2 Linear Algebra Proofs about Linear Independence

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SUMMARY

The discussion centers on two proofs regarding linear independence in linear algebra. Proof 1 establishes that a set of vectors S = {v1, v2, ... vp} is linearly independent if and only if the equation Ax = 0 has only the trivial solution, where A is formed by the vectors in S. Proof 2 confirms that if {v1, v2, ... vp} is a linearly independent set with p ≥ 2, then vp cannot be an element of Span {v1, v2, ... vp-1}. The participants emphasize the importance of understanding the definitions and implications of linear independence and spanning sets.

PREREQUISITES
  • Understanding of linear independence and spanning sets in linear algebra
  • Familiarity with homogeneous systems of equations
  • Knowledge of vector spaces and their dimensions
  • Basic algebraic manipulation and proof techniques
NEXT STEPS
  • Study the definition and properties of linear independence in detail
  • Learn about the rank-nullity theorem in linear algebra
  • Explore the concept of span and its implications for vector sets
  • Investigate the relationship between the dimension of a vector space and the solutions to homogeneous systems
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Students and educators in mathematics, particularly those focusing on linear algebra, as well as anyone seeking to deepen their understanding of vector spaces and linear independence proofs.

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Homework Statement


Proof 1:
Show that S= {v1, v2, ... vp} is a linearly independent set iff Ax = 0 has only the trivial solution, where the columns of A are composed of the vectors in S. Be sure to state the relationship of the vector x to the vectors in S

2. The attempt at a solution
As far as I can tell (from my book and class), the actual definition of linear independence is
"A set of vectors is linearly independent iff there exists only the trivial solution to c1v1 + c2v2+ ... + cpvp = 0, or similarly only a trivial solution exists to the equation Ax = 0 when the columns of A are composed of the vectors in S. "

I know many implications of this property, but not the steps in between the term and the definition. Is there a more basic definition that I need to start this proof out with? Are the two options I gave in my definition of linear independence really different enough to merit in-between steps?

Homework Statement



Proof 2:
Prove or disprove: If {v1, v2, ... vp}, p>=2 is a linearly independent set, then vp is not an element of Span {v1, v2, ... vp-1}. 2. The attempt at a solution

I just took a guess at this one:

Assume that vp}, p>=2 is linearly independent, and let vp be an element of Span {v1, v2, ... vp-1}.

By definition of spanning, vp is a linear combination of {v1, v2, ... vp-1} .

By definition of linear combination, there exists c1v1 + c2v2+ ... + cpvp-1 = vp.

Algebra give us:
c1v1 + c2v2+ ... + cpvp-1 - vp = 0.

This gives a solution other than the trivial solution (where {c1, c2, ... cp} are all zero) to the equation c1v1 + c2v2+ ... + cpvp = 0, because cp must equal -1.

This is a contradiction. {v1, v2, ... vp}, p>=2 can't be a linearly independent set while vp is an element of Span {v1, v2, ... vp-1}. , so if {v1, v2, ... vp}, p>=2 is a linearly independent set, then vp is not an element of Span {v1, v2, ... vp-1}, as desired. Q.E.D.Any help or hints on either of these would be greatly appreciated. The first one I just need a starting point, and the second one I need to know if I made any mistakes, or if that is a valid proof. Thanks a lot.
 
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I agree with the 2nd proof. In the first problem, the statement and the predicate (the definition) seem to be identical -- I am not clear on what is being asked.
 
Regarding proof 1, do you know anything about the dimension of the vector space of solutions to a homogenous system? How does it relate to the rank of the system matrix A? What *is* the rank of your matrix A, since it consists of p linear independent vectors?
 

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