Skip to content
Hub Nexus

No author yetClaim it

Version 1Updated 1 min read

Support

This chapter requires that we factor polynomials.
Of course, many polynomials do not factor over the real numbers;
for instance,
{x}^{2} + 1 does not factor into the product of two linear polynomials
with real coefficients.
For that reason, we shall from now on take our scalars from the complex
numbers.

That is, we are shifting from studying vector spaces over the real numbers
to vector spaces over the complex numbers—
in this chapter
vector and matrix entries are complex.

Any real number is a complex number and
a glance through this chapter shows that most of the examples use
only real numbers.
Nonetheless, the critical theorems require that the scalars be complex
numbers, so
the first section below is a quick review of complex numbers.

In this book we are moving to the more general context
of taking scalars to be complex only for the
pragmatic reason that we must do so in order to
develop the representation.
We will not go into using other sets of scalars in more detail because
it could distract from our goal.
However, the idea of taking scalars from a structure other than the real
numbers is an interesting one.
Delightful
presentations taking this approach are in
(Halmos 1958) and (Hoffman & Kunze 1971) harv error: no target: CITEREFHoffmanKunze1971 (help).

References

  • Halmos, Paul P. (1958), Finite Dimensional Vector Spaces (Second ed.), Van Nostrand.
  • Hoffman, Kenneth; Kunzy, Ray (1971), Linear Algebra (Second ed.), Prentice-Hall.

Where this page came from

This page was imported from Wikibooks. From “Linear Algebra” on Wikibooks, by its contributors, under CC BY-SA 4.0. Changed here: set as a page; navigation and edit links left out; each image under its own licence, credited in its caption.

Nobody has written it yet — it is the source material at a new address, which is why search engines are asked to skip it and why no one earns from it. It is up for grabs: take it on, and it is yours to rewrite and to earn from.

Comments

Spinner Logo

Next in Linear Algebra

CC BY-SA 4.0
This subsection is a review only and we take the main results as known. For proofs, see (Birkhoff & MacLane 1965) or (Ebbinghaus 1990). Just as integers have a division operation— e.g., "4 goes 5 times into 21 with remainder 1"— so do polynomials. Theorem 1.1 (Division Theorem for Polynomials) Let $
CC BY-SA 4.0
Recall the definitions of the complex number addition $( a + b i ) \ + \ ( c + d i ) = ( a + c ) + ( b + d ) i$ and multiplication. $\begin{matrix}( a + b i ) ( c + d i ) & = a c + a d i + b c i + b d ( − 1 ) \\ & = ( a c − b d ) + ( a d + b c ) i\end{matrix}$ Example 2.1 For instance, $( 1 − 2 i )
CC BY-SA 4.0
In matrix terms, ${R e p}_{D , D} ( t ) = {R e p}_{B , D} ( \text{id} ) \ {R e p}_{B , B} ( t ) \ ( {R e p}_{B , D} ( \text{id} ) {)}^{− 1}$ (recall that a representation of composition like this one reads right to left). Definition 1.1 The matrices T and S are **similar** if there is a nonsingular
CC BY-SA 4.0
The prior subsection defines the relation of similarity and shows that, although similar matrices are necessarily matrix equivalent, the converse does not hold. Some matrix-equivalence classes break into two or more similarity classes (the nonsingular $n \ × \ n$ matrices, for instance). This means
CC BY-SA 4.0
In this subsection we will focus on the property of [Corollary 2.4]( Definition 3.1 A transformation $t : V → V$ has a scalar **eigenvalue** $λ$ if there is a nonzero **eigenvector** $\overset{→}{ζ} ∈ V$ such that $t ( \overset{→}{ζ} ) = λ ⋅ \overset{→}{ζ}$. ("Eigen" is German for "characteristic of
CC BY-SA 4.0
This subsection is optional, although it is necessary for later material in this section and in the next one. A linear transformations $t : V → V$, because it has the same domain and codomain, can be iterated.[1] That is, compositions of t with itself such as ${t}^{2} = t ∘ t$ and ${t}^{3} = t ∘ t ∘