Expanding linear independent vectors

In summary, the conversation discusses expanding a set of linearly independent vectors into a basis. This can be done by adding additional vectors to the set, as long as they are not in the span of the existing vectors. The process can be repeated until there are enough vectors to form a basis.
  • #1
member 428835
Hi PF!

The other day in class my professor mentioned something about expanding linear independent vectors, but he did not elaborate. From what I understand, if ##x_1,...,x_k## are linearly independent vectors in ##V##, where ##dimV=n>k##, how would you extend ##x_1,...x_k## to a basis ##\{ x_1,...,x_n \}##. Let's say ##\{ y_1,...,y_n \}## is a basis for ##V##. By extending the ##x## vectors, do you think he was just referring to including all the ##y## vectors in the set of ##x## vectors that are linearly independent of the ##x## vectors?

Thanks!
 
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  • #2
If you have a set of linearly independent vectors, you can expand that set into a basis simply be adding more vectors. In your example, you can choose ##x_{k+1}## as any vector in V that is not in the span of ##\{x_1, \dots x_k \}##.

And then, any vector ##x_{k+2}## that is not in the span of ##\{x_1, \dots x_{k+1} \}##.

Until you have a basis of ##n## linearly independent vectors.
 
  • #3
OK cool, that's what I thought but I wanted someone else's perspective! Thanks PeroK!
 

Related to Expanding linear independent vectors

1) What does it mean for vectors to be linearly independent?

Linear independence refers to a set of vectors in which none of the vectors can be written as a linear combination of the others. In other words, no vector in the set can be expressed as a sum of scalar multiples of the remaining vectors.

2) How do I know if a set of vectors is linearly independent?

To determine if a set of vectors is linearly independent, you can use the determinant test. This involves creating a matrix with the vectors as columns and calculating the determinant. If the determinant is non-zero, the vectors are linearly independent. Another way is to try to write one of the vectors as a linear combination of the others. If this is not possible, the vectors are linearly independent.

3) What is the significance of having linearly independent vectors?

Linear independence is important in many areas of mathematics and science, including linear algebra, physics, and engineering. It allows us to solve systems of equations, perform calculations with matrices, and understand the relationships between different variables in a system.

4) Can a set of vectors be linearly independent in one dimension but not in another?

Yes, a set of vectors can be linearly independent in one dimension but not in another. For example, the vectors (1,0) and (0,1) are linearly independent in two dimensions, but if we add a third dimension with the vector (1,1), the three vectors become linearly dependent.

5) How does adding new vectors affect the linear independence of a set of vectors?

Adding new vectors can change the linear independence of a set of vectors. If a new vector is a linear combination of the existing vectors, the set will become linearly dependent. However, if the new vector is not a linear combination of the existing vectors, the set will remain linearly independent. This is why it is important to consider linear independence when adding new vectors to a set.

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