Linear Dependence of Vectors in R^3 with Variable Coefficients

In summary, the homework statement is that the vectors v_{1}, v_{2}, and v_{3} form a linearly dependent set when the real value of \lambda is between -1/2 and 1.
  • #1
kwal0203
69
0

Homework Statement



For which real values of [itex]\lambda[/itex] do the following vectors form a linearly dependent set in [itex]\mathbb{R}^{3}[/itex]

[itex]v_{1}=(\lambda ,-\frac{1}{2},-\frac{1}{2}), v_{2}=(-\frac{1}{2},\lambda ,-\frac{1}{2}), v_{3}=(-\frac{1}{2},-\frac{1}{2},\lambda )[/itex]

The Attempt at a Solution



I know that

[itex]k_{1}(\lambda ,-\frac{1}{2},-\frac{1}{2})+k_{2}(-\frac{1}{2},\lambda ,-\frac{1}{2})+k_{3}(-\frac{1}{2},-\frac{1}{2},\lambda )=0[/itex]

will have non trivial solutions if the vectors form a linearly dependent set. The problem is when I put this in matrix form with the lambdas on the diagonal I don't know how to reduce it to row echelon form.

Is that the correct thing to do? and how can I reduce the matrix with lamdas in it if so?

thanks any help appreciated
 
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  • #2
hi kwal0203! :smile:

hint: if the rows of a square matrix are independent, then what can you say about the matrix? :wink:

(btw, i assume you've already seen the one obvious value?)
 
  • #3
tiny-tim said:
hi kwal0203! :smile:

hint: if the rows of a square matrix are independent, then what can you say about the matrix? :wink:

(btw, i assume you've already seen the one obvious value?)

Is that something to do with the rank? I'm not up to studying that yet, I'm not sure what it means when the rows of a square matrix are independent. Does it mean the system will have no free variables when solving it?
 
  • #4
first, have you done determinants (of a matrix) yet?
 
  • #5
tiny-tim said:
first, have you done determinants (of a matrix) yet?

Yes I have done work on determinants
 
  • #6
ok, then when is the determinant zero? :wink:
 
  • #7
tiny-tim said:
ok, then when is the determinant zero? :wink:

Lambda=x

Using the first row of the Matrix I got:

X^3-(3x/4)+1/4, so the determinant is 0 when that equation equals zero.

Is that correct?

So x=1?
 
  • #8
that doesn't seem to fit the obvious solution :confused:

can you show your calculations?
 
  • #9
Thanks for the help I do understand this now. The system will be linearly dependant when the determinant is equal to zero so he question is essentially asking for what values of lambda is the determinant zero.
 
  • #10
tiny-tim said:
that doesn't seem to fit the obvious solution :confused:

can you show your calculations?

The answer in my book is lambda equals -1/2 or 1

I'm guessing -1/2 was the obvious answer? :)
 
  • #11
kwal0203 said:
I'm guessing -1/2 was the obvious answer? :)

yes … you can instantly see that -1/2 makes all three vectors the same! :biggrin:

(unfortunately, -1/2 doesn't seem to fit your equation :redface:)
The system will be linearly dependant when the determinant is equal to zero so he question is essentially asking for what values of lambda is the determinant zero.

exactly! :smile:
 
  • #12
tiny-tim said:
that doesn't seem to fit the obvious solution :confused:

can you show your calculations?

Oops it should be -1/4 in the determinant formula :)
 
  • #13
ok, that makes sense!

so it's x3 - 3x/4 -1/4 = 0

the way you solve it (since it's a cubic equation, which isn't easy)

is to notice that -1/2 is obviously a solution to the original equation, so divide by (x + 1/2),

and you get x2 - x/2 - 1/2, which you can easily solve (what is the third solution?) :wink:
 

Related to Linear Dependence of Vectors in R^3 with Variable Coefficients

What is linear dependence?

Linear dependence is a mathematical concept that refers to the relationship between two or more variables in a linear equation. It means that one variable can be expressed as a linear combination of the other variables.

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

To determine if a set of vectors is linearly dependent, you can use the determinant or rank method. The determinant method involves creating a matrix with the vectors as columns and calculating its determinant. If the determinant is equal to zero, the vectors are linearly dependent. The rank method involves creating a matrix with the vectors as rows and finding the rank of the matrix. If the rank is less than the number of vectors, the vectors are linearly dependent.

What is the difference between linear dependence and linear independence?

Linear independence is the opposite of linear dependence. It means that the set of vectors cannot be expressed as a linear combination of each other. In other words, each vector in a linearly independent set adds new information to the overall system, while in a linearly dependent set, some vectors can be expressed as combinations of others, and therefore do not add new information.

Why is linear dependence important in linear algebra?

Linear dependence is important because it helps us understand the relationships between variables in a system. In linear algebra, it is used to determine if a system of equations has a unique solution or if it has infinitely many solutions. It is also used in applications such as data analysis, computer graphics, and engineering.

Can a set of two vectors be linearly dependent?

Yes, a set of two vectors can be linearly dependent. This means that one vector can be expressed as a multiple of the other vector. For example, if one vector is twice the other vector, they are linearly dependent. In this case, the set of vectors lies on the same line and does not add new information to the system.

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