Unique set of vectors normal to a hyperplane

In summary, the point-normal representation of a space is given by n \cdot P_{0}P = 0, where n is a vector and P_{0} is a point through which the space passes. In ℝ^{2}, this represents a line, in ℝ^{3} a plane, and in ℝ^{n} a hyperplane. Conversely, the component-wise representation of a space is given by a_{1}x_{1} + a_{2}x_{2} + ... + a_{n}x_{n} = b, and the only vectors normal to this space are of the form t<a_{1},a_{2}...a_{n}> where t is a scalar parameter. This
  • #1
Bipolarity
776
2
Let's say we have a point-normal representation of a space:
[tex] n \cdot P_{0}P = 0 [/tex] where n is a vector [itex]<a_{1},a_{2}...a_{n}>[/itex] and [itex]P_{0}[/itex] is a point through which the space passes and P is the set of all points contained in the space.

In [itex]ℝ^{2}[/itex], the point-normal representation defines a line.
In [itex]ℝ^{3}[/itex], the point-normal representation defines a plane.
In [itex]ℝ^{n}[/itex], the point-normal representation defines a hyperplane, or (n-1) dimensional affine space.

It can be shown that this affine space can be represented in component form as:
[tex] a_{1}x_{1} + a_{2}x_{2} + ... + a_{n}x_{n} = b [/tex]
where b is a constant.

My question is essentially asking about the converse of the representation done above: Can we go from the component-wise representation to the point-normal representation?
In other words, can we show that the only vectors normal to the space determined by [tex] a_{1}x_{1} + a_{2}x_{2} + ... + a_{n}x_{n} = b [/tex] are vectors of the form
[itex]t<a_{1},a_{2}...a_{n}>[/itex] where t is a scalar parameter?

BiP
 
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  • #2
Hey Bipolarity.

You could use the identity that <tx,y> = t<x,y> for general inner products and if <x,y> = 0 then <tx,y> = t<x,y> = 0 for all x and y in the inner product space.
 

FAQ: Unique set of vectors normal to a hyperplane

1. What is a hyperplane?

A hyperplane is a geometric concept that represents a subspace with one less dimension than its surrounding space. In a two-dimensional space, a hyperplane is a line; in a three-dimensional space, it is a plane. In higher dimensions, a hyperplane is defined as a set of points that satisfies a linear equation.

2. What is a normal vector?

A normal vector is a vector that is perpendicular to a given surface or hyperplane. It is often represented by a line with an arrow pointing away from the surface. In the context of a hyperplane, a normal vector is a vector that is orthogonal to all the points in that hyperplane.

3. How is a unique set of vectors normal to a hyperplane determined?

A unique set of vectors normal to a hyperplane is determined by finding the null space of the matrix that represents the linear equation of the hyperplane. The null space is the set of vectors that, when multiplied by the matrix, result in a zero vector. These vectors are orthogonal to the hyperplane and form a unique set.

4. What is the significance of a unique set of vectors normal to a hyperplane?

A unique set of vectors normal to a hyperplane is significant because it helps describe the orientation and direction of the hyperplane. These vectors are also useful in determining the distance of a point from the hyperplane and in performing operations such as projection and rotation on the hyperplane.

5. Can a hyperplane have more than one unique set of vectors normal to it?

No, a hyperplane can only have one unique set of vectors normal to it. This is because the null space of a matrix is unique, and the set of vectors that make up the null space are the only vectors that are orthogonal to the hyperplane. However, a hyperplane can have multiple normal vectors that are scalar multiples of each other, meaning they have the same direction but different magnitudes.

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