Perpendicular projections and reflections with respect to affine subspaces
How to calculate a projection of a vector onto an affine subspace and its image under reflection with respect to such a subspace? Simply bring it to the know case of linear subspaces, calculate, and go back to the initial setting. In other words translate everything to make the considered affine subspace go through zero, calculate the projection and translate everything backwards.
E.g. let us calculate the projection of onto . First we calculate the projection of onto :
And translate this projection backwards, so the final result is: .
We can define a distance between two points , as .
Notice that iff , and for every points , .
Orthogonal basic system
A basic system of an affine space , is called orthogonal (respectively, orthonormal), if is an orthogonal basis (respectively, orthonormal) of .
Distance between a point and an affine subspace.
Distance between a point and an affine subspace is the distance between , and its projection onto .
Parallelepipeds and simplexes
Let oraz are linearly independent. Then the set
is called a -parallelepiped given by this system.
Given affine independent system of points , the set
is called a -dimensional simplex with vertices in . Obviously, one dimensional simplex is a line segment, two dimensional is a triangle, and three-dimensional is a tetrahedron.
-dimension measure of parallelepipeds and simplexes
-dimensional measure is a generalization of length (), area () and volume ().
To explain how to calculate the -dimensional measure of a -dimensional simplex or parallelepiped, we have to notice that if is a system of linearly independent vectors in , and is the projection of onto , then
We have , where . Let . Thus,
But the first is equal to zero because the last column is a combination of the other columns. And the second is equal to (expansion by the last column). Thus
Since, we know that -dimensional measure of a parallelepiped should be the product of -dimension measure of its base by its height (length of an appropriate projection), using induction you can easily come to the conclusion that
Orientation of a space
We say that bases , of a vector space are consistently oriented if , and inconsistently oriented if . Being consistently oriented is thus an equivalence relation on the set of all bases which has two equivalence classes.
An orientation of a vector space is choosing one of those two equivalence classes (e.g. by choosing a basis). Then bases from this equivalence class are said to have positive orientation and all the rest have negative orientation.
Given an -dimensional Euclidean space , the vector product defines a method of completing a system of linearly independent vectors to a basis of .
We say that is the vector product of (denoted by ), if
- iff are not linearly independent,
- otherwise, is a positively oriented basis of ,