Reading guide · Proof index

Completing the elementary inputs actually used

This companion proves elementary sequence, determinant and positive-root facts used in finite-dimensional compactness, contraction and smooth calculus. The Archimedean and square-root arguments are written from the stated real-field axioms and natural-number induction.

Mathematical source: Jiří Lebl, Basic Analysis, theorem numbering from version 6.3, freely accessible author edition, §§1.2, 2.1–2.4, 7.2–7.6 and 8.2. The human-proof references identify external statements and proofs. The programme proof index locates the local arguments and their inputs. Original programme exposition is CC0 to the extent rights exist; cited human works retain their own terms.

The underlying definitions are those of an ordered field of real numbers with the least-upper-bound axiom, natural-number induction, finite sums, functions, sets and sequences. These are the declared axioms and definitions of the arguments, rather than additional theorems asserted without proof. No claim about constructing or uniquely characterizing that field is used here. For a nonempty set bounded below, its infimum exists by applying the supremum axiom to its negative: if u=sup⁡(−A)u=\sup(-A), then −u-u is a lower bound of AA, and every lower bound bb satisfies −b≥u-b\geq u, hence b≤−ub\leq-u.

P6. The short omissions in the compactness and contraction chain

P6.0. The Archimedean argument used by these proofs

This follows the argument of Lebl's Theorem 1.2.4(i), with its hypothesis explicit. The natural numbers are not bounded above in the ordered complete field. Otherwise b=sup⁡Nb=\sup\mathbb N would exist. Since b−1<bb-1<b, b−1b-1 is not an upper bound, so some m∈Nm\in\mathbb N satisfies m>b−1m>b-1. Then m+1∈Nm+1\in\mathbb N and m+1>bm+1>b, a contradiction. Consequently, for x>0x>0 and any yy, some nn satisfies n>y/xn>y/x, or nx>ynx>y. In particular 1/n→01/n\to0: given ε>0\varepsilon>0, choose N>1/εN>1/\varepsilon; every n≥Nn\geq N satisfies 0<1/n<ε0<1/n<\varepsilon. □\square

P6.1. Subsequence indices, metric limits and tails

If nk+1>nkn_{k+1}>n_k are positive integers, then nk≥kn_k\geq k. Indeed n1≥1n_1\geq1; if nk≥kn_k\geq k, the integer inequality gives nk+1≥nk+1≥k+1n_{k+1}\geq n_k+1\geq k+1. Thus, if xn→px_n\to p in a metric space, every subsequence converges to pp: a bound d(xn,p)<εd(x_n,p)<\varepsilon for n≥Nn\geq N also holds at nkn_k for k≥Nk\geq N. If a tail converges, take the larger of its threshold and its initial index to obtain the same bound for the original sequence. These statements complete the metric version left as Proposition 7.3.6.

For Proposition 7.3.5, if d(xn,p)≤an→0d(x_n,p)\leq a_n\to0, then, for every ε>0\varepsilon>0, eventually d(xn,p)≤an<εd(x_n,p)\leq a_n<\varepsilon, so xn→px_n\to p. Conversely take an=d(xn,p)a_n=d(x_n,p); the definition of convergence says exactly that this nonnegative real sequence tends to zero. A convergent metric sequence is bounded (Proposition 7.3.4): eventually its distance from pp is less than 1, and the maximum of 1 and the finitely many earlier distances bounds every term. □\square

P6.2. Decreasing limits and approximation to an extremum

For the decreasing half of Theorem 2.1.10, let xnx_n decrease and be bounded below, and set l=inf⁡{xn:n≥1}l=\inf\{x_n:n\geq1\}. Given ε>0\varepsilon>0, l+εl+\varepsilon cannot be a lower bound, so there is an NN with xN<l+εx_N<l+\varepsilon. Then l≤xn≤xN<l+εl\leq x_n\leq x_N<l+\varepsilon for every n≥Nn\geq N, proving xn→lx_n\to l. The increasing proof is already written in the programme source.

If S≠∅S\ne\varnothing is bounded above and s=sup⁡Ss=\sup S, for every ε>0\varepsilon>0 there is an a∈Sa\in S with s−ε<a≤ss-\varepsilon<a\leq s: otherwise s−εs-\varepsilon would be an upper bound smaller than the least one. This proves Proposition 1.2.8. Choose an∈Sa_n\in S with s−1/n<an≤ss-1/n<a_n\leq s, and put un=max⁡(a1,…,an)u_n=\max(a_1,\ldots,a_n). Each unu_n is an element of SS, the sequence is increasing, and 0≤s−un<1/n0\leq s-u_n<1/n. P6.0 implies un→su_n\to s. For an infimum tt, choose bn∈Sb_n\in S with t≤bn<t+1/nt\leq b_n<t+1/n, using the same greatest-lower-bound argument, and take the finite minima. This supplies both monotone sequences in Proposition 2.1.13, used by the extreme-value proof of Theorem 7.5.6. □\square

P6.3. The omitted lower-limit steps in the real Cauchy proof

For a bounded sequence, choose L≤xj≤UL\leq x_j\leq U for every jj. Each tail is nonempty, so its infimum bnb_n and supremum ana_n satisfy L≤bn≤an≤UL\leq b_n\leq a_n\leq U. Passing to a smaller tail cannot decrease its infimum: any lower bound of the old tail is a lower bound of the new one. Hence bn+1≥bnb_{n+1}\geq b_n. These are the omitted boundedness and monotonicity steps of Proposition 2.3.2.

The liminf half of Theorem 2.3.4 follows from its fully written limsup half with all signs justified as follows. The number −bn-b_n is the supremum of {−xj:j≥n}\{-x_j:j\geq n\}, by the definition of an infimum. If vn→vv_n\to v, then −vn→−v-v_n\to-v because ∣(−vn)−(−v)∣=∣vn−v∣|(-v_n)-(-v)|=|v_n-v|. Applying the proved limsup subsequence theorem to −xj-x_j therefore gives indices mkm_k such that −xmk→−lim⁡bn-x_{m_k}\to-\lim b_n, hence xmk→lim⁡bn=lim inf⁡xnx_{m_k}\to\lim b_n=\liminf x_n. This completes the input used in the existing full proof of Theorem 2.4.5; that Cauchy proof is retained, including its comparison of the two subsequential limits.

The subtraction case omitted in Proposition 2.2.5 uses

∣(xn−yn)−(x−y)∣≤∣xn−x∣+∣yn−y∣. |(x_n-y_n)-(x-y)|\leq|x_n-x|+|y_n-y|.

Choose both terms smaller than ε/2\varepsilon/2. Its addition, product and reciprocal proofs are already complete in the source. □\square

P6.4. Closed balls and closed complete subspaces

To complete Proposition 7.2.9, if y∉C(p,r)y\notin C(p,r), then δ=d(p,y)−r>0\delta=d(p,y)-r>0. For d(y,z)<δd(y,z)<\delta, the triangle inequality implies d(p,z)≥d(p,y)−d(y,z)>rd(p,z)\geq d(p,y)-d(y,z)>r. Thus a ball about every point of the complement stays in the complement, proving that C(p,r)C(p,r) is closed. The proof that open balls are open is already in §7.2.

To complete Proposition 7.4.6, let EE be a closed subset of a complete metric space XX. A Cauchy sequence in EE is Cauchy in XX, since its distances are unchanged. It has a limit pp in XX. The already proved Proposition 7.3.12 says p∈Ep\in E. The same distance inequalities then prove convergence in EE. In particular a closed Euclidean ball is complete, using the complete coordinatewise proof for Rn\mathbb R^n in Proposition 7.4.4. □\square

P6.5. The geometric bound inside the contraction proof

In Theorem 7.6.2 the contraction constant can be taken with 0≤k<10\leq k<1: replacing any negative constant by zero only weakens the distance bound. For 0<k<10<k<1 and a positive integer qq, expanding and cancelling finite sums gives

(1−k)∑j=0q−1kj=1−kq,0≤∑j=0q−1kj≤11−k. (1-k)\sum_{j=0}^{q-1}k^j=1-k^q, \qquad 0\leq\sum_{j=0}^{q-1}k^j\leq\frac1{1-k}.

Consequently the existing iteration estimate can use a finite sum directly:

d(xm,xn)≤kn1−kd(x1,x0),m>n. d(x_m,x_n)\leq\frac{k^n}{1-k}d(x_1,x_0),\qquad m>n.

Proposition 2.2.11 has a complete proof that kn→0k^n\to0, after the decreasing-limit step is supplied by P6.2. Given ε>0\varepsilon>0, choose NN so that the right side at n=Nn=N is smaller than ε\varepsilon. It then stays smaller for every n≥Nn\geq N, independently of m>nm>n; interchanging the indices covers n>mn>m, and equal indices have zero distance. This proves precisely the Cauchy claim used in the source. If k=0k=0, the image of the map is a single point, and the iteration is constant after its first step.

The limiting fixed-point step needs only the same Lipschitz estimate:

d(φ(x),x)≤k d(x,xn)+d(xn+1,x)⟶0. d(\varphi(x),x)\leq k\,d(x,x_n)+d(x_{n+1},x)\longrightarrow0.

Thus it does not require a separate unproved continuity theorem. The existence and uniqueness argument of the source remains unchanged. □\square

P6.6. Closed-set operations used by the closure argument

Lebl's Proposition 7.2.8 leaves the closed-set version of the topology rules as an exercise. It follows from the proved open-set rules of Proposition 7.2.6 and the following identities, verified by membership:

X∖⋂λFλ=⋃λ(X∖Fλ),X∖⋃j=1mFj=⋂j=1m(X∖Fj). X\setminus\bigcap_{\lambda}F_\lambda =\bigcup_{\lambda}(X\setminus F_\lambda),\qquad X\setminus\bigcup_{j=1}^m F_j =\bigcap_{j=1}^m(X\setminus F_j).

If the FF's are closed, their complements are open by definition. The first right side is open by the union rule, and the second by the finite-intersection rule. Hence arbitrary intersections and finite unions of closed sets are closed. The empty set and the whole space are closed because their complements are open. These steps complete the input of the existing proofs that a closure is closed and that every ball about a point of the closure meets the set (Propositions 7.2.19 and 7.2.22).

The empty-set case in the compactness arguments is immediate from the definition: an empty family of members of any cover already covers the empty set. Proofs that begin by choosing a point apply to the nonempty case. This convention also removes an implicit nonemptiness step in Theorem 7.4.11.

That theorem also refers to Proposition 7.3.5. Here is the complete metric majorant argument. A sequence xnx_n in a metric space converges to pp if and only if there are real numbers an→0a_n\to0 with d(xn,p)≤and(x_n,p)\leq a_n for every nn. For the forward implication take an=d(xn,p)a_n=d(x_n,p); the real limit assertion is exactly the metric convergence definition. Conversely, given ε>0\varepsilon>0, choose NN such that ∣an∣<ε|a_n|<\varepsilon for n≥Nn\geq N. Then 0≤d(xn,p)≤an≤∣an∣<ε0\leq d(x_n,p)\leq a_n\leq|a_n|<\varepsilon, which is metric convergence. In the compactness proof aj=1/ja_j=1/j tends to zero by the Archimedean argument P6.0. Thus that source cross-reference uses the full proof here, including both directions, rather than an unproved external result. □\square

P6.7. Compact sets and the empty-ambient boundedness convention

For a subset EE of a metric space XX, call EE bounded when there is M≥0M\geq0 such that d(x,y)≤Md(x,y)\leq M for every x,y∈Ex,y\in E. Thus the empty set is bounded, including in the empty ambient space. When X≠∅X\ne\varnothing, this agrees with containment in a ball with centre in XX. Indeed a radius bound B≥0B\geq0 about one centre gives the pairwise bound 2B2B. Conversely, if E≠∅E\ne\varnothing, choose p∈Ep\in E and use its pairwise bound as the radius about pp; if E=∅E=\varnothing, any p∈Xp\in X and radius zero suffice. An open ball can instead have radius M+1M+1. The author definition in §7.1 requires a centre in XX; the convention here specifies the case where that centre cannot exist.

Every compact subset K⊂XK\subset X is closed and bounded with this convention. If K=∅K=\varnothing, it is closed because its complement is open, and its pairwise bound can be zero. Otherwise choose p∈Kp\in K. The open balls B(p,n)B(p,n), for positive integers nn, cover KK: the Archimedean property P6.0 puts every finite distance below some integer. A finite subcover has a largest radius NN, so K⊂B(p,N)K\subset B(p,N) and d(x,y)<2Nd(x,y)<2N for x,y∈Kx,y\in K.

To prove closedness, let x∈X∖Kx\in X\setminus K. For each z∈Kz\in K put rz=d(x,z)/3>0r_z=d(x,z)/3>0. The open balls B(z,rz)B(z,r_z) cover KK; choose a finite subcover B(zj,rj)B(z_j,r_j), and put r=min⁡jrj>0r=\min_j r_j>0. If y∈Ky\in K, one of these balls contains it, and the triangle inequality gives

d(x,y)≥d(x,zj)−d(zj,y)>3rj−rj=2rj≥2r. d(x,y)\geq d(x,z_j)-d(z_j,y)>3r_j-r_j=2r_j\geq2r.

Hence B(x,r)∩K=∅B(x,r)\cap K=\varnothing. Every point outside KK has such a ball, so its complement is open and KK is closed. This proof also covers the compactness conclusion of Proposition 7.4.9 without requiring a point in an empty ambient space. □\square

P7. Why permutation parity in the determinant is well defined

This completes the parity exercise preceding Lebl's determinant formula in §8.2.3. For indeterminates t1,…,tnt_1,\ldots,t_n, put

V(t)=∏i<j(tj−ti). V(t)=\prod_{i<j}(t_j-t_i).

Interchanging two adjacent variables reverses their mutual factor and permutes all the other factors in pairs, so it multiplies VV by −1-1. Interchanging positions i<ji<j can be performed by moving position ii to jj in j−ij-i adjacent steps and then moving the old position jj back to ii in j−i−1j-i-1 steps. It therefore also changes VV by −1-1.

Every permutation is a product of adjacent interchanges: move the entry 1 to the first position, then the entry 2 to the second without changing the first, and continue. Each step moves a specified entry through only finitely many positions; after nn stages the order is the identity. Thus a permutation σ\sigma transforms V(t)V(t) into ϵσV(t)\epsilon_\sigma V(t), where ϵσ∈{1,−1}\epsilon_\sigma\in\{1,-1\}. This sign is independent of the chosen decomposition, since evaluation at (t1,…,tn)=(1,…,n)(t_1,\ldots,t_n)=(1,\ldots,n) gives the fixed nonzero quotient

ϵσ=∏i<j(σ(j)−σ(i))∏i<j(j−i). \epsilon_\sigma= \frac{\prod_{i<j}(\sigma(j)-\sigma(i))} {\prod_{i<j}(j-i)}.

Applying two permutations successively shows ϵστ=ϵσϵτ\epsilon_{\sigma\tau}=\epsilon_\sigma\epsilon_\tau: both sides are the factor by which the same substitution changes VV. Any decomposition into rr arbitrary interchanges changes VV by (−1)r(-1)^r, so (−1)r=ϵσ(-1)^r=\epsilon_\sigma. Its parity is therefore intrinsic. These facts supply exactly the signs used in the programme determinant proofs and the cofactor calculation P2. For n=1n=1, the empty product is 1 and the same conclusions hold. □\square

P8. Positive square roots without a smooth-inverse circularity

The existence argument generalizes the actually read square-root example in Lebl §1.2; the general root there is left as an exercise. Fix r>0r>0 and let A={x≥0:x2≤r}A=\{x\geq0:x^2\leq r\}. This set is nonempty and bounded above by r+1r+1. It contains a positive point, for example min⁡(r,1)/2\min(r,1)/2. Therefore s=sup⁡A>0s=\sup A>0 exists.

If s2<rs^2<r, choose

0<h≤min⁡(1,r−s22(2s+1)). 0<h\leq\min\left(1,\frac{r-s^2}{2(2s+1)}\right).

Then (s+h)2−s2=h(2s+h)≤(r−s2)/2<r−s2(s+h)^2-s^2=h(2s+h)\leq(r-s^2)/2<r-s^2, so s+h∈As+h\in A, contradicting that ss is an upper bound. If s2>rs^2>r, choose

0<h≤min⁡(s2,s2−r4s). 0<h\leq\min\left(\frac s2,\frac{s^2-r}{4s}\right).

Then s−h>0s-h>0, and (s−h)2≥s2−2sh≥(s2+r)/2>r(s-h)^2\geq s^2-2sh\geq(s^2+r)/2>r. Every nonnegative x≥s−hx\geq s-h has x2>rx^2>r, so s−hs-h is an upper bound for AA, contradicting minimality of ss. Hence s2=rs^2=r. Positive squaring is strictly increasing, since u2−v2=(u−v)(u+v)>0u^2-v^2=(u-v)(u+v)>0 when u>v≥0u>v\geq0; this also proves uniqueness. Write this root as r\sqrt r, and set 0=0\sqrt0=0. The same comparison proves monotonicity of the square root.

For r>0r>0 and t≥0t\geq0,

∣t−r∣=∣t−r∣t+r≤∣t−r∣r. |\sqrt t-\sqrt r|= \frac{|t-r|}{\sqrt t+\sqrt r}\leq\frac{|t-r|}{\sqrt r}.

This proves continuity at positive rr. At zero, 0≤t<ε20\leq t<\varepsilon^2 implies t<ε\sqrt t<\varepsilon, proving one-sided continuity there. For r+h>0r+h>0 and h≠0h\ne0, rationalizing gives

r+h−rh=1r+h+r⟶12r. \frac{\sqrt{r+h}-\sqrt r}{h} =\frac1{\sqrt{r+h}+\sqrt r}\longrightarrow\frac1{2\sqrt r}.

So the positive square root is C1C^1 on (0,∞)(0,\infty). If it is CkC^k, the reciprocal calculation in P2 and the chain rule show that its displayed derivative is CkC^k; hence it is Ck+1C^{k+1}. Induction proves smoothness without invoking the inverse function theorem whose proof later uses these roots. The derivative step inherits the declared elementary product and chain rules; it does not close all of F0-CALC. □\square

Further calculus readings

The finite-dimensional companion gives cofactor inversion and the smooth inverse upgrade. The differentiation companion gives algebraic and differential arguments, while the compact-integral companion gives mixed derivatives, integral bounds, the fundamental theorem and Taylor’s formula. Global integration, exponentials and multidimensional substitution are treated in their respective readings.