = (αa b) is also a matrix in M2. Therefore, M2 is closed under scalar multiplication.

7. Distributivity of scalar multiplication over vector addition: α*(A+B) = α*((a+c b+d)) = (αa+αc αb+αd) = (αa αb)+(αc αd) = α*A+α*B.

8. Distributivity of scalar multiplication over scalar addition: (α+β)*A = ((α+β)a (α+β)b) = (αa+βa αb+βb) = (αa αb)+(βa βb) = α*A+β*A.

9. Associativity of scalar multiplication: (αβ)*A = ((αβ)a (αβ)b) = (α(βa) β(β
 

FAQ: Is M2 a Vector Space with Modified Scalar Multiplication?

What is a vector space?

A vector space is a mathematical structure composed of a set of objects, called vectors, and two operations, vector addition and scalar multiplication, that satisfy certain properties. These properties include closure, associativity, commutativity, and distributivity.

How do you determine if a set is a vector space?

To determine if a set is a vector space, you must check if it satisfies the properties of a vector space. These properties include closure, associativity, commutativity, and distributivity. If the set satisfies these properties, it is a vector space.

What is closure in a vector space?

Closure in a vector space means that when you perform an operation (such as vector addition or scalar multiplication) on two vectors in the set, the result is also in the set. In other words, the set is closed under the operations defined for the vector space.

Can a set be a vector space if it does not contain the zero vector?

No, a set cannot be a vector space if it does not contain the zero vector. The zero vector is a necessary element in a vector space, and it must be present in the set for it to satisfy the properties of a vector space.

What is the difference between a vector space and a subspace?

A subspace is a subset of a vector space that also satisfies the properties of a vector space. This means that a subspace is a smaller vector space that is contained within a larger vector space. A vector space, on the other hand, is a complete set that satisfies all the properties of a vector space.

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