Difference between revisions of "Euclidean Spaces: Algebraic Structure and Inner Product"

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==Euclidean n-Space==
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So far we have looked strictly at <math>\mathbb{R}</math> - the set of real numbers. We will now extend our reach to higher dimensions and looked at Euclidean <math>n</math>-space.
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<blockquote style="background: white; border: 1px solid black; padding: 1em;">
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:'''Definition:''' For each positive integer <math>n</math>, the '''Euclidean <math>n</math>-Space''' denoted <math>\mathbb{R}^n</math> is the set of all points <math>\mathbf{x} = (x_1, x_2, ..., x_n)</math> such that <math>x_1, x_2, ..., x_n \in \mathbb{R}</math>. The <math>k^{\mathrm{th}}</math> coordinate of the point <math>\mathbf{x}</math> is the real number <math>x_k</math>.
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</blockquote>
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In the case where <math>n = 1</math> we have that Euclidean 1-space is simply the real line <math>\mathbb{R}</math>. When <math>n = 2</math> we are looking at points <math>(x, y)</math> in the plane, and when <math>n = 3</math> we are looking in at points <math>(x, y, z)</math> in three-dimensional space.
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The graphic in this [http://mathonline.wdfiles.com/local--files/euclidean-n-space/Screen%20Shot%202015-09-13%20at%209.25.03%20AM.png link] illustrates how we can visualize Euclidean <math>n</math>-space for <math>n = 1, 2, 3</math>.
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Of course when <math>n \geq 4</math> it is practically impossibly to visualize Euclidean <math>n</math>-space and so, we will usually talk merely about the points (or vectors) which make up the space. Like with the cases above, the point <math>\mathbf{x} = (x_1, x_2, ..., x_n)</math> for <math>x_1, x_2, ..., x_n \in \mathbb{R}</math> symbolically imply the existence of <math>n</math> mutually perpendicular axes that intersect at a point called the origin we denote by:
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<div style="text-align: center;"><math>\begin{align} \quad \mathbf{0} = (0, 0, ..., 0) \end{align}</math></div>
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The point <math>\mathbf{x}</math> is described to be located in respect to <math>\mathbf{0}</math>, i.e., the point <math>\mathbf{x}</math> is located <math>x_1</math> along the first axis, <math>x_2</math> along the the second axis, &#8230;, <math>x_n</math> along the <math>n^{\mathrm{th}}</math> axis. Sometimes we instead prefer to visualize <math>\mathbf{x}</math> as a vector (arrow) the starts at the origin and whose arrowhead ends at the point <math>(x_1, x_2, ..., x_n)</math>.
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==Basic Operations Euclidean n-Space==
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In a moment we will look at some operations defined on Euclidean n-space that the reader should already be familiar with. Before we do though, the reader should note that all of the operations defined below are in compliance to the field axioms of the real numbers in that all of the operations below are all in conjunction with the operations <math>+</math> of addition and <math>\cdot</math> of multiplication of reals.
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<blockquote style="background: white; border: 1px solid black; padding: 1em;">
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:'''Definition:''' If <math>\mathbb{x} = (x_1, x_2, ..., x_n), \mathbb{y} = (y_1, y_2, ..., y_n) \in \mathbb{R}^n</math> then we define '''Equality''' <math>\mathbf{x} = \mathbf{y}</math> if and only if <math>x_k = y_k</math> for all <math>k \in \{ 1, 2, ..., n \}</math>.
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</blockquote>
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For example, if <math>\mathbf{x} = (1, 4, 7)</math> and <math>\mathbf{y} = (1, 3, 7)</math> then <math>\mathbf{x} \neq \mathbf{y}</math> since <math>4 \neq 3</math>.
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<blockquote style="background: white; border: 1px solid black; padding: 1em;">
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: '''Definition:''' If <math>\mathbf{x} = (x_1, x_2, ..., x_n), \mathbf{y} = (y_1, y_2, ..., y_n) \in \mathbb{R}^n</math> then '''Addition''' is defined to be <math>\mathbf{x} + \mathbf{y} = (x_1 + y_1, x_2 + y_2, ..., x_n + y_n)</math> and <strong>Subtraction</strong> is defined to be <math>\mathbf{x} - \mathbf{y} = (x_1 - y_1, x_2 - y_2, ..., x_n - y_n)</math>.
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</blockquote>
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For example, consider the points <math>\mathbf{x} = (1, 4, 2, 6), \mathbf{y} = (3, -2, 0.5, \pi) \in \mathbb{R}^4</math>. Then:
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<div style="text-align: center;"><math>\begin{align} \quad \mathbf{x} + \mathbf{y} = (1+3, 4 +(-2), 2 + 0.5, 6 + \pi) = (4, -2, 2.5, 6 + \pi) \end{align}</math></div>
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And furthermore we have that:
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<div style="text-align: center;"><math>\begin{align} \quad \mathbf{x} - \mathbf{y} = (1 - 3, 4 - (-2), 2 - 0.5, 6 - \pi) = (-2, 6, 1.5, 6 -\pi) \end{align}</math></div>
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Note that in general <math>\mathbf{x} + \mathbf{y} \neq \mathbf{y} + \mathbf{x}</math> which we are already familiar with in the case when <math>n = 1</math>.
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<blockquote style="background: white; border: 1px solid black; padding: 1em;">
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:'''Definition:''' If <math>\mathbf{x} = (x_1, x_2, ..., x_n) \in \mathbb{R}^n</math> then '''Scalar Multiplication''' by the scalar <math>a \in \mathbb{R}</math> is defined to be
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:<math>a \mathbf{x} = a(x_1, x_2, ..., x_n) = (ax_1, ax_2, ..., ax_n)</math>.
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</blockquote>
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For example, consider the point <math>\mathbf{x} = (1, 2, 3, 4, 5) \in \mathbb{R}^5</math> and <math>a = 2 \in \mathbb{R}</math>. Then:
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<div style="text-align: center;"><math>\begin{align} \quad a\mathbf{x} = 2(1, 2, 3, 4, 5) = (2, 4, 6, 8, 10) \end{align}</math></div>
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==The Euclidean Inner Product==
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<blockquote style="background: white; border: 1px solid black; padding: 1em;">
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:'''Definition:''' Let <math>\mathbf{x} = (x_1, x_2, ..., x_n), \mathbf{y} = (y_1, y_2, ..., y_n) \in \mathbb{R}^n</math>. Then '''Euclidean Inner Product''' between <math>\mathbf{x}</math> and <math>\mathbf{y}</math> denoted <math>\mathbf{x} \cdot \mathbf{y}</math> is defined to be <math>\displaystyle{\mathbf{x} \cdot \mathbf{y} = x_1y_1 + x_2y_2 + ... + x_ny_n = \sum_{i=1}^{n} x_iy_i}</math>.
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</blockquote>
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''Another term for the Euclidean inner product is simply "Dot Product".''
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Note that when <math>n = 1</math> that the Euclidean inner product is simply the operation <math>\cdot</math> of multiplication. Let's look at an example for when <math>n > 1</math>. Consider the points <math>\mathbf{x} = (1, 4, 9, 16), \mathbf{y} = (-4, -3, -2, -1) \in \mathbb{R}^4</math>. Then the dot product <math>\mathbf{x} \cdot \mathbf{y}</math> is:
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<div style="text-align: center;"><math>\begin{align} \quad \mathbf{x} \cdot \mathbf{y} = (1 \cdot (-4), 4 \cdot (-3), 9 \cdot (-2), 16 \cdot (-1)) = (-4, -12, -18, -16) \end{align}</math></div>
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We will now look at some nice properties of the Euclidean inner product that can be derived by the field axioms of <math>\mathbb{R}</math>.
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<blockquote style="background: white; border: 1px solid black; padding: 1em;">
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:'''Theorem 1:''' If <math>\mathbf{x} = (x_1, x_2, ..., x_n), \mathbf{y} = (y_1, y_2, ..., y_n)</math> and <math>a \in \mathbb{R}</math> then:
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:'''a)''' <math>\mathbf{x} \cdot \mathbf{y} = \mathbf{y} \cdot \mathbf{x}</math>.
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:'''b)''' <math>(a \mathbf{x}) \cdot \mathbf{y} = \mathbf{x} \cdot (a \mathbf{y})</math>.
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</blockquote>
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*'''Proof of a)''' Let <math>\mathbf{x}, \mathbf{y} \in \mathbb{R}^n</math>. Then:
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<div style="text-align: center;"><math>\begin{align} \quad \mathbf{x} \cdot \mathbf{y} = x_1y_1 + x_2y_2 + ... + x_ny_n \end{align}</math></div>
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*By the commutativity of multiplication, we have that:
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<div style="text-align: center;"><math>\begin{align} \quad = y_1x_1 + y_2x_2 + ... + y_nx_n = \mathbf{y} \cdot \mathbf{x} \quad \blacksquare \end{align}</math></div>
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*'''Proof of b)''' Let <math>\mathbf{x}, \mathbf{y} \in \mathbb{R}^n</math> and <math>a \in \mathbb{R}</math>. Then:
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<div style="text-align: center;"><math>\begin{align} \quad (a\mathbf{x}) \cdot \mathbf{y}) = (ax_1, ax_2, ... ax_n) \cdot (y_1, y_2, ..., y_n) = ax_1y_1 + ax_2y_2 + ... + ax_ny_n \end{align}</math></div>
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*Once again, by the commutativity of multiplication, we have that:
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<div style="text-align: center;"><math>\begin{align} \quad = x_1(ay_1) + x_2(ay_2) + ... + x_n(ay_n) = \mathbf{x} \cdot (a \mathbf{y}) \quad \blacksquare \end{align}</math></div>
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==Licensing==
 
==Licensing==
 
Content obtained and/or adapted from:
 
Content obtained and/or adapted from:

Latest revision as of 10:26, 30 October 2021

Euclidean n-Space

So far we have looked strictly at - the set of real numbers. We will now extend our reach to higher dimensions and looked at Euclidean -space.

Definition: For each positive integer , the Euclidean -Space denoted is the set of all points such that . The coordinate of the point is the real number .

In the case where we have that Euclidean 1-space is simply the real line . When we are looking at points in the plane, and when we are looking in at points in three-dimensional space.

The graphic in this link illustrates how we can visualize Euclidean -space for .

Of course when it is practically impossibly to visualize Euclidean -space and so, we will usually talk merely about the points (or vectors) which make up the space. Like with the cases above, the point for symbolically imply the existence of mutually perpendicular axes that intersect at a point called the origin we denote by:

The point is described to be located in respect to , i.e., the point is located along the first axis, along the the second axis, …, along the axis. Sometimes we instead prefer to visualize as a vector (arrow) the starts at the origin and whose arrowhead ends at the point .

Basic Operations Euclidean n-Space

In a moment we will look at some operations defined on Euclidean n-space that the reader should already be familiar with. Before we do though, the reader should note that all of the operations defined below are in compliance to the field axioms of the real numbers in that all of the operations below are all in conjunction with the operations of addition and of multiplication of reals.

Definition: If then we define Equality if and only if for all .

For example, if and then since .

Definition: If then Addition is defined to be and Subtraction is defined to be .

For example, consider the points . Then:

And furthermore we have that:

Note that in general which we are already familiar with in the case when .

Definition: If then Scalar Multiplication by the scalar is defined to be
.

For example, consider the point and . Then:

The Euclidean Inner Product

Definition: Let . Then Euclidean Inner Product between and denoted is defined to be .

Another term for the Euclidean inner product is simply "Dot Product".

Note that when that the Euclidean inner product is simply the operation of multiplication. Let's look at an example for when . Consider the points . Then the dot product is:

We will now look at some nice properties of the Euclidean inner product that can be derived by the field axioms of .

Theorem 1: If and then:
a) .
b) .
  • Proof of a) Let . Then:
  • By the commutativity of multiplication, we have that:
  • Proof of b) Let and . Then:
  • Once again, by the commutativity of multiplication, we have that:


Licensing

Content obtained and/or adapted from: