How is the Definition of Angle in Linear Algebra Derived?

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Discussion Overview

The discussion centers around the derivation of the definition of angle in linear algebra, specifically how it relates to the dot product of vectors and the cosine function. Participants explore the mathematical relationships and expressions involved in defining angles between vectors, including the scalar product and its implications in vector spaces.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant expresses confusion about the definition of angle in terms of the cosine function and its relationship to the dot product.
  • Another participant clarifies that the scalar product of two vectors can be expressed in terms of their magnitudes and the cosine of the angle between them.
  • Several participants discuss the mathematical manipulation of the cosine definition, emphasizing the equivalence of different expressions for the dot product.
  • One participant notes that there are two formulas for the scalar product, one in terms of coordinates and another involving the cosine of the angle.
  • A participant references a source (MathWorld) to support their understanding of the relationship between the definitions of the dot product and the angle.
  • Another participant elaborates on the properties of inner products in vector spaces and how they lead to the Cauchy-Schwarz inequality, which is foundational for defining angles in this context.

Areas of Agreement / Disagreement

Participants exhibit varying levels of understanding and agreement regarding the definitions and relationships involved. Some express confusion and seek clarification, while others provide explanations and mathematical derivations. The discussion does not reach a consensus, as differing interpretations and approaches are presented.

Contextual Notes

Some participants highlight the potential for misunderstanding due to the multiple ways of expressing the dot product and the angle, indicating that the definitions may depend on the context or source material used.

danne89
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Hi! I cannot get this definition of angle a:
cos a = u * v / (||u|| * ||v||)
and how it match up with
u * v = ||u|| * ||v|| * cos a
 
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What do you mean you don't get it...?

[tex]\vec{u}\cdot\vec{v}=\left|\left|\vec{u}\right|\right| \cdot \left|\left|\vec{v}\right|\right| \cos\alpha[/tex]

is the expression of the scalar product of 2 vectors,when you know their modulus & the angle between their directions...

Daniel.
 
From [tex]cos(\alpha) = \frac{u\bullet v}{|u||v|}[/tex]

To [tex]{cos(\alpha)|u||v| = u\bullet v[/tex]

Just multiply both sides by [tex]|u||v|[/tex]
 
whozum said:
From [tex]cos(\alpha) = \frac{u\bullet v}{|u||v|}[/tex]

To [tex]{cos(\alpha)|u||v| = u\bullet v[/tex]

Just multiply both sides by [tex]|u||v|[/tex]

I'm sure there's something devious in your post... :rolleyes:


Daniel.

EDIT:Not anymore,you edited it... :wink:
 
:-D

I switched the wrong quantity, I always do that with latex.
 
Hmm. I formulated me quite wrong. How come, if u = (x1, y1), v = (x2, y2), u * v = x1x2 + y1y2 = ||v|| ||u|| cos a
 
there are two formulas for scalar product:

1- *[v] = xx' + yy' where (x,y) and [v](x',y')

2- *[v] = u*v*cos(,[v])

they usually give you in exercises the coordinates of the two vectors and ask you to find their angle
 
But, indeed, I've concluded from MathWorld, which I should have checked out right the way of cource, there's not two definition of the dot product. One can from the definition involving cosinus derive the other one, which strangely Schamu's Outline of Linear Algebra state is The definition. Only for complicate the matter in my limit brain, they also define angle as cos a = u * v / (||u|| ||v||). So I'm not as mad you may think I am.
 
  • #10
Well, if you approach it from the perspective of vector spaces (as you probably are in your linalg course), then you usually define a vector space with an inner product to be a vector space [itex]V/\mathbb{R}[/itex] with an operation [itex]( , ): V\times V \longrightarrow \mathbb{R}[/itex] such that [itex]\forall \ a, b, c \in V[/itex] and [itex]x \in \mathbb{R}[/itex],

1. [itex](a, b) = (b, a)[/itex]
2. [itex](b + c, a) = (b, a) + (c, a)[/itex]
3. [itex](xa, b) = x(a, b)[/itex]
4. [itex](a, a)[/itex] is definite positive, ie. [itex](a, a)\geq 0[/itex] with equality iff [itex]a = 0[/itex].

We then define

[tex]\|a\| = \sqrt{(a, a)}[/tex]

which we call the magnitude or length or modulus of [itex]a[/itex].

Now say we choose [itex]a, b[/itex] such that [itex]\|a\| =\|b\| = 1[/itex]. Then

[tex](a-b, a-b) = (a, a) - 2(a, b) + (b, b) = \|a\|^2 - 2(a, b) + \|b\|^2 \geq 0[/tex]

with the last inequality by 4., so

[tex]-2(a, b) \geq -\|a\|^2 - \|b\|^2 = -1^2 - 1^2 = -2 = -2\|a\|\|b\|[/tex]

[tex]\Longrightarrow (a, b) \leq \|a\|\|b\|[/tex]

and thus for any [tex]c, d[/itex] we note that<br /> <br /> [tex]\biggr\|\frac{c}{\|c\|}\biggr\| = \biggr\| \frac{d}{\|d\|} \biggr\| = 1[/tex]<br /> <br /> and so by the previous argument,<br /> <br /> [tex]\frac{(c, d)}{\|c\|\|d\|} = \left(\frac{c}{\|c\|}, \frac{d}{\|d\|}\right) \leq \biggr\|\frac{c}{\|c\|}\biggr\|\biggr\|\frac{d}{\|d\|}\biggr\| = \frac{\|c\|\|d\|}{\|c\|\|d\|}[/tex]<br /> <br /> [tex]\Longrightarrow (c, d) \leq \|c\| \|d\| \; \forall c, d \in V[/tex]<br /> <br /> which is the well-known <b>Cauchy-Schwarz inequality</b>.<br /> <br /> From here, we very simply <i>define</i> that<br /> <br /> [tex](c, d) = \|c\|\|d\| \cos{\theta}[/tex]<br /> <br /> it's just like any other definition you ever will encounter. You can then derive all the properties of the [itex]\cos[/itex] function.[/tex]
 
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