Linear independance and span (Definition)

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SUMMARY

The discussion clarifies the relationship between linear independence and span in vector spaces. It establishes that if Span(S) is not equal to zero, it does not imply that S is linearly independent. Conversely, if Span(S) equals zero, it also does not guarantee linear independence. The definition of linear independence is provided, emphasizing that a set of vectors is linearly independent if the only solution to the equation a1s1 + a2s2 + ... + ansn = 0 is the trivial solution where all coefficients are zero. Examples using vectors in R3 and subsets in R2 illustrate these concepts effectively.

PREREQUISITES
  • Understanding of vector spaces and their properties
  • Familiarity with linear combinations and scalar multiplication
  • Knowledge of the definitions of linear independence and span
  • Basic proficiency in mathematical notation and concepts in R2 and R3
NEXT STEPS
  • Study the concept of basis in vector spaces
  • Learn about the implications of linear dependence in higher dimensions
  • Explore the geometric interpretation of span and linear independence
  • Investigate applications of linear independence in solving systems of equations
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Students and educators in linear algebra, mathematicians, and anyone seeking a deeper understanding of vector spaces, linear independence, and span.

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Would I be correct in saying that:

If Span(S)≠0 then S is linearly independent.
If Span(S)=0 then S is linearly independent.

With S being a subset of V.
 
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No. Span describes the set of all vectors in V that are linear combinations of vectors in S, it is entirely separate from linear independence. Linear independence means (there are various equivalent definitions) the following...

If the vectors s1, s2, ... sn are linearly independent, then the equality a1s1 + a2s2 + ... + ansn = 0 has only the trivial solution a1 = a2 = ... = an = 0 (where a1, a2,... an are scalars). Equivalently, none of the vectors can be expressed as a linear combination of the others.

Take, for example, the vectors [1 0 0], [0 1 0], and [0 0 1] in R3. These vectors are linearly independent, and yet they span all of R3 (the term for such a set of vectors is a basis for the space R3).
 
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Suppose e.g. that S is a subset of ##\mathbb R^2## that contains two points on the same line through the origin. For example, ##S=\{(0,1),(0,2)\}##. Then S is linearly dependent, and span S is that line, so span S is neither ∅ nor {0}. (I don't know which of those you meant by "0").
 

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