Solving Linear Dependence in a Vector Space

In summary, the conversation discusses a problem where the given matrix is linearly dependent and one of the variables is a free variable. The question is whether the free variable will always cancel out when solving for T(vector). Part b) of the problem is also discussed, with the conclusion that there are infinitely many solutions where none of the coefficients are zero.
  • #1
Clandry
74
0
Hi. I attached the problem and my work.
I'm not sure if I did part a) right. In the past problems I've done, they usually provide you with 3 vectors that are linearly independent, thus giving you unique values for C1, C2, C3. The matrix for this one forms:
1 1 1
0 1 3
0 0 0
Which is obviously linearly dependent.

In my work I solved it and C3 ended up canceling out. Does the free variable always cancel out when solving for T(vector) if the matrix above is linearly dependent?



For part b)
I said no, because if the 3rd element (element in 3rd row) in T(v) is nonzero, then the system is inconsistent.
 

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  • #2
As you noted, the three vectors (1,0,0), (1,1,0), and (1,3,0) are linearly dependent. You can express (2,-5,0) as a linear combination of any two of them. For example,
[tex](2,-5,0) = 7(1,0,0) -5(1,1,0)[/tex]
Note that these are unique coefficients, because now you are working with two linearly independent vectors, (1,0,0) and (1,1,0).

If you use all three vectors, then there are many ways to write (2,-5,0) as a linear combination of the three. For example, you could make [itex]C_3=0[/itex]:
[tex](2,-5,0) = 7(1,0,0) -5(1,1,0) + 0(1,3,0)[/tex]
(This is the solution you found.)

Or you could make [itex]C_2=0[/itex]:
[tex](2,-5,0) = (11/3)(1,0,0) + 0(1,1,0) -(5/3)(1,3,0)[/tex]
Or you could make [itex]C_1=0[/itex]:
[tex](2,-5,0) = 0(1,0,0) + (11/2)(1,1,0) - (7/2)(1,3,0)[/tex]
There are also infinitely many solutions where none of the coefficients are zero, for example:
[tex](2,-5,0) = 9(1,0,0) -8(1,1,0) + 1(1,3,0)[/tex]
Note that you will get the same answer for [itex]T(2,-5,0)[/itex] no matter which of the above solutions you use.
 

Related to Solving Linear Dependence in a Vector Space

1. What is linear dependence in a vector space?

Linear dependence in a vector space refers to a situation where one vector can be expressed as a linear combination of other vectors in that space. In other words, the vector can be written as a sum of scalar multiples of the other vectors.

2. How do you determine if a set of vectors is linearly dependent?

To determine if a set of vectors is linearly dependent, we can use the method of row reduction or Gaussian elimination to create an augmented matrix and then find its rank. If the rank is less than the number of vectors in the set, then the vectors are linearly dependent.

3. What is the difference between linear dependence and linear independence?

Linear dependence and linear independence are two opposite concepts. Linear dependence refers to a set of vectors that can be expressed as a linear combination of each other, while linear independence refers to a set of vectors that cannot be written as a linear combination of each other.

4. Why is linear dependence important in linear algebra?

Linear dependence is an important concept in linear algebra because it helps us understand the relationship between vectors in a vector space. It also allows us to determine whether a system of linear equations has a unique solution or not. Additionally, linear dependence is closely related to the concept of linear transformations, which are fundamental in many areas of mathematics and science.

5. How can we solve for linear dependence in a vector space?

To solve for linear dependence in a vector space, we can use various methods such as Gaussian elimination, computing the determinant of a matrix, or finding the rank of an augmented matrix. These methods allow us to determine if a set of vectors is linearly dependent and to find a basis for the subspace spanned by the vectors.

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