(a) Consider a sequence x n x_{n} x n that converges to x x x and another sequence
y n = x 1 + x 2 + ⋯ + x n n . y_{n} = \frac{x_1 + x_2 + \dots + x_{n}}{n}. y n = n x 1 + x 2 + ⋯ + x n . By definition, for any ϵ > 0 \epsilon > 0 ϵ > 0 ,
∃ N 1 ∈ N , ∀ n ∈ N , n ≥ N 1 ⟹ ∣ x n − x ∣ < ϵ . \exists N_1 \in \mathbb{N}, \forall n \in \mathbb{N}, \quad n \geq N_1 \implies
\abs{x_{n} - x} < \epsilon. ∃ N 1 ∈ N , ∀ n ∈ N , n ≥ N 1 ⟹ ∣ x n − x ∣ < ϵ . Let's use the N 1 N_1 N 1 from that definition, for any given ϵ \epsilon ϵ . Then, consider the absolute difference
∣ y n − x ∣ = ∣ x 1 + x 2 + ⋯ + x n n − x ∣ = ∣ x 1 + x 2 + ⋯ + x n n − x n n ∣ = ∣ ( x 1 − x ) + ( x 2 − x ) + ⋯ + ( x n − x ) n ∣ = ∣ 1 n ∑ i = 1 N 1 − 1 ( x i − x ) + 1 n ∑ i = N 1 n ( x i − x ) ∣ \begin{aligned}
\abs{y_{n} - x}
&= \abs{\frac{x_1 + x_2 + \dots + x_{n}}{n} - x} \\
&= \abs{\frac{x_1 + x_2 + \dots + x_{n}}{n} -
\frac{xn}{n}} \\
&= \abs{\frac{(x_1 - x) + (x_2 - x) + \dots + (x_{n} -
x)}{n}} \\
&= \abs{ \frac{1}{n}\sum^{N_1 - 1}_{i=1} (x_{i} - x) +
\frac{1}{n}\sum^{n}_{i=N_1} (x_{i} - x)}
\end{aligned} ∣ y n − x ∣ = n x 1 + x 2 + ⋯ + x n − x = n x 1 + x 2 + ⋯ + x n − n x n = n ( x 1 − x ) + ( x 2 − x ) + ⋯ + ( x n − x ) = n 1 i = 1 ∑ N 1 − 1 ( x i − x ) + n 1 i = N 1 ∑ n ( x i − x ) for n ≥ N 1 n \geq N_1 n ≥ N 1 . By separating the sum into two parts, we first apply the triangle inequality and then find bounds on both parts, hoping they can be strictly inferior to ϵ \epsilon ϵ (to satisfy the definition of convergence). Thus,
∣ 1 n ∑ i = 1 N 1 − 1 ( x i − x ) + 1 n ∑ i = N 1 n ( x i − x ) ∣ ≤ 1 n ∑ i = 1 N 1 − 1 ∣ x i − x ∣ + 1 n ∑ i = N 1 n ∣ x i − x ∣ \abs{ \frac{1}{n}\sum^{N_1 - 1}_{i=1} (x_{i} - x) +
\frac{1}{n}\sum^{n}_{i=N_1} (x_{i} - x)}
\leq \frac{1}{n} \sum^{N_1 - 1}_{i=1} \abs{x_{i} - x} +
\frac{1}{n} \sum^{n}_{i=N_1} \abs{x_{i} - x} n 1 i = 1 ∑ N 1 − 1 ( x i − x ) + n 1 i = N 1 ∑ n ( x i − x ) ≤ n 1 i = 1 ∑ N 1 − 1 ∣ x i − x ∣ + n 1 i = N 1 ∑ n ∣ x i − x ∣ Notice that the first part has a constant numerator but an increasing denominator, dependent only on n n n . Therefore, by the Archimedean property, we can find N 2 N_2 N 2 for which
1 n ∑ i = 1 N 1 − 1 ∣ x i − x ∣ < ϵ ( N 1 − 1 ) N 1 = ϵ N 1 N 1 − ϵ N 1 \frac{1}{n} \sum^{N_1 - 1}_{i=1} \abs{x_{i} - x} <
\frac{\epsilon (N_1 - 1)}{N_1}
= \frac{\epsilon N_1}{N_1} - \frac{\epsilon}{N_1} n 1 i = 1 ∑ N 1 − 1 ∣ x i − x ∣ < N 1 ϵ ( N 1 − 1 ) = N 1 ϵ N 1 − N 1 ϵ when n ≥ N 2 n \geq N_2 n ≥ N 2 . For the second part, notice that all the terms of the numerator are in V ϵ ( x ) V_{\epsilon}(x) V ϵ ( x ) . Therefore, their sum can be bounded above by ϵ \epsilon ϵ because the furthest x n x_{n} x n can be from x x x in that interval is less than ϵ \epsilon ϵ . Now, if we choose N = max { N 1 , N 2 } N = \max \{N_1, N_2\} N = max { N 1 , N 2 } , we obtain
∣ y n − x ∣ = ∣ 1 n ∑ i = 1 N 1 − 1 ( x i − x ) + 1 n ∑ i = N 1 n ( x i − x ) ∣ ≤ 1 n ∑ i = 1 N 1 − 1 ∣ x i − x ∣ + 1 n ∑ i = N 1 n ∣ x i − x ∣ < ϵ ( N 1 − 1 ) N 1 + 1 n ( n − N 1 + 1 ) ϵ = ϵ N 1 N 1 − ϵ N 1 + ϵ − ϵ N n + ϵ n = ( ϵ N 1 N 1 − ϵ N n ) ≤ ϵ \begin{aligned}
\abs{y_{n} - x}
&=\abs{ \frac{1}{n}\sum^{N_1 - 1}_{i=1} (x_{i} - x) +
\frac{1}{n}\sum^{n}_{i=N_1} (x_{i} - x)} \\
&\leq \frac{1}{n}
\sum^{N_1 - 1}_{i=1} \abs{x_{i} - x} + \frac{1}{n}
\sum^{n}_{i=N_1} \abs{x_{i} - x} \\
&< \frac{\epsilon (N_1 - 1)}{N_1} + \frac{1}{n} (n - N_1 +
1) \epsilon \\
&= \frac{\epsilon N_1}{N_1} - \frac{\epsilon}{N_1} +
\epsilon - \frac{\epsilon N}{n} + \frac{\epsilon}{n} \\
&= (\frac{\epsilon N_1}{N_1} -\frac{\epsilon N}{n}) \\
&\leq \epsilon
\end{aligned} ∣ y n − x ∣ = n 1 i = 1 ∑ N 1 − 1 ( x i − x ) + n 1 i = N 1 ∑ n ( x i − x ) ≤ n 1 i = 1 ∑ N 1 − 1 ∣ x i − x ∣ + n 1 i = N 1 ∑ n ∣ x i − x ∣ < N 1 ϵ ( N 1 − 1 ) + n 1 ( n − N 1 + 1 ) ϵ = N 1 ϵ N 1 − N 1 ϵ + ϵ − n ϵ N + n ϵ = ( N 1 ϵ N 1 − n ϵ N ) ≤ ϵ when n ≥ N n \geq N n ≥ N .
(b) Consider the sequence
x n = ( 0 , 1 , 0 , 1 , … ) . x_{n} = (0, 1, 0, 1, \dots). x n = ( 0 , 1 , 0 , 1 , … ) . Clearly, it does not converge. However, the sequence of its average
y n = ( 0 , 1 2 , 1 3 , 2 4 , 2 5 , 3 6 , … ) y_{n} = (0, \frac{1}{2}, \frac{1}{3}, \frac{2}{4},
\frac{2}{5}, \frac{3}{6}, \dots) y n = ( 0 , 2 1 , 3 1 , 4 2 , 5 2 , 6 3 , … ) does converge to 1/2.