Real Numbers

This page serves as an introduction to the real numbers in an axiomatic way. Note we do not claim here that the real numbers exist. We shall construct the real numbers more carefully from ZF set theory later.

Definition 1. A set \(\mathbb{R}\) is called a set of real numbers if \(\mathbb{R}\) admits binary operations \(+,\cdot\) and a strict total order relation \(<\) satisfying the following axioms.

Axiom 1 (Associativity of Addition). \[\forall a,b,c\in\mathbb{R} \quad (a+b)+c = a+(b+c)\]

Axiom 2 (Identity of Addition). \[\exists e\in\mathbb{R} \ \forall a\in\mathbb{R} \quad a+e=e+a=a\] Such an element \(e\) is called an additive identity.

Theorem 1 (Uniqueness of Additive Identity). The additive identity \(e\in\mathbb{R}\) from Axiom 2 is unique. We denote the additive identity by 0.

Proof. Suppose \(e_1,e_2\in\mathbb{R}\) are additive identities. Then, by Axiom 2 \[e_1 = e_1 + e_2\] since \(e_2\) is an additive identity. Likewise, by Axiom 2 \[e_1 + e_2 = e_2\] since \(e_1\) is an additive identity. It follows that \(e_1=e_2\). ◻

Axiom 3 (Inverse of Addition). \[\forall a\in\mathbb{R} \ \exists b\in\mathbb{R} \quad a+b=b+a=0\] Such an element \(b\) is called an additive inverse of \(a\).

Theorem 2 (Uniqueness of Additive Inverse). For each \(a\in\mathbb{R}\), the additive inverse from Axiom 2 is unique. We denote the additive inverse of \(a\) by \(-a\).

Proof. Let \(a\in\mathbb{R}\). Suppose \(b,c\in\mathbb{R}\) are additive inverses of \(a\). Then, by Axiom 2 \[b = b + 0.\] By Axiom 3 \[b + 0 = b + (a + c)\] since \(c\) is an additive inverse of \(a\). By Axiom 1 \[b + (a + c) = (b + a) + c.\] By Axiom 3 \[(b + a) + c = 0 + c\] since \(b\) is an additive inverse of \(a\). By Axiom 2 \[0 + c = c.\] It follows that \(b=c\). ◻

Axiom 4 (Commutativity of Addition). \[\forall a,b\in\mathbb{R} \quad a+b=b+a\]

Axiom 5 (Associativity of Multiplication). \[\forall a,b,c\in\mathbb{R} \quad (a\cdot b)\cdot c = a\cdot (b\cdot c)\]

Axiom 6 (Identity of Multiplication). \[\exists e\in\mathbb{R} \quad (e \neq 0) \land (\forall a\in\mathbb{R} \quad a\cdot e = e\cdot a = a)\] Such an element \(e\) is called a multiplicative identity.

Theorem 3 (Uniqueness of Multiplicative Identity). The multiplicative identity \(e\in\mathbb{R}\) from Axiom 6 is unique. We denote the multiplicative identity by 1.

Proof. Suppose \(e_1,e_2\in\mathbb{R}\) are multiplicative identities. Then, by Axiom 6 \[e_1 = e_1 \cdot e_2\] since \(e_2\) is a multiplicative identity. Likewise, by Axiom 6 \[e_1 \cdot e_2 = e_2\] since \(e_1\) is a multiplicative identity. It follows that \(e_1 = e_2\). ◻

Axiom 7 (Inverse of Multiplication). \[\forall a\in\mathbb{R}\setminus\{0\} \ \exists b\in\mathbb{R} \quad ab = ba = 1\] Such an element \(b\) is called a multiplicative inverse of \(a\).

Theorem 4 (Uniqueness of Multiplicative Inverse). For each \(a\in\mathbb{R}\setminus\{0\}\), the multiplicative inverse from Axiom 7 is unique. We denote the multiplicative inverse of \(a\) by \(a^{-1}\).

Proof. Let \(a\in\mathbb{R}\setminus\{0\}\). Suppose \(b,c\in\mathbb{R}\) are multiplicative inverses of \(a\). Then, by Axiom 6 \[b = b \cdot 1.\] By Axiom 7 \[b \cdot 1 = b \cdot (a \cdot c)\] since \(c\) is a multiplicative inverse of \(a\). By Axiom 5 \[b \cdot (a \cdot c) = (b \cdot a) \cdot c.\] By Axiom 7 \[(b \cdot a) \cdot c = 1 \cdot c\] since \(b\) is a multiplicative inverse of \(a\). By Axiom 6 \[1 \cdot c = c.\] It follows that \(b=c\). ◻

Axiom 8 (Commutativity of Multiplication). \[\forall a,b\in\mathbb{R} \quad a \cdot b = b \cdot a\]

Axiom 9 (Distributivity). \[\forall a,b,c\in\mathbb{R} \quad a \cdot (b + c) = a \cdot b + a \cdot c\]

Axiom 10 (Trichotomy of Order). \[\forall a,b\in\mathbb{R} \quad ((a < b) \veebar (a = b) \veebar (b < a)) \land \lnot ((a < b) \land (a = b) \land (b < a))\]

Axiom 10 states that there is a true statement among \(a < b\), \(a = b\), \(b < a\), and when any is true the others are false. The symbol \(\veebar\) represents the xor operation from predicate logic.

Axiom 11 (Transitivity of Order). \[\forall a,b,c\in\mathbb{R} \quad (a < b) \land (b < c) \implies (a < c)\]

Axiom 12 (Addition and Order). \[\forall a,b,c\in\mathbb{R} \quad a < b \implies a + c < b + c\]

Axiom 13 (Multiplication and Order). \[\forall a,b,c\in\mathbb{R} \quad (c > 0) \land (a < b) \implies a \cdot c < b \cdot c\]

Definition 2. We define the relation \(\leq\) on \(\mathbb{R}\) by \[\forall a,b\in\mathbb{R} \quad a\leq b \iff (a < b) \lor (a = b).\]

Theorem 5. The relation \(\leq\) on \(\mathbb{R}\) is a partial order relation.

Proof. Since \(<\) and \(=\) are reflexive and transitive, so is \(\leq\). Supose \(a,b\in\mathbb{R}\) with \(a\leq b\) and \(b\leq a\). By Axiom 10, we cannot have \(a = b\) and either of \(a < b\) or \(b < a\) simultaneously, so it must be the case that \(a = b\). Then \(\leq\) is antisymmetric. ◻

Definition 3. A subset \(A\subseteq\mathbb{R}\) is bounded above if \[\exists b\in\mathbb{R} \ \forall a\in A \quad a \leq b.\] Such a real number \(b\) is called an upper bound of \(A\).

Definition 4. An upper bound \(c\in\mathbb{R}\) of a subset \(A\subseteq\mathbb{R}\) is a supremum, or least upper bound, of \(A\) if for all upper bounds \(b\in\mathbb{R}\) of \(A\), \(c \leq b\).

Theorem 6 (Uniqueness of Supremum). If \(c\in\mathbb{R}\) is a supremum of \(A \subseteq\mathbb{R}\), then it is unique. We denote \(c\) by \(\sup A\).

Proof. Suppose \(c_1,c_2\in\mathbb{R}\) are supremums of \(A \subseteq\mathbb{R}\). Then \(c_1\) is an upper bound of \(A\). Since \(c_2\) is a supremum of \(A\) \[c_2 \leq c_1.\] Likewise, \(c_2\) is an upper bound of \(A\). Since \(c_1\) is a supremum of \(A\) \[c_1 \leq c_2.\] Since \(\leq\) is antisymmetric, it follows that \(c_1 = c_2\). ◻

Axiom 14 (Completeness). If \(A\subseteq\mathbb{R}\) is nonempty and bounded above, then \(\sup A\in\mathbb{R}\). \[\forall A\subseteq\mathbb{R} \quad (A \neq \emptyset) \land (\exists b\in\mathbb{R} \ \forall a\in A \quad a \leq b) \implies \exists c\in\mathbb{R} \quad c = \sup A\]

Definition 5. For each \(x\in\mathbb{R}\), we define the absolute value of \(x\), denoted \(|x|\), as \[|x| = \begin{cases} x & x\geq 0\\ -x & x<0. \end{cases}\]

Theorem 7 (Triangle Inequality). If \(x,y\in\mathbb{R}\), then \(|x+y|\leq |x|+|y|\).

Proof. Let \(x,y\in\mathbb{R}\). Without loss of generality, suppose \(x \leq y\). We have four cases to consider.
Case 1: Suppose \(0 \leq x \leq y\). Then \(x+y \geq 0\) and so \[|x+y| = x+y = |x|+|y|.\] Case 2: Suppose \(x \leq y < 0\). Then \(x+y < 0\) and so \[|x+y| = -(x+y) = -x-y = |x|+|y|.\] Case 3: Suppose \(x < 0 \leq y\) with \(|x|\leq |y|\). Then \(x+y \geq 0\) and so \[|x+y| = x+y = -|x|+|y| \leq |x|+|y|.\] Case 4: Suppose \(x < 0 \leq y\) with \(|y| < |x|\). Then \(x+y < 0\) and so \[|x+y| = -(x+y) = -x-y = |x|-|y| \leq |x|+|y|.\] In any case, \(|x+y| \leq |x|+|y|\). ◻

Theorem 8 (Reverse Triange Inequality). If \(a,b\in\mathbb{R}\), then \(||a|-|b||\leq |a-b|\).

Proof. Let \(a,b\in\mathbb{R}\). By the triangle inequality \[|b| = |a+(b-a)| \leq |a| + |b-a| \quad\text{and}\quad |a| = |b+(a-b)| \leq |b| + |a-b|.\] Then \[|b|-|a| \leq |b-a| \quad\text{and}\quad |a|-|b| \leq |a-b|.\] It follows that \[-|a-b| = -|b-a| \leq |a|-|b| \leq |a-b|\] and so \[||a|-|b|| \leq |a-b|.\] ◻

Theorem 9 (Positive Definiteness). If \(x\in\mathbb{R}\), then \(|x|\geq 0\). Additionaly, \(|x|=0\) if and only if \(x=0\).

Proof. Let \(x\in\mathbb{R}\). If \(x\geq 0\), then \(|x|=x\geq 0\). If \(x<0\), then \(|x|=-x>0\). It follows that \(|x|\geq 0\). Moreover, \(|x|=0\) if and only if \(x\geq 0\) and \(x=0\) or \(x<0\) and \(-x=0\). Only one case is possible, from which it follows \(|x|=0\iff x=0\). ◻

Theorem 10 (Absolute Homogeneity). If \(c,x\in\mathbb{R}\), then \(|c\cdot x| = |c|\cdot|x|\).

Proof. Let \(c,x\in\mathbb{R}\). Without loss of generality, suppose \(c\leq x\). We have three cases to consider.
Case 1: Suppose \(0 \leq c \leq x\). Then \(c \geq 0\), \(x\geq 0\), and \(c\cdot x\geq 0\). It follows that \[|c\cdot x| = c\cdot x = |c|\cdot|x|.\] Case 2: Suppose \(c \leq x < 0\). Then \(c < 0\), \(x < 0\), and \(c\cdot x > 0\). It follows that \[|c\cdot x| = c\cdot x = (-c)\cdot (-x) = |c|\cdot|x|.\] Case 3: Suppose \(c < 0 \leq x\). Then \(c < 0\), \(x\geq 0\), and \(c\cdot x \leq 0\). It follows that \[|c\cdot x| = -(c\cdot x) = (-c)\cdot x = |c|\cdot|x|.\] In any case, \(|c\cdot x| = |c|\cdot|x|\). ◻

Corollary 11. The absolute value is a norm on \(\mathbb{R}\). In particular, \((\mathbb{R}, |\cdot|)\) is a normed space.

Proof. It follows from positive definiteness, homogeneity, and the triangle inequality that \(|\cdot|\) is a norm on \(\mathbb{R}\). ◻

We later show that \((\mathbb{R}, |\cdot|)\) is a Banach space, and even a Hilbert space.

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