Is the Second Set of Vectors {v1, v1 + v2, v1 + v2 + v3} Linearly Independent?

In summary, the conversation discusses the linear independence of a set of vectors in a vector space. The first set, {v1, v2, v3}, is known to be linearly independent in R^M. The second set, {v1, v1 + v2, v1 + v2 + v3}, is being considered for its linear independence, and the argument presented is that since the dimension of the space would have to be greater than or equal to the number of vectors for the first set to be independent, and the second set has the same number of vectors, it must also be independent. However, this argument is flawed, as demonstrated by a counterexample.
  • #1
simmonj7
66
0
1. Homework Statement

Suppose that {v1, v2, v3} is a linearly independent subset of R^M. Show that the set {v1, v1 + v2, v1 + v2 + v3} is also linearly independent.

3. The Attempt at a Solution
So I know that {v1, v2, v3} is contained in R^M. And that since the set is linearly independent, the only solution to the vector equation a1v1 + a2v2 + a3v3 = 0 is a1 = 0, a2 = 0, and a3 = 0.

It is true to say that the vector {v1, v1 + v2, v1 + v2 + v3} is linearly independent because for the first vector to be linearly independent, the dimension (M) would have to be greater than the number of vectors (which in this case is three). We know from a theorem that if p (the number of vectors) is greater than m (the dimensional space) then the set is linearly dependent. In this case, for the second set to be linearly independent, M would have to be equal to or less then the number of vectors. However, since the number of vectors is still the same in both the first and second set, the set is linearly dependent...

Correct?
 
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  • #2
Of course, the whole idea doesn't make sense unless M >= 3.

But this is pretty easy. What does α v1 + β (v1 + v2) + γ (v1 + v2 + v3) = 0 imply (if you use the fact that {v1, v2, v3} is linearly independent)?
 
  • #3
simmonj7 said:
It is true to say that the vector {v1, v1 + v2, v1 + v2 + v3} is linearly independent because for the first vector to be linearly independent, the dimension (M) would have to be greater than the number of vectors (which in this case is three). We know from a theorem that if p (the number of vectors) is greater than m (the dimensional space) then the set is linearly dependent. In this case, for the second set to be linearly independent, M would have to be equal to or less then the number of vectors. However, since the number of vectors is still the same in both the first and second set, the set is linearly dependent...

Correct?
I think you mixed up independent and dependent a couple of times. Either that or you have a major misconception about linear independence. I'll assume you just accidentally used one word when you meant the other.

If I understand your argument, you're essentially saying: (1) there are 3 vectors that are linearly independent, so [itex]M\ge 3[/itex]; (2) the second set of vectors contains 3 vectors; (3) since this number is less than or equal to M, the vectors must be linearly independent. The problem is the last step doesn't work. Take the vectors (1,0,0), (0,1,0), and (1,1,0) in R3. There are only 3 which is equal to the dimension of the vector space, but they're clearly not independent since (1,1,0)=(1,0,0)+(0,1,0).

The theorem you're referring to tells you if you have more vectors than the dimension of the space, then they are linearly dependent. It doesn't tell you anything about independence or dependence, however, if you have fewer vectors than the dimension.
 

Related to Is the Second Set of Vectors {v1, v1 + v2, v1 + v2 + v3} Linearly Independent?

1. What is the definition of linear dependence of vectors?

Linear dependence of vectors refers to the relationship between two or more vectors, where one can be expressed as a linear combination of the others. In other words, if one vector can be written as a combination of the others, then the vectors are linearly dependent.

2. 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 linear dependence test. This test involves setting up a system of equations with the vectors as the variables and solving for the coefficients. If the system has infinitely many solutions or no solutions, then the vectors are linearly dependent. If the system has a unique solution, then the vectors are linearly independent.

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 where one vector can be expressed as a linear combination of the others, while linear independence refers to a set of vectors where no vector can be written as a linear combination of the others.

4. How does linear dependence affect the dimension of a vector space?

If a set of vectors is linearly dependent, then it means that at least one vector in the set is redundant and can be removed without changing the span of the vector space. Therefore, the dimension of the vector space decreases by one for each linearly dependent vector in the set.

5. Can linearly dependent vectors still be useful in applications?

Yes, linearly dependent vectors can still be useful in applications. In some cases, linear dependence can be used to simplify calculations and make problems more tractable. Additionally, linear dependence can also be used to identify patterns and relationships between vectors, which can be valuable in data analysis and machine learning.

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