Can Linear Independence be Proven with Given Information?

In summary, the problem states that to prove linear independence, the linear combination of u_i must equal 0 with only the trivial solution. The given information is that u_i= Av_i for all i, and we can use this to show that if A is invertible, then the linear combination of v_i must also equal 0 with only the trivial solution. However, if A is not invertible, there exists a non-zero vector v_0 such that Av_0=0, and since the given vectors are independent, we can write v_0 as a linear combination of the given vectors. Applying A to both sides of this equation will result in a contradiction, proving linear independence.
  • #1
zohapmkoftid
27
1

Homework Statement



[PLAIN]http://uploadpie.com/nsXSv

Homework Equations





The Attempt at a Solution



I have no idea how to start. To be linearly independent, c1u1+c2u2+...+cnun = 0 has only trivial solution. But I don't know how can I use the given information to prove that
 
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  • #2
You are given that [itex]u_i= Av_i[/itex] for all i.

For any linear combination, [itex]a_1u_1+ a_2u_2+ \cdot\cdot\cdot a_nu_n= 0[/itex] we have [itex]a_1Av_1+ a_2Av_2+ \cdot\cdot\cdot a_nAv_n= A(a_1v_1+ a_2v_2+ a_nv_n)= 0[/itex].

If A is invertible, we have [itex]a_1v_1+ a_2v_2+ \cdot\cdot\cdot+ a_nv_n= 0[/itex].

On the other hand, if A is NOT invertible, there exist [itex]v_0\ne 0[/itex] such that [itex]Av_0= 0[/itex] (you should show that). Since [itex]\{v_1, v_2, \cdot\cdot\cdot, v_n\}[/itex] are n independent vectors in [itex]R^n[/itex], we can write [itex]v_0= c_1v_1+ c_2v_2+ \cdot\cdot\cdot+ c_nv_n[/itex] for some numbers [itex]c_n[/itex]. Apply A to both sides of that equation.
 

FAQ: Can Linear Independence be Proven with Given Information?

What is linear independence?

Linear independence is a concept in linear algebra that describes the relationship between a set of vectors. It means that none of the vectors in the set can be written as a linear combination of the others, meaning they are all unique and necessary to span the vector space.

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

To determine if a set of vectors is linearly independent, you can use the process of Gaussian elimination. You can put the vectors into a matrix and use row operations to reduce the matrix to its row echelon form. If there are no rows of all zeros, then the vectors are linearly independent. You can also use the determinant of the matrix, and if it is non-zero, then the vectors are linearly independent.

What is the significance of linear independence?

Linear independence is important because it allows us to describe the span of a vector space using the fewest number of necessary vectors. It also helps us understand the relationships between vectors and their components, and is essential in solving systems of linear equations.

Can a set of linearly dependent vectors span a vector space?

No, a set of linearly dependent vectors cannot span a vector space. If the vectors are linearly dependent, it means that some of the vectors can be written as a linear combination of the others, making them redundant. Therefore, they do not add any new information and cannot fully span the vector space.

How does linear independence relate to linear transformations?

Linear independence is closely related to linear transformations because it helps determine the dimension of the domain and range of the transformation. If the set of basis vectors for the domain is linearly independent, then the dimension of the domain is equal to the number of vectors in the set. Similarly, if the set of basis vectors for the range is linearly independent, then the dimension of the range is equal to the number of vectors in the set.

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