Finding Linearly Independent Vectors in Subspaces

In summary, the vectors a1, a2, and a3 are given, and the vectors b1, b2, and b2 are also given. The vectors V1 and V2 are given, and the vectors W are given. The vectors a1, a2, and a3 are linearly independent because they have no nonzero components in common with any of the vectors in V1 or V2. The vectors b1, b2, and b2 are also linearly independent because they have no nonzero components in common with any of the vectors in V1 or V2.
  • #1
Faiq
348
16

Homework Statement


The vectors ##a_1, a_2, a_3, b_1, b_2, b_3## are given below
$$\ a_1 = (3~ 2~ 1 ~0) ~~a_2 = (1~ 1~ 0~ 0) ~~ a_3 = (0~ 0~ 1~ 0)~~ b_1 = (3~ 2~ 0~ 2)~~ b_2 = (2 ~2~ 0~ 1)~~ b_3 = (1~ 1~ 0~ 1) $$
The subspace of ## \mathbb R^4 ## spanned by ##a_1, a_2, a_3## is denoted by ##V_1## and the subspace of ## \mathbb R^4 ## spanned by ##b_1, b_2, b_3## is denoted by ##V_2##

The set of vectors which consists a zero vector and all vectors which belong to ##V_1## and ##V_2## is denoted by ##W##.

Write down two linearly independent vectors which belong to ##W##

The Attempt at a Solution


Can somebody please explain how to get the independent vectors? I am very aware of the definition of independent vectors. However, I cannot seem to relate the definition and use it to solve this question.
 
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  • #2
Hint: a1, a2, and a3 all have 0 as their 4'th coordinate, so they can not generate any vector with a nonzero 4'th coordinate.
 
  • #3
And ##b_1, b_2, b_3## all have 0 as their 3rd coordinate, so they cannot generate any vector with a nonzero 3rd coordinate
Thus the resulting vector should be ##(p~q~0~0)##
Correct?
 
  • #4
Faiq said:
And ##b_1, b_2, b_3## all have 0 as their 3rd coordinate, so they cannot generate any vector with a nonzero 3rd coordinate
Thus the resulting vector should be ##(p~q~0~0)##
Correct?
You need to find two vectors. Look for one in V2 that could not be a linear combination of the ais and look for a vector in V1 that could not be a linear combination of the bis
 
  • #5
a3 and b3?
And the reasoning behind the special criteria for choosing the vector is this right? If I were to choose a vector in V1 which was a linear combination of V2 vectors, then I would technically be choosing just V2 vectors and not taking into account the V1 vectors.
 
  • #6
Faiq said:
a3 and b3?
In homework, you would want to briefly explain why the two you pick are in W and are linearly independent. Use the definition of linearly independent.
 
  • #7
Thank you very much for your help
 

FAQ: Finding Linearly Independent Vectors in Subspaces

1. What is a matrix?

A matrix is a rectangular array of numbers or symbols arranged in rows and columns. It is commonly used to represent systems of equations and perform operations such as addition, subtraction, and multiplication.

2. What is a linear space?

A linear space, also known as a vector space, is a collection of objects called vectors that can be added together and multiplied by numbers to create new vectors. It follows certain properties such as closure under addition and scalar multiplication.

3. What is the difference between a matrix and a linear space?

A matrix is a specific representation of a linear space. It is a way to organize and manipulate vectors in a tabular form. A linear space, on the other hand, is a more abstract concept that encompasses all possible combinations of vectors and operations.

4. How are matrices and linear spaces used in science?

Matrices and linear spaces are used in many areas of science, including physics, engineering, and computer science. They are used to model and solve problems involving systems of equations, analyze data, and create mathematical models for various phenomena.

5. Can matrices and linear spaces be applied to real-world problems?

Yes, matrices and linear spaces have a wide range of applications in real-world problems. They are used in fields such as economics, genetics, and data analysis to solve complex problems and make predictions. For example, a matrix can be used to represent a transportation network, and linear spaces can be used to analyze gene expression data.

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