Express x and y as a linear combination of v's

In summary, the conversation discusses solving a matrix problem with three unknowns and two equations. The participants explore different ways of setting up the matrix, including vertically and horizontally, and discuss how to find a basis for the vectors involved. They also discuss how to express x in terms of v1 and v2, and how to solve for the values of the variables in the matrix. Ultimately, the conversation ends with the participants attempting to find solutions for x using different methods.
  • #1
robertjford80
388
0

Homework Statement



Screenshot2012-05-13at10858PM.png



The Attempt at a Solution



I can't figure out how to do this problem. If you set the matrix up like this

2 -1 2 1
3 1 1 1

You can't solve it because there are 3 unknowns and two equations.

I still don't really understand quite well when you set the matrix up vertically or horizontally but if you set it up like this

2 3
-1 1
2 1

there's no place to put the values for x and y to solve things.
 
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  • #2
Reduce the matrix and you'll get two pivots: 2 and 5/2.
 
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  • #3
But if I reduce it to

x x x x
0 0 x x

where x is any number, I still won't be able to solve it because there will be two unknowns in the first row.
 
  • #4
You have only 3 v's. So, in column form, after reducing the matrix:
[tex]V=
\displaystyle\left[ {\begin{array}{*{20}{c}}
2&-1&2 \\
0&5/2&-2 \\
\end{array}} \right][/tex]
Hence, you can see that columns 1 and 2 are a basis.
 
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  • #5
Well, if I reduce it to

[tex]V=
\displaystyle\left[ {\begin{array}{*{20}{c}}
2&-1&2 \\
0&5/2&-2 \\
\end{array}} \right][/tex]

Then I don't see what the next step should be.
 
  • #6
Hi Robert
You have 3 2D vectors, v1, v2, v3. as you can see, none is perpendicular to any, but they can't form a basis since they are 3 and the dimensionality is 2.
So any two of them will form a basis.
So for instance, you could have a first basis (v1,v2) a second one (v2,v3) and a third one (v1,v3) and that would be the 3 ways in which you can later express x and y
So for instance, in the first base, you would want to express x in (v1,v2)
(and later y too, but first let's look at x)
How do you express x in (v1, V2) ?
x must be a linear combination of v1 and v2, so x=av1+bv2
x being (1,1) that would mean
av1+b2=(1,1), so a(2,3)+b(-1,1)=(1,1)
therefore you would just have to solve (2a-b, 3a+b)=(1,1)
I let you continue at this point

Cheers...
 
  • #7
here are the answers

Screenshot2012-05-13at25038PM.png


by using v1 and v2 and setting up the matrix as follows

2 -1 1
3 1 1

I was able to get .4 -.2 which is the answer listed in 13

Using that technique I was not able to get the other two answers for x which are

-4/5 v + 7/5v + 2v

I don't see how I could even get values for three different v's when I'm only using two equations.
 

FAQ: Express x and y as a linear combination of v's

1. What does it mean to express x and y as a linear combination of v's?

Expressing x and y as a linear combination of v's means writing them as a sum of multiples of the vector v. In other words, we are finding coefficients that, when multiplied by v, will give us x and y.

2. How is this concept used in linear algebra?

In linear algebra, expressing a vector as a linear combination of other vectors is a fundamental concept. It allows us to represent a vector in terms of a basis, which is a set of linearly independent vectors that can be combined to form any vector in a given vector space.

3. Why is it important to be able to express vectors as linear combinations?

Expressing vectors as linear combinations allows us to simplify calculations and solve systems of equations. It also helps us understand the relationships between vectors and their components, which is crucial in many applications of linear algebra, such as in physics and engineering.

4. What are some common methods for finding the coefficients of a linear combination?

One common method is using Gaussian elimination, also known as row reduction. This involves writing the vectors and their components in a matrix and performing row operations to reduce the matrix to its reduced row echelon form. Another method is using the Gram-Schmidt process, which involves orthogonalizing a set of vectors to create an orthonormal basis.

5. How can expressing vectors as linear combinations be useful in data analysis?

In data analysis, vectors can represent data points, and expressing them as linear combinations can help us understand the relationships between different variables. This can be helpful in identifying patterns and making predictions. Additionally, expressing data in terms of a basis can also reduce the dimensionality of the data, making it easier to analyze and visualize.

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