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For n1n \ge 1 all norms on Rn\mathbb{R}^n are equivalent

Statement

Let nNn \in \mathbb{N} with n1n \ge 1. Then any two norms on Rn\mathbb{R}^{n} are equivalent (Equivalent norms, and the dictionary with equivalent metrics, A norm on a real vector space, the induced metric, and the dictionary with the metric axioms).

More precisely, for every norm NN on Rn\mathbb{R}^{n} there are reals c>0c > 0 and C>0C' > 0 with

cx2    N(x)    Cx2for every xRn,c\,\lVert x\rVert_2 \;\le\; N(x) \;\le\; C'\,\lVert x\rVert_2 \qquad \text{for every } x \in \mathbb{R}^{n},

and the general statement follows because equivalence of norms is an equivalence relation.

Consequently all the metric notions on Rn\mathbb{R}^{n} are norm independent for n1n \ge 1: any two norms give the same open sets, the same convergent sequences with the same limits, the same Cauchy sequences and the same uniformly continuous maps (Equivalent norms, and the dictionary with equivalent metrics).

The hypothesis n1n \ge 1 is used twice in the proof and both uses are marked: once so that the constant CC of The finite and reverse triangle inequalities for a norm; and for n1n \ge 1 every norm NN on Rn\mathbb{R}^n satisfies N(x)Cx1N(x) \le C\lVert x\rVert_1 and is Lipschitz, hence continuous, for d2d_2 exists, and once so that the Euclidean unit sphere is nonempty, which is what the extreme value theorem needs. At n=0n = 0 the conclusion is true but vacuous, the zero space carrying exactly one norm (A norm on a real vector space, the induced metric, and the dictionary with the metric axioms), and it is not obtained from the argument below.

Facts & Assumptions

[L1]

For n1n \ge 1: C:=max{N(ek):k<n}C := \max\{N(e_k) : k<n\} exists with C0C \ge 0, N(x)Cx1N(x) \le C\lVert x\rVert_1, x1ι(n)x2\lVert x\rVert_1 \le \sqrt{\iota(n)}\lVert x\rVert_2, and NN is continuous as a map (Rn,d2)(R,dR)(\mathbb{R}^{n}, d_2) \to (\mathbb{R}, d_{\mathbb{R}}) (The finite and reverse triangle inequalities for a norm; and for n1n \ge 1 every norm NN on Rn\mathbb{R}^n satisfies N(x)Cx1N(x) \le C\lVert x\rVert_1 and is Lipschitz, hence continuous, for d2d_2 clauses 2, 3, 4).

[L2]

Equivalence of norms is an equivalence relation, and cMNCMc M \le N \le C M with c,C>0c, C > 0 is what it means (Equivalent norms, and the dictionary with equivalent metrics).

[L4]

Extreme value theorem: a continuous real-valued function on a nonempty compact metric space attains a least value (A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value).

[L6]

Balls, openness and boundedness (Open ball, closed ball and sphere in a metric space, The metric topology: a set is open when every one of its points has a ball around it inside the set; closed means open complement, Bounded subset, diameter, distance from a point to a set, and distance between two sets in a metric space): UU is open when every point of UU has a ball inside UU; AA is bounded when A=A = \emptyset or AB(x0,r)A \subseteq B(x_0,r) for some x0x_0 and real r>0r>0.

[L7]

The norm axioms (N1) and (N2), and nonnegativity of a norm (A norm on a real vector space, the induced metric, and the dictionary with the metric axioms).

[L9]

Inverses: u>0u > 0 gives u1>0u^{-1} > 0, and trichotomy of the order of R\mathbb{R} (Inverses of positives are positive, and reciprocation reverses order, Complete ordered field (least-upper-bound property)).

Proof

technique · direct
1.1

The singleton {1}R\{1\} \subseteq \mathbb{R} is closed: if y1y \ne 1 then r:=y1>0r := |y-1| > 0 and the ball B(y,r)B(y,r) omits 11, so the complement of {1}\{1\} is open.

L6L9
1.2

2\lVert\cdot\rVert_2 is itself a norm on Rn\mathbb{R}^{n}, so by [L1] applied to it, 2:(Rn,d2)(R,dR)\lVert\cdot\rVert_2 : (\mathbb{R}^{n},d_2) \to (\mathbb{R},d_{\mathbb{R}}) is continuous.

L1L7
1.3

SB(0,2)S \subseteq B(0,2), since xSx \in S gives d2(x,0)=x2=1<2d_2(x,0) = \lVert x\rVert_2 = 1 < 2; so SS is bounded.

L6L7
1.4

e0Se_0 \in S, because e02=1\lVert e_0\rVert_2 = 1; this is where n1n \ge 1 is used, since for n=0n = 0 there is no index 0<n0 < n and no such vector. So SS \ne \emptyset.

L8
1.5

For every aSa \in S and every real ε>0\varepsilon > 0, a δ>0\delta > 0 witnessing continuity of NN at aa as a map on Rn\mathbb{R}^{n} also witnesses it for the restriction NSN|_S on the metric subspace (S,dS)(S, d_S), because dSd_S is the restriction of d2d_2 and the condition is quantified over fewer points; so NSN|_S is continuous.

L1L10
1.6

Put C:=Cι(n)+1C' := C\sqrt{\iota(n)} + 1, a real >0> 0. By [L1], N(x)Cx1Cι(n)x2Cx2N(x) \le C\lVert x\rVert_1 \le C\sqrt{\iota(n)}\lVert x\rVert_2 \le C'\lVert x\rVert_2, the last step because x20\lVert x\rVert_2 \ge 0.

L1L7
2.1

SS is the preimage of {1}\{1\} under the continuous 2\lVert\cdot\rVert_2, hence closed in Rn\mathbb{R}^{n}.

step 1.1step 1.2L5
3.1

SS is a compact subset of (Rn,d2)(\mathbb{R}^{n},d_2), being closed and bounded.

step 1.3step 2.1L3
4.1

By the extreme value theorem applied to the nonempty compact metric space (S,dS)(S,d_S) and the continuous NSN|_S, there is xminSx_{\min} \in S with N(xmin)N(x)N(x_{\min}) \le N(x) for every xSx \in S; put c:=N(xmin)c := N(x_{\min}).

step 1.4step 1.5step 3.1L4
5.1

c>0c > 0: from xminSx_{\min} \in S we get xmin2=10\lVert x_{\min}\rVert_2 = 1 \ne 0, so xmin0x_{\min} \ne 0 by (N1) for 2\lVert\cdot\rVert_2, so N(xmin)0N(x_{\min}) \ne 0 by (N1) for NN, and N(xmin)0N(x_{\min}) \ge 0; trichotomy leaves c>0c > 0.

step 4.1L7L9
5.2

Let x0x \ne 0. Then x2>0\lVert x\rVert_2 > 0 by (N1) and nonnegativity, so t:=1/x2>0t := 1/\lVert x\rVert_2 > 0 and u:=txu := t\,x satisfies u2=tx2=1\lVert u\rVert_2 = |t|\,\lVert x\rVert_2 = 1 by (N2); hence uSu \in S and cN(u)=tN(x)=N(x)/x2c \le N(u) = |t|\,N(x) = N(x)/\lVert x\rVert_2, that is cx2N(x)c\,\lVert x\rVert_2 \le N(x).

step 4.1L7L9
6.1

For x=0x = 0 both cx2c\lVert x\rVert_2 and N(x)N(x) are 00 by (N1), so cx2N(x)c\lVert x\rVert_2 \le N(x) holds for every xRnx \in \mathbb{R}^{n}.

step 5.2L7
7.1

Steps 5.1, 6.1 and 1.6 give cx2N(x)Cx2c\lVert x\rVert_2 \le N(x) \le C'\lVert x\rVert_2 with c,C>0c, C' > 0, so every norm NN on Rn\mathbb{R}^{n} is equivalent to 2\lVert\cdot\rVert_2.

step 5.1step 6.1step 1.6L2
8.1

Given two norms MM and NN on Rn\mathbb{R}^{n}, each is equivalent to 2\lVert\cdot\rVert_2 by step 7.1, so MM is equivalent to NN by symmetry and transitivity of the relation.

step 7.1L2

Remarks

Depends on

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Dependency tree · next 3 levels

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