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If xkLx_k \to L then σnL\sigma_n \to L: convergence implies (C,1)(C,1)-summability to the same value

Statement

Let (xk)(x_k) be a sequence of reals that converges (Sequences of reals: bounded, eventually, frequently, tails, subsequences, Limits and Cauchy sequences of reals), and let (σn)(\sigma_n) be its sequence of Cesaro means (The Cesaro means σn=(x0++xn)/(n+1)\sigma_n = (x_0 + \dots + x_n)/(n+1) and (C,1)(C,1)-summability). Then (σn)(\sigma_n) converges as well, and

limnσn  =  limkxk.\lim_n \sigma_n \;=\; \lim_k x_k .

Both limits are asserted to exist: the right-hand one by hypothesis, the left-hand one as part of the conclusion. Equivalently: a convergent sequence is (C,1)(C,1)-summable, to its own limit. The notation is licensed by uniqueness of limits of real sequences (A sequence has at most one limit).

The converse is false (FALSE: if the Cesaro means of a sequence converge then the sequence converges).

Facts & Assumptions

Given: A sequence (xk)(x_k) of reals converging to L:=limkxkL := \lim_k x_k, and its Cesaro means σn=(n+1)1k=0nxk\sigma_n = (n+1)^{-1}\sum_{k=0}^{n} x_k.

[L1]

The Cesaro means and (C,1)(C,1)-summability (The Cesaro means σn=(x0++xn)/(n+1)\sigma_n = (x_0 + \dots + x_n)/(n+1) and (C,1)(C,1)-summability); (n+1)1R>0(n+1)\cdot 1_{\mathbb{R}} > 0 for every nNn \in \mathbb{N} (Canonical naturals are positive and strictly increasing).

[L2]

Finite sums (Finite sums and finite products, by recursion) and their laws: additivity, scaling with k<dλ=dλ\sum_{k<d}\lambda = d\lambda, splitting k<n=k<m+k=mn1\sum_{k<n} = \sum_{k<m} + \sum_{k=m}^{n-1} for mnm \le n, and monotonicity of \sum in its terms (Laws of finite sums and finite products).

[L3]

Triangle inequality for finite sums: k<nakk<nak\big|\sum_{k<n} a_k\big| \le \sum_{k<n}|a_k| (Triangle inequality for finite sums).

[L4]

Convergence: for every rational ε>0\varepsilon > 0 there is KK with xkL<ε|x_k - L| < \varepsilon for all kKk \ge K, and equivalently for every real ε>0\varepsilon > 0, since below every positive real lies a positive rational (Limits and Cauchy sequences of reals, Sequences of reals: bounded, eventually, frequently, tails, subsequences, The rationals embed densely in the reals).

[L5]

Reciprocal Archimedean property: for every real ε>0\varepsilon > 0 there is a natural m1m \ge 1 with 1/m<ε1/m < \varepsilon (For every ε>0\varepsilon > 0 in a complete ordered field there is a natural n1n \ge 1 with 1/n<ε1/n < \varepsilon).

[L6]

Order arithmetic: a>0a > 0 gives a1>0a^{-1} > 0 and 0<a<b0 < a < b gives 0<b1<a10 < b^{-1} < a^{-1} (Inverses of positives are positive, and reciprocation reverses order); for c>0c > 0, aba \le b gives acbcac \le bc (Sign rules for products and monotonicity of multiplication); adding a constant preserves the order and inequalities add (Order is preserved by adding a constant and by adding inequalities); the order is total and transitive (Complete ordered field (least-upper-bound property), Ordered field); u0|u| \ge 0 (Basic properties of the absolute value); and d1Re1Rd \cdot 1_{\mathbb{R}} \le e \cdot 1_{\mathbb{R}} whenever ded \le e in N\mathbb{N} (Canonical naturals are positive and strictly increasing). In each clause above, Sign rules for products and monotonicity of multiplication and Order is preserved by adding a constant and by adding inequalities state the STRICT forms and only those; the nonstrict forms used below are those together with the equality cases, which trichotomy settles, the order being total (Ordered field).

[L7]

The order on N\mathbb{N} is total, so two indices have a larger one (\le is a linear order on N\mathbb{N}).

Proof

technique · direct
1.1

Let ε>0\varepsilon > 0 be an arbitrary real; choose KNK \in \mathbb{N} with xkL<ε/2|x_k - L| < \varepsilon/2 for every kKk \ge K.

L4L6choose
1.2

Put C:=k<KxkLC := \sum_{k<K} |x_k - L|, a real with C0C \ge 0 because every summand is 0\ge 0.

L2L6
2.1

For every nKn \ge K: since k=0nL=(n+1)L\sum_{k=0}^{n} L = (n+1)L, one has σnL=(n+1)1k=0n(xkL)\sigma_n - L = (n+1)^{-1}\sum_{k=0}^{n}(x_k - L), hence σnL(n+1)1k=0nxkL=(n+1)1(C+k=KnxkL)(n+1)1(C+(n+1K)(ε/2))C(n+1)1+ε/2|\sigma_n - L| \le (n+1)^{-1}\sum_{k=0}^{n}|x_k - L| = (n+1)^{-1}\big(C + \sum_{k=K}^{n}|x_k - L|\big) \le (n+1)^{-1}\big(C + (n+1-K)(\varepsilon/2)\big) \le C(n+1)^{-1} + \varepsilon/2, the last two steps using xkL<ε/2|x_k - L| < \varepsilon/2 for KknK \le k \le n and n+1Kn+1n+1-K \le n+1.

step 1.1step 1.2L1L2L3L6
2.2

Since ε(2(C+1))1>0\varepsilon\,(2(C+1))^{-1} > 0, choose a natural m1m \ge 1 with 1/m<ε(2(C+1))11/m < \varepsilon\,(2(C+1))^{-1}; then for every nmn \ge m one has n+1>mn + 1 > m, so C(n+1)1C/m(C+1)/m<ε/2C(n+1)^{-1} \le C/m \le (C+1)/m < \varepsilon/2.

step 1.2L5L6choose
3.1

Let NN be the larger of KK and mm; for every nNn \ge N both estimates apply and σnL<ε/2+ε/2=ε|\sigma_n - L| < \varepsilon/2 + \varepsilon/2 = \varepsilon.

step 2.1step 2.2L6L7
4.1

As ε>0\varepsilon > 0 was arbitrary, (σn)(\sigma_n) converges to LL, so limnσn\lim_n \sigma_n exists and equals L=limkxkL = \lim_k x_k.

step 3.1L1L4

Remarks

Depends on

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