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TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passjudge pass (z-ai/glm-5.2)audited 2026-07-27
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Fubini for double series: if ∑i∑j∣aij∣ converges then both iterated sums and the sum along every bijection N→N×N converge to one and the same value

Statement

Let a:N×N→R be a doubly indexed array of reals, written aij. Assume:

(H) for every i the series ∑j∣aij∣ converges, with sum Ai; and the series ∑iAi converges, with sum L.

Then, with J:N→N×N any bijection (N×N≈N, Injection, surjection, bijection):

  1. ∑naJ(n) converges absolutely (Absolutely convergent and conditionally convergent series, and the general starting index), and its sum S is the same for every such bijection (Dirichlet's rearrangement theorem: an absolutely convergent series converges unconditionally, and every rearrangement of it has the same sum);
  2. for every i the series ∑jaij converges, say to Ri; the series ∑iRi converges absolutely; and ∑i=0∞Ri=S;
  3. for every j the series ∑i∣aij∣ converges and ∑iaij converges, say to Cj; the series ∑jCj converges absolutely; and ∑j=0∞Cj=S.

In particular the two iterated sums exist and agree:

∑i=0∞(∑j=0∞aij)  =  ∑j=0∞(∑i=0∞aij)  =  ∑n=0∞aJ(n).

The hypothesis is on the absolute values, and it is stated as an iterated condition, not as an unqualified "double sum". Each row must be absolutely summable, and the row totals must themselves be summable. Without it the two iterated sums may both exist and differ, which is FALSE: whenever both iterated sums of a double array exist, they are equal.

Facts & Assumptions

Given: An array a:N×N→R satisfying (H), with row totals Ai and L=∑i=0∞Ai, and a bijection J:N→N×N.

[L1]

Finite sums: the empty sum is 0, ∑k<n+1xk=∑k<nxk+xn, finite sums are additive, monotone in their terms, and may be split at any intermediate index (Finite sums and finite products, by recursion, Laws of finite sums and finite products).

[L2]

For a series of nonnegative terms, convergence is equivalent to the range of the partial sums being bounded above; then the sum is the supremum of that range, every partial sum is at most the sum, and the partial sums converge to it (A series of nonnegative terms converges iff its partial sums are bounded, and then the sum is their supremum, Series, partial sums, convergence and the sum, divergence, and the tail series).

[L4]

∣∑k<nxk∣≤∑k<n∣xk∣ (Triangle inequality for finite sums).

[L5]

Absolute value: ∣x∣≥0, −∣x∣≤x≤∣x∣, and ∣x∣=0 exactly when x=0 (Basic properties of the absolute value).

[L6]

The principle of induction on N (The principle of mathematical induction).

[L7]

A bijection is an injective surjection; N×N admits a bijection with N (Injection, surjection, bijection, N×N≈N).

[L10]

Proof

technique · direct
1.1givenL1L2

Rectangles are bounded by L. For all P,Q∈N one has ∑i<P∑j<Q∣aij∣≤∑i<PAi≤L, since each inner sum is a partial sum of the convergent nonnegative series ∑j∣aij∣ and so is at most Ai, and finite sums are monotone.

1.2L1L7

Single points. Let d:N×N→R vanish except at one pair (p,q), let N∈N and let ρ be injective on {n:n<N} with values in N×N. If (p,q)=ρ(n0) for some (necessarily unique) n0<N, then ∑n<Ndρ(n)=dpq; otherwise ∑n<Ndρ(n)=0. Both follow by splitting the sum at n0 and at n0+1, all remaining terms being 0.

1.3L1L6

List dominated by a rectangle. For every N∈N, every array (cij) of nonnegative reals, all P,Q∈N and every injective ρ on {n:n<N} with values in {(i,j):i<P, j<Q}, one has ∑n<Ncρ(n)≤∑i<P∑j<Qcij. Induction on N, everything else universally quantified: at N=0 the left side is 0 and the right side is nonnegative; and passing from N to N+1, put (p,q):=ρ(N) and let c′′ agree with c except that cpq′′:=0, so that the induction hypothesis applied to c′′ and ρ restricted gives ∑n<Ncρ(n)≤∑i<P∑j<Qcij−cpq, the subtraction coming from splitting the outer sum at p and the inner one at q; adding cpq closes the induction.

1.4L6L7

Bounding indices. For every N there are P,Q with J(n)∈{(i,j):i<P, j<Q} for all n<N; and for all P,Q there is N with {(i,j):i<P, j<Q}⊆{J(n):n<N}. Both are inductions using that the order on N is total, so that finitely many naturals have a strict upper bound; the second uses surjectivity of J to name, for each pair, the index mapping onto it.

1.5givenL2L3L4L8L10

For every i the series ∑jaij converges, since ∑j∣aij∣ does; write Ri for its sum, so ∣Ri∣≤Ai by [L4] and [L10]. Hence ∑i∣Ri∣ converges by comparison with ∑iAi, and ∑iRi converges absolutely.

1.6givenL2L6choose

Let ε>0 be real. Choose P0≥1 with L−∑i<P0Ai<ε, possible because the partial sums of ∑iAi converge to L; then choose, for each i<P0, an index Qi with Ai−∑j<Qi∣aij∣<ε/P0, and let Q0 be an upper bound of the finitely many Qi, so that Ai−∑j<Q0∣aij∣<ε/P0 for every i<P0.

2.1step 1.2L1L6

Rectangle to list. Let c be an array, let P,Q,N∈N and let ρ be injective on {n:n<N} with {(i,j):i<P, j<Q}⊆{ρ(n):n<N}. Let c′ agree with c on that rectangle and vanish off it. Then ∑i<P∑j<Qcij=∑n<Ncρ(n)′. This is proved by induction on P, with an inner induction on Q: enlarging the rectangle by one column adds the single term cPQ to the left side, and changes c′ by an array vanishing except at (P,Q), which by step 1.2 adds exactly cPQ to the right side; at P=0 or Q=0 both sides are 0.

2.2step 1.1step 1.3step 1.4L2L8L9

By step 1.3 and step 1.4, every partial sum ∑n<N∣aJ(n)∣ is at most ∑i<P∑j<Q∣aij∣≤L; hence ∑n∣aJ(n)∣ converges, with sum Λ≤L, and ∑naJ(n) converges, say to S. Any two bijections N→N×N differ by a bijection of N, so by [L9] the value S does not depend on J; this is claim 1.

2.3step 1.6L1

Write D:=∑i<P0∑j<Q0aij and E:=∑i<P0∑j<Q0∣aij∣. By step 1.6 and monotonicity, E>∑i<P0(Ai−ε/P0)=∑i<P0Ai−ε>L−2ε, so L−E<2ε.

2.4step 1.4choose

By step 1.4 fix N with {(i,j):i<P0, j<Q0}⊆{J(n):n<N}, and by step 1.4 again fix P≥P0, Q≥Q0 with J(n) in the rectangle {(i,j):i<P, j<Q} for all n<N.

2.5step 1.1L1L2L10

The transposed array aijT:=aji satisfies (H): its i-th row total is ∑j∣aji∣, which converges because its partial sums ∑j<Q∣aji∣ are bounded by L by step 1.1; and the partial sums ∑i<P∑j∣aji∣ are limits of the rectangle sums ∑i<P∑j<Q∣aji∣, again bounded by L by step 1.1, so the series of row totals converges.

3.1step 2.2L1L4L10

For every N, ∣S−∑n<NaJ(n)∣≤Λ−∑n<N∣aJ(n)∣: for M>N the triangle inequality gives ∣∑n<MaJ(n)−∑n<NaJ(n)∣≤∑n<M∣aJ(n)∣−∑n<N∣aJ(n)∣≤Λ−∑n<N∣aJ(n)∣, and letting M grow, the limit preserves the two non-strict inequalities bounding the left side.

3.2step 2.1step 2.2step 2.4L1L2

Let a′ agree with a on the rectangle {(i,j):i<P0, j<Q0} and vanish off it. By step 2.1, D=∑n<NaJ(n)′ and E=∑n<N∣aJ(n)′∣; since ∣aJ(n)′∣≤∣aJ(n)∣ termwise, monotonicity gives E≤∑n<N∣aJ(n)∣≤Λ≤L.

4.1step 3.1step 2.3step 3.2

By step 3.1 and step 3.2, ∣S−∑n<NaJ(n)∣≤Λ−∑n<N∣aJ(n)∣≤L−E<2ε.

4.2step 1.1step 2.1step 1.3step 2.3step 2.4step 3.2L1L4

Also ∣∑n<NaJ(n)−D∣=∣∑n<N(a−a′)J(n)∣≤∑n<N∣(a−a′)J(n)∣≤∑i<P∑j<Q∣(a−a′)ij∣=∑i<P∑j<Q∣aij∣−E≤L−E<2ε, the middle inequality by step 1.3 and the following equality by splitting the iterated sum at P0 and at Q0, the array a−a′ agreeing with a off the small rectangle and vanishing on it.

4.3step 3.1step 1.6L1L4

For each i<P0, ∣Ri−∑j<Q0aij∣≤Ai−∑j<Q0∣aij∣<ε/P0, by the argument of step 3.1 applied to the row i; summing over i<P0 gives ∣∑i<P0Ri−D∣<ε.

4.4step 3.1step 1.5step 1.6L1L2

Writing ΣR for the sum of ∑iRi, the same argument applied to the series ∑iRi and the comparison ∣Ri∣≤Ai gives ∣ΣR−∑i<P0Ri∣≤∑i=0∞∣Ri∣−∑i<P0∣Ri∣≤L−∑i<P0Ai<ε.

5.1step 1.5step 4.1step 4.2step 4.3step 4.4L5

Combining step 4.1, step 4.2, step 4.3 and step 4.4, ∣ΣR−S∣<ε+ε+2ε+2ε=6ε. As ε>0 was arbitrary and ∣ΣR−S∣≥0, this forces ΣR=S, which with step 1.5 is claim 2.

6.1step 2.2step 5.1step 2.5L7∎

Applying claims 1 and 2 to aT and to the bijection JT obtained by exchanging the coordinates of J gives claim 3, since aJT(n)T=aJ(n) for every n, so the two linear series are the same series and have the same sum S.

Remarks

  • What the finite bookkeeping of steps 1.2 to 1.5 does, and why it is proved. Three facts are needed and none of them is among the laws of Laws of finite sums and finite products, all of which compare sums term by term over the same index range: that a sum along an injective list picks up an isolated term exactly once; that an iterated sum over a rectangle equals the sum along any injective list containing that rectangle, of the array cut down to it; and that a sum of nonnegative terms along an injective list into a rectangle is at most the iterated sum over the rectangle. Each is proved by zeroing out one entry at a time, which keeps the argument inside those laws.

  • Where the hypothesis is used. Only through step 1.1, which bounds every rectangle by L, and through step 1.6, which makes a single rectangle capture all but 2ε of the total mass. Everything else is bookkeeping. This is why the hypothesis has to be an absolute one: for a signed array no rectangle captures the mass, and the two iterated sums can disagree.

  • The independence of the enumeration is Dirichlet's rearrangement theorem: an absolutely convergent series converges unconditionally, and every rearrangement of it has the same sum and nothing more. Two bijections N→N×N differ by a bijection of N, and an absolutely convergent series is unconditionally convergent. So the "sum of the array" is a well-defined real number attached to the array itself, and the theorem says the two iterated sums compute it.

Depends on

Dependency tree · 2 levels

Sources