Proof of Cauchy-Shwartz Inequality

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SUMMARY

The Cauchy-Schwarz inequality states that for any vectors v and w in an inner product space V, the absolute value of their inner product || is less than or equal to the product of their norms ||v|| ||w||. This inequality holds with equality if and only if the vectors v and w are linearly dependent. The proof involves considering two cases: when one vector is the zero vector and when both vectors are non-zero, utilizing the property that the inner product of a vector with itself is non-negative.

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  • Understanding of inner product spaces
  • Familiarity with vector norms
  • Knowledge of linear dependence and independence
  • Basic properties of inner products
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Mathematicians, physics students, and anyone studying linear algebra or functional analysis will benefit from this discussion, particularly those interested in the properties of inner product spaces and vector relationships.

smoothman
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How would you prove the following:

let V be an inner product space. For v,w \epsilon V we have:

|<v,w>| \leq ||v|| ||w||

with equality if and only if v and w are linearly dependent.

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So far I know that the Cauchy-Schwartz inequality says |< v,w>| is less than or equal to ||v||||w|| for any two vectors v and w in an inner product space.

I also realize i have to prove

|< v, w>|< ||v||||w||

that is, that there is a strict inequality, except in the case where one vector is a multiple of the other.
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any ideas on the proof for this please
 
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Separate them into two cases, one where vector x = 0, and one where it does not. The first is simple to prove, the starting point for the other case is that the scalar product of a vector with itself is greater or equal to 0 (or is it? Why? Try to understand why this is the case!).

x,y are vectors in R^n[/tex]; t is an arbitrary scalar.<br /> <br /> ? \leq (tx + y) \cdot (tx + y) = ?...
 

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