Introductory university-level calculus, linear algebra, abstract algebra, probability, statistics, and stochastic processes.
Limit of a univariate function
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Let \(f(x)\) be a function. Suppose we move \(x\in\mathbb{R}\) towards \(a\) while keeping \(x \neq a\). If in this case \(f(x)\) approaches a constant value \(\alpha\) irrespective of the way how \(x\) approaches \(a\), we say that \(f(x)\) converges to \(\alpha\) as \(x \to a\) and write \[\lim_{x\to a}f(x) = \alpha\] or \[f(x) \to \alpha \text{ as \(x \to a\)}.\]
Remark. \(a\) needs not belong to \(\text{dom}(f)\) (the domain of \(f\)) as long as \(x\) can approach \(a\) arbitrarily closely. □
But what does this mean exactly? Here's a rigorous definition in terms of what is called the \(\varepsilon-\delta\) argument.
Definition (Limit of a function)
We say that the function \(f(x)\) converges to \(\alpha\) as \(x \to a\) and write
\[\lim_{x\to a}f(x) = \alpha\]
if the following condition is satisfied.
For any \(\varepsilon > 0\), there exists \(\delta > 0\) such that, for all \(x\in \text{dom}(f)\), if \(0 < |x - a| < \delta\) then \(|f(x) - \alpha| < \varepsilon\).
Remark. We are implicitly assuming \(\varepsilon, \delta \in\mathbb{R}\). □
Here's how this definition works. Suppose \(f(x) \to \alpha\) as \(x \to a\). Let us pick any positive real number \(\varepsilon\). However small this \(\varepsilon\) may be, if \(x\) is sufficiently close to \(a\), we can always have \(|f(x) - \alpha| < \varepsilon\). Here ``sufficiently close'' means that we can pick some sufficiently small positive real number \(\delta\) such that \(|x - a| < \delta\) implies \(|f(x) - \alpha| < \varepsilon\). In other words, we move \(x\) closer and closer to \(a\) until \(|f(x) - \alpha| < \varepsilon\) holds. Conversely, if this operation is possible for any \(\varepsilon\), it makes sense to say that \(f(x)\) converges to \(\alpha\) as \(x \to a\).
Example. Consider \(f(x) = x^2 + 1\). We have
\[\lim_{x \to 1}f(x) = 2.\]
Let \(\varepsilon = 0.1\). Let us find \(\delta > 0\) such that \(0 < |x - 1| < \delta\) implies \(|f(x) - 2| < \varepsilon\). Suppose
Since \(\sqrt{0.9} - 1 = -0.0513\cdots\) and \(\sqrt{1.1} - 1 = 0.0488\cdots\), if
\[|x - 1| < \sqrt{1.1} - 1,\]
then we have
\[|f(x) - 2| < 0.1 = \varepsilon.\]
So \(\delta\) can be any positive number less than \(\sqrt{1.1} - 1\). For example, let \(\delta = 0.04\). Then \(|x - 1| < 0.04\) implies \(0.96 < x < 1.04\), which implies \(-0.0784 < x^2 - 1 < 0.0816\) so
We can use multiple integrals to compute areas and volumes of various shapes. Area of a planar region Definition (Area) Let \(D\) be a bounded closed region in \(\mathbb{R}^2\). \(D\) is said to have an area if the multiple integral of the constant function 1 over \(D\), \(\iint_Ddxdy\), exists. Its value is denoted by \(\mu(D)\): \[\mu(D) = \iint_Ddxdy.\] Example . Let us calculate the area of the disk \(D = \{(x,y)\mid x^2 + y^2 \leq a^2\}\). Using the polar coordinates, \(x = r\cos\theta, y = r\sin\theta\), \(dxdy = rdrd\theta\), and the ranges of \(r\) and \(\theta\) are \([0, a]\) and \([0, 2\pi]\), respectively. Thus, \[\begin{eqnarray*} \mu(D) &=& \iint_Ddxdy\\ &=&\int_0^a\left(\int_0^{2\pi}rd\theta\right)dr\\ &=&2\pi\int_0^a rdr\\ &=&2\pi\left[\frac{r^2}{2}\right]_0^a = \pi a^2. \end{eqnarray*}\] □ Volume of a solid figure Definition (Volume) Let \(V\) be a solid figure in the \((x,y,z)\) space \(\mathbb{R}^3\). \(V\) is...
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}...
Consider integrating a function \(f(x,y)\) over a region \(D\) which may not be bounded or closed. In the case of a univariate function, this corresponds to the improper integral where we took the limits of the endpoints of a closed interval. In the case of multiple integrals, we adopt the notion of a "sequence of regions." Consider a sequence of regions \(\{K_n\}\) where each \(K_n\) is a subset of \(\mathbb{R}^2\) that satisfies the following conditions: (a) \(K_1 \subset K_2\)\(\subset \cdots \subset\) \(K_n \subset K_{n+1} \subset \cdots\). (b) For all \(n\in \mathbb{N}\), \(K_n \subset D\). (c) For all \(n \in\mathbb{N}\), \(K_n\) is bounded and closed. (d) For any bounded closed set \(F\) that is included in \(D\) (i.e., \(F \subset D\)), if \(n\) is sufficiently large, then \(F \subset K_n\). In other words: for all bounded closed \(F \subset D\), there exists some \(N\in \mathbb{N}\) such that, for all \(n\in \mathbb{N}\), if \(n \geq N\) then \(F \subset K_...
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