Linear Algebra Field & Vector Space Problem

In summary: In the same way, the multiplication between a scalar and a vector is a different operation than the multiplication between two real numbers. This is because we are dealing with two different vector spaces, ##\mathbb{R}^2## and ##\mathbb{R}##. The same is true in the definitions of addition and scalar multiplication in the given problem. As for part b), you have to show that V satisfies all the axioms of a vector space, such as the zero vector axiom, the associativity of addition axiom, and the distributivity of scalar multiplication over vector addition axiom.
  • #1
1LastTry
64
0

Homework Statement


Let V1 and V2 be vector spaces over the same field F.
Let V = V1 X V2 = {f(v1, v2) : v1 [itex]\in[/itex] V1; v2 [itex]\in[/itex] V2}, and de fine addition and scalar multiplication as follows.
 For (v1, v2) and (u1, u2) elements of V , defi ne (v1, v2) + (u1, u2) = (v1 + u1, v2 + u2).
 For (v1, v2) element of V and c [itex]\in[/itex] F, defi ne c  (v1, v2) = (c  v1, c  v2).
a) In the de finitions of addition and scalar multiplication there are three "+" and three "." To
which vector space is each one associated with?
b) Show that V is a vector space. NB: you must provide some reason why each of the axioms is
satis ed.


Homework Equations



To be absolute honest i have no idea what it means when it asked which vector space it belong to in part a).
ANd for part 2, i do not know where to start.


The Attempt at a Solution



I know that to proof fields or vector spaces, it has to satisfy with the axioms

Zero vector
addition
scalar multiplication
and etc.
Just have trouble starting this problem

Thanks.

 
Physics news on Phys.org
  • #2
Take, for example, ##\vec{x}+\vec{y}## where ##\vec{x},\vec{y} \in \mathbb{R}^2##. You write ##\vec{x} = \begin{bmatrix} x_1 \\ x_2 \end{bmatrix}## and ##\vec{y} = \begin{bmatrix} y_1 \\ y_2 \end{bmatrix}##. Then
$$\vec{x}+\vec{y} = \begin{bmatrix} x_1 \\ x_2 \end{bmatrix} + \begin{bmatrix} y_1 \\ y_2 \end{bmatrix} = \begin{bmatrix} x_1+y_1 \\ x_2+y_2 \end{bmatrix}.$$ The plus sign between ##\vec{x}## and ##\vec{y}## represents a different operation than the plus sign between ##x_1## and ##y_1##. Why? Because the first one is about adding two vectors while the second one is about adding two real numbers.
 

1. What is the purpose of studying linear algebra in the field of mathematics?

Linear algebra is a branch of mathematics that focuses on the study of linear equations and their representations in vector spaces. It has many applications in fields such as physics, engineering, computer science, and economics. By understanding the concepts of linear algebra, we can solve real-world problems involving systems of linear equations, transformations, and optimization.

2. What is a vector space and what are its properties?

A vector space is a mathematical structure that consists of a set of vectors and operations such as addition and scalar multiplication. It has the following properties: closure under addition and scalar multiplication, associativity and commutativity of addition, distributivity of scalar multiplication, and the existence of an additive identity and inverse.

3. How is linear independence related to vector spaces?

Linear independence refers to a set of vectors that are not linearly dependent, meaning they cannot be written as a linear combination of each other. In vector spaces, linear independence is important because it allows us to have a basis, which is a set of vectors that can be used to represent any other vector in the space.

4. What is the difference between a subspace and a span?

A subspace is a subset of a vector space that is closed under addition and scalar multiplication. It is a smaller vector space within the larger vector space. On the other hand, the span of a set of vectors is the set of all possible linear combinations of those vectors. In other words, the span is the set of all vectors that can be created by scaling and adding the original set of vectors.

5. How can linear algebra be applied in machine learning and data analysis?

Linear algebra plays a crucial role in machine learning and data analysis. It is used to represent and manipulate datasets, perform dimensionality reduction, and train and optimize machine learning models. Techniques such as linear regression, principal component analysis, and singular value decomposition all rely on linear algebra concepts.

Similar threads

  • Calculus and Beyond Homework Help
Replies
4
Views
2K
  • Calculus and Beyond Homework Help
Replies
8
Views
697
  • Calculus and Beyond Homework Help
Replies
9
Views
1K
  • Calculus and Beyond Homework Help
Replies
18
Views
1K
  • Calculus and Beyond Homework Help
Replies
7
Views
1K
  • Calculus and Beyond Homework Help
Replies
0
Views
450
  • Calculus and Beyond Homework Help
Replies
15
Views
2K
  • Calculus and Beyond Homework Help
Replies
14
Views
596
  • Calculus and Beyond Homework Help
Replies
5
Views
3K
  • Calculus and Beyond Homework Help
Replies
12
Views
979
Back
Top