Introductory university-level calculus, linear algebra, abstract algebra, probability, statistics, and stochastic processes.
Monotone sequences and Cauchy sequences
Get link
Facebook
X
Pinterest
Email
Other Apps
-
Deciding whether a given sequence \(\{a_n\}\) converges or diverges is usually very difficult. Sometimes it is possible to decide if a sequence converges without knowing its limit explicitly if certain conditions are met.
This sequence is monotone decreasing but it cannot be arbitrarily small because it is also bounded below. Therefore it must converge to some number (namely \(\sqrt{2}\) in this case). That such a real number exists is guaranteed by the continuity of real numbers. □
The observation in the above example can be generalized.
Theorem (Monotone convergence theorem)
Any bounded monotone sequence converges.
Proof. Suppose \(\{a_n\}\) is a bounded monotone increasing sequence. Then the set \(S = \{a_n\mid n\in\mathbb{N}\}\) is bounded above so that its supremum \(\alpha\) exists. For any \(\varepsilon > 0\), \(\alpha - \varepsilon\) is not an upper bound of \(S\) so there exists \(N\in\mathbb{N}\) such that \(\alpha - \varepsilon < a_N\). Since \(\{a_n\}\) is monotone increasing, for all \(n\geq N\), \(a_n \geq a_N > \alpha - \varepsilon\). Since \(\alpha\) is the supremum of \(\{a_n\}\), we have \(a_n \leq \alpha < \alpha + \varepsilon\). Thus we have \(\alpha -\varepsilon < a_n < \alpha + \varepsilon\) or \(|a_n -\alpha| < \varepsilon\) for all \(n\geq N\). Hence \(\lim_{n\to\infty}a_n = \alpha\).
We can prove similarly for the case of a bounded monotone decreasing sequence. ■
Definition (Cauchy sequence)
The sequence \(\{a_n\}\) is said to be a Cauchy sequence if and only if the following condition is met.
For any \(\varepsilon > 0\), there exists \(N\in\mathbb{N}\) such that for any \(k, l\geq N\), \(|a_k - a_l| < \varepsilon\).
Or, in a logical form,
\[\forall \varepsilon > 0, \exists N\in\mathbb{N}, \forall k,l\in\mathbb{N} ~ (k, l \geq N \implies |a_k - a_l| < \varepsilon).\]
Theorem
Any convergent sequence is a Cauchy sequence.
Proof. Suppose \(\lim_{n\to\infty}a_n = \alpha\). For any \(\varepsilon > 0\), there exists \(N\in\mathbb{N}\) such that for all \(k,l \geq N\),
\(2\varepsilon\) is an arbitrary positive real number. Therefore, \(\{a_n\}\) is a Cauchy sequence. ■
The converse is also true, but the proof is beyond the scope of this lecture.
Theorem
Any Cauchy sequence converges.
Corollary
A sequence converges if and only if it is a Cauchy sequence.
For later convenience, we provide the following theorem without proof.
Theorem (Bolzano-Weierstrass Theorem)
Let \(\{a_n\}\) be a sequence such that \(a_n \in [c, d]\) for all \(n\in\mathbb{N}\). Then there exists a subsequence \(\{a_{n_k}\}\) of \(\{a_n\}\) that converges to a value in the closed interval \([c,d]\).
Proof. Exercise. (Hint: use the squeeze theorem) ■
Defining the birth process Consider a colony of bacteria that never dies. We study the following process known as the birth process , also known as the Yule process . The colony starts with \(n_0\) cells at time \(t = 0\). Assume that the probability that any individual cell divides in the time interval \((t, t + \delta t)\) is proportional to \(\delta t\) for small \(\delta t\). Further assume that each cell division is independent of others. Let \(\lambda\) be the birth rate. The probability of a cell division for a population of \(n\) cells during \(\delta t\) is \(\lambda n \delta t\). We assume that the probability that two or more births take place in the time interval \(\delta t\) is \(o(\delta t)\). That is, it can be ignored. Consequently, the probability that no cell divides during \(\delta t\) is \(1 - \lambda n \delta t - o(\delta t)\). Note that this process is an example of the Markov chain with states \({n_0}, {n_0 + 1}, {n_0 + 2}...
In mathematics, we must prove (almost) everything and the proofs must be done logically and rigorously. Therefore, we need some understanding of basic logic. Here, I will informally explain some rudimentary formal logic. Definitions (Proposition): A proposition is a statement that is either true or false. "True" and "false" are called the truth values, and are often denoted \(\top\) and \(\bot\). Here is an example. "Dr. Akira teaches at UBD." is a statement that is either true or false (we understand the existence of Dr. Akira and UBD), hence a proposition. The following statement is also a proposition, although we don't know if it's true or false (yet): Any even number greater than or equal to 4 is equal to a sum of two primes. See also: Goldbach's conjecture Next, we define several operations on propositions. Note that propositions combined with these operations are again propositions. (Conjunction, logical "and"): Let \(P\)...
Defining sets Set theory is the foundation of modern mathematics. Every mathematical notion is built on some set. What is a set? This is a very deep question beyond the scope of this lecture. Instead, we give the following very rough, informal definition. Definition (very informal) A set is a collection of distinct objects. Objects of a set are called elements of the set. To denote a set, we can enumerate its elements, enclosed by curly brackets. For example, \[\{1, 2, 3\}\] denotes a set consisting of three elements that are 1, 2, and 3. We may give a set a name as in \[S = \{1, 2, 3\},\] and say, for example, "the set \(S\) contains the elements 1, 2, and 3. Suppose \(S\) is any set. To denote that an object \(x\) is an element of \(S\), we write \[x \in S\] and say "\(x\) is in \(S\)" and so on. To denote that \(x\) is not in \(S\), we write \[x \not\in S.\] It is important that the elements of a set are distinct. For example, \[\{a, a, b, c\}\] is not a set beca...
Comments
Post a Comment