Euclidean space geometry: scalar product, norm and distance

Jul 18

Euclidean space geometry: scalar product, norm and distance

Euclidean space geometry: scalar product, norm and distance

Learning this material has spillover effects for Stats because everything in this section has analogs for means, variances and covariances.

Scalar product

Definition 1. The scalar product of two vectors $x,y\in R^n$ is defined by $x\cdot y=\sum_{i=1}^nx_iy_i$ . The motivation has been provided earlier.

Remark. If matrix notation is of essence and $x,y$ are written as column vectors, we have $x\cdot y=x^Ty.$ The first notation is better when we want to emphasize symmetry $x\cdot y=y\cdot x.$

Linearity. The scalar product is linear in the first argument when the second argument is fixed: for any vectors $x,y,z$ and numbers $a,b$ one has

(1) $(ax+by)\cdot z=a(x\cdot z)+b(y\cdot z).$

Proof. $(ax+by)\cdot z=\sum_{i=1}^n(ax_i+by_i)z_i=\sum_{i=1}^n(ax_iz_i+by_iz_i)$

$=a\sum_{i=1}^nx_iz_i+b\sum_{i=1}^ny_iz_i=ax\cdot z+by\cdot z.$

Special cases. 1) Homogeneity: by setting $b=0$ we get $(ax)\cdot z=a(x\cdot z).$ 2) Additivity: by setting $a=b=1$ we get $(x+y)\cdot z=x\cdot z+y\cdot z.$

Exercise 1. Formulate and prove the corresponding properties of the scalar product with respect to the second argument.

Definition 2. The vectors $x,y$ are called orthogonal if $x\cdot y=0.$

Exercise 2. 1) The zero vector is orthogonal to any other vector. 2) If $x,y$ are orthogonal, then any vectors proportional to them are also orthogonal. 3) The unit vectors in $R^n$ are defined by $e_i=(0,...,1,...,0)$ (the unit is in the $i$ th place, all other components are zeros), $i=1,...,n.$ Check that they are pairwise orthogonal.

Norm

Exercise 3. On the plane find the distance between a point $x$ and the origin.

Figure 1. Pythagoras theorem

Once I introduce the notation on a graph (Figure 1), everybody easily finds the distance to be $\text{dist}(0,x)=\sqrt{x_1^2+x_2^2}$ using the Pythagoras theorem. Equally easily, almost everybody fails to connect this simple fact with the ensuing generalizations.

Definition 3. The norm in $R^n$ is defined by $\left\Vert x\right\Vert=\sqrt{\sum_{i=1}^nx_i^2}.$ It is interpreted as the distance from point $x$ to the origin and also the length of the vector $x$ .

Exercise 4. 1) Can the norm be negative? We know that, in general, there are two square roots of a positive number: one is positive and the other is negative. The positive one is called an arithmetic square root. Here we are using the arithmetic square root.

2) Using the norm can you define the distance between points $x,y\in R^n?$

3) The relationship between the norm and scalar product:

(2) $\left\Vert x\right\Vert =\sqrt{x\cdot x}.$

True or wrong?

4) Later on we'll prove that $\Vert x+y\Vert\leq\Vert x\Vert+\Vert{ y}\Vert .$ Explain why this is called a triangle inequality. For this, you need to recall the parallelogram rule.