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ExampleConstruction: AI-adaptedVerification: AI-generatedSession-authored (Fable 5 assisted)precheck passjudge pass (z-ai/glm-5.2)audited 2026-07-29
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The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

A convergent series in R2\mathbb{R}^{2} with Γ\Gamma a line and Γ\Gamma^{\perp} a line, computed from the definition

Example

Let (εk)(\varepsilon_k) be the alternating sequence, the unique sequence of reals with ε0=1\varepsilon_0 = 1 and εk+1=εk\varepsilon_{k+1} = -\varepsilon_k, so εk=1|\varepsilon_k| = 1 for every kk (The even and odd index maps and the alternating sequence: strictly increasing e,oe, o with N\mathbb{N} their disjoint union, and the unique (sk)(s_k) with s0=1s_0 = 1, sσ(k)=sks_{\sigma(k)} = -s_k, which satisfies sk=1|s_k| = 1, se1s \circ e \equiv 1 and so1s \circ o \equiv -1). In R2\mathbb{R}^{2} put

xk  :=  (εkι(k+1), 0)(kN),x_k \;:=\; \Bigl(\frac{\varepsilon_k}{\iota(k+1)},\ 0\Bigr) \qquad (k \in \mathbb{N}),

with ι\iota the canonical natural (The canonical natural ι(n)=n1F\iota(n) = n \cdot 1_F of a field). Call a line through the origin the set of scalar multiples of a fixed nonzero vector; each such set is a linear subspace (Linear subspace of a vector space). Then:

  1. xk\sum x_k converges, to s=(S,0)s = (S, 0) where SS is the sum of the alternating harmonic series; the value of SS is not computed here, being a logarithm and outside this page's reach.
  2. xk\sum x_k does not converge absolutely (Series of vectors in Rn\mathbb{R}^n, absolute convergence, rearrangement, and the set of rearrangement sums).
  3. Γ={(0,t):tR}\Gamma = \{\, (0,t) : t \in \mathbb{R} \,\}, the line of multiples of e1e_1, and Γ={(t,0):tR}\Gamma^{\perp} = \{\, (t,0) : t \in \mathbb{R} \,\}, the line of multiples of e0e_0 (The subspace Γ\Gamma of directions along which a series converges absolutely, and its orthogonal complement Γ\Gamma^{\perp}).
  4. Consequently The set of rearrangement sums of a convergent series in Rn\mathbb{R}^n is a nonempty subset of the affine subspace s+Γs + \Gamma^{\perp} confines every rearrangement sum to the horizontal line s+Γ={(t,0):tR}s + \Gamma^{\perp} = \{(t,0) : t \in \mathbb{R}\}; and for this series the confinement is exact, S(x)=s+Γ\mathcal{S}(x) = s + \Gamma^{\perp}, by the published The Riemann series theorem: a conditionally convergent real series has, for every cRc \in \mathbb{R}, a rearrangement with sum cc, and rearrangements diverging to ++\infty, to -\infty, and oscillating with any prescribed lim inflim sup\liminf \le \limsup in R\overline{\mathbb{R}} applied to the first coordinate.

Clause 4 decides nothing about the general question. This series is degenerate: it lies inside a line, so its rearrangement behaviour is the one-dimensional behaviour of its first coordinate and nothing more. It is therefore not evidence about whether S(x)=s+Γ\mathcal{S}(x) = s + \Gamma^{\perp} for a series genuinely spread over Rn\mathbb{R}^{n} with n2n \ge 2, a question this library does not settle (Conventions of this page, the standing n1n \ge 1 hypothesis, and what is taken up elsewhere in the reading order).

Facts & Assumptions

Given: The sequence (xk)(x_k) above, its first coordinate sequence ck:=εk/ι(k+1)c_k := \varepsilon_k/\iota(k+1) and the sequence bk:=1/ι(k+1)b_k := 1/\iota(k+1).

[L2]

ι(k+1)>0\iota(k+1) > 0 and ι\iota is strictly increasing; 0<uv0 < u \le v gives 0<1/v1/u0 < 1/v \le 1/u; and for every real ε>0\varepsilon>0 there is KK with 1/ι(K+1)<ε1/\iota(K+1) < \varepsilon (The canonical natural ι(n)=n1F\iota(n) = n \cdot 1_F of a field, Canonical naturals are positive and strictly increasing, Inverses of positives are positive, and reciprocation reverses order, For every ε>0\varepsilon > 0 in a complete ordered field there is a natural n1n \ge 1 with 1/n<ε1/n < \varepsilon).

[L4]

The pp-series theorem: k11/kp\sum_{k\ge1}1/k^{p} converges if and only if p>1p>1; at p=1p=1 the harmonic series diverges (For rational p>0p > 0, 1/kp\sum 1/k^p converges iff p>1p > 1, Rational powers ara^r of a positive base, Series, partial sums, convergence and the sum, divergence, and the tail series).

[L7]

For c0c \ne 0, cak\sum c\,a_k converges if and only if ak\sum a_k converges (Convergent series add and scale termwise clause 3).

Verification

technique · direct
1.1

(bk)(b_k) is positive, nonincreasing and converges to 00: positivity and monotonicity from 0<ι(k+1)<ι(k+2)0 < \iota(k+1) < \iota(k+2), and convergence because for a rational ε>0\varepsilon>0 an index KK with 1/ι(K+1)<ε1/\iota(K+1)<\varepsilon gives bkbK<εb_k \le b_K < \varepsilon for kKk \ge K.

L2
1.2

The second coordinate sequence is constantly 00, so its series converges with sum 00.

L5
2.1

By the alternating series test kck=kεkbk\sum_k c_k = \sum_k \varepsilon_k b_k converges; write SS for its sum.

step 1.1L3
2.2

xk2=ck2+0=ck=εkbk=bk\lVert x_k\rVert_2 = \sqrt{c_k^{2}+0} = |c_k| = |\varepsilon_k| b_k = b_k, and kbk\sum_k b_k is the harmonic series, which diverges; so xk\sum x_k does not converge absolutely, which is clause 2.

step 1.1L1L4L6
3.1

By componentwise convergence, xk\sum x_k converges with sum s=(S,0)s = (S,0), which is clause 1.

step 2.1step 1.2L5
3.2

For a=(a0,a1)R2a = (a_0,a_1) \in \mathbb{R}^{2}: a,xk=a0ck\langle a, x_k\rangle = a_0 c_k, so a,xk=a0bk|\langle a,x_k\rangle| = |a_0|\,b_k. If a0=0a_0 = 0 every term is 00 and the series converges; if a00a_0 \ne 0 then a0>0|a_0| > 0 and convergence of ka0bk\sum_k |a_0| b_k would give convergence of kbk\sum_k b_k, which is false.

step 2.2L1L6L7
3.3

Conversely let tRt \in \mathbb{R}. The real series kck\sum_k c_k converges by step 2.1 and does not converge absolutely by step 2.2, so it converges conditionally, and the Riemann series theorem supplies a bijection σ\sigma of N\mathbb{N} with kcσ(k)=t\sum_k c_{\sigma(k)} = t. The rearranged vector series kxσ(k)\sum_k x_{\sigma(k)} has first coordinate series kcσ(k)\sum_k c_{\sigma(k)} and second coordinate series constantly 00, so by componentwise convergence it converges to (t,0)(t,0); hence (t,0)S(x)(t,0) \in \mathcal{S}(x).

step 2.1step 2.2L5L8
4.1

Hence Γ={a:a0=0}={(0,t):tR}\Gamma = \{a : a_0 = 0\} = \{(0,t) : t \in \mathbb{R}\}, the set of scalar multiples of e1e_1.

step 3.2L6
5.1

For y=(y0,y1)y = (y_0,y_1): yΓy \in \Gamma^{\perp} means (0,t),y=ty1=0\langle (0,t), y\rangle = t\,y_1 = 0 for every real tt, which at t=1t = 1 forces y1=0y_1 = 0, and conversely y1=0y_1 = 0 makes every such product 00. So Γ={(t,0):tR}\Gamma^{\perp} = \{(t,0) : t \in \mathbb{R}\}, the set of scalar multiples of e0e_0, and clause 3 is proved.

step 4.1L6
6.1

By the containment theorem, S(x)s+Γ={(S+t, 0):tR}={(w,0):wR}\mathcal{S}(x) \subseteq s + \Gamma^{\perp} = \{\,(S+t,\ 0) : t \in \mathbb{R}\,\} = \{\,(w,0) : w\in\mathbb{R}\,\}.

step 3.1step 5.1L8
7.1

Steps 6.1 and 3.3 give S(x)=s+Γ\mathcal{S}(x) = s + \Gamma^{\perp}, which is clause 4.

step 6.1step 3.3

Remarks

Depends on

Used by

Dependency tree · next 3 levels

Direct dependencies and their dependencies through the next three levels: 231 results over 44 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.

Sources