How can I find the bases and generated subspace for a given set of vectors?

In summary, this student tried to find the dimensions and bases of a subspace generated by vectors (1,2,3), (4,5,6), (7,8,9). They were unable to do so and were looking for help from the teacher. The teacher provided a solution and explained how to find the generated subspace.
  • #1
kacete
27
0

Homework Statement


From the course of Linear Algebra and Analytic Geometry

I need to find the dimension and two different bases of subspace R3 generated by vectors (1,2,3), (4,5,6), (7,8,9).

Homework Equations


None.

The Attempt at a Solution


I tried

(a,b,c)=α1(1,2,3)+α2(4,5,6)+α3(7,8,9)

which became

a = α1 + 4α2 + 7α3
b = 2α1 + 5α2 + 8α3
c = 3α1 + 6α2 + 9α3

which (by Gaussian elimination) became an undetermined system with free variable α3.

4. The solution given by teacher
dim=2, example of bases={(1,0,-1),(0,1,2)} or {(2,1,0),(-1,0,1)}

I don't want the solution, I just want to understand the mechanics on how to find the bases and the generated subspace. If someone could explain it to me, thank you.
 
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  • #2
The system of equations you show would be used to find the span of your three vectors. Instead of equations that start with a= , b=, and c=, put 0 in for all three of those variables. You should end up with a row of zeroes and two nonzero rows.

What did you end up with when you row-reduced your matrix?
 
  • #3
So, I should have used the homogeneous system A . x = 0 ? Being A a matrix. Hmm...
I ended up with (after Gaussian elimination):

Code:
[ 1  4  7 | a      ]
[ 0 -3 -6 | b-2a   ]
[ 0  0  0 | c-a-2b ]
 
  • #4
If I can backpedal a bit, your work is fine. For the system represented by your augmented matrix to be consistent, it must be that c - a - 2b = 0.

or
Code:
a = -2b + c
b =    b
c =          c

I added the 2nd and 3rd equations above so that I can get some vectors out of the equation c - a - 2b = 0. The equations I added are obviously true for all values of b and c, respectively.

Any vector [a b c]^T is a linear combination of [-2 1 0]^T and [1 0 1]^T. These come from setting b = 1, c = 0 and then b = 0, c = 1.

Different pairs of choices for b and c will give you different pairs of vectors for your basis.

Hope that helps.
 

FAQ: How can I find the bases and generated subspace for a given set of vectors?

What is a linear space?

A linear space, also known as a vector space, is a mathematical structure that consists of a set of objects called vectors and two operations, vector addition and scalar multiplication. These operations follow specific rules, such as closure, associativity, and distributivity, which make the space behave like a coordinate system.

What are the properties of linear spaces?

The properties of a linear space include closure, associativity, commutativity, distributivity, existence of a neutral element, and existence of inverses. These properties allow for the manipulation and combination of vectors within the space.

How do you determine if a set of vectors form a linear space?

To determine if a set of vectors form a linear space, you need to check if they satisfy the properties of a linear space. This includes checking if the set is closed under vector addition and scalar multiplication, if the operations are associative and commutative, and if there exists a neutral element and inverses for each vector.

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

A subspace is a subset of a linear space that also satisfies all of the properties of a linear space. In other words, a subspace is a smaller linear space within a larger linear space. All subspaces are linear spaces, but not all linear spaces are subspaces.

How are linear spaces used in scientific research?

Linear spaces are used in various scientific fields, including physics, engineering, and computer science. They provide a mathematical framework for modeling and analyzing real-world phenomena, such as motion, forces, and data. Linear spaces are also used in data analysis and machine learning algorithms for tasks such as classification and regression.

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