Introductory university-level calculus, linear algebra, abstract algebra, probability, statistics, and stochastic processes.
Applications of integrals (1): Length of a curve
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As an application of integrals, we consider the length of a curve. Specifically, we consider curves in the 2-dimensional space defined parametrically.
Let \(x(t)\) and \(y(t)\) be \(C^1\) functions defined on an interval containing the closed interval \([a,b]\). If \(t\) moves in \([a,b]\), the point \((x(t), y(t))\) on \(\mathbb{R}^2\) moves smoothly, drawing a curve. Let us denote this curve by \(C\). Let \(P = (x(a), y(a))\) and \(Q = (x(b), y(b))\) be the end points of the curve \(C\). We want to measure the ``length'' of the curve \(C\). But what is the length of a \emph{curve}, anyway? We do know how to calculate the length of a line segment (Pythagorean theorem). So, let us approximate the curve by line segments. Consider the partition of the closed interval \([a,b]\):
Then \(P_0 = (x(t_0), y(t_0)) = P\), \(P_1 = (x(t_1), y(t_1))\), \(P_2 = (x(t_2), y(t_2))\), \(\cdots\), \(P_{n-1} = (x(t_{n-1}), y(t_{n-1}))\), \(P_n = (x(t_n), y(t_n)) = Q\) are all on \(C\). We can approximate the curve \(C\) by connecting the line segments \(P_0P_1\), \(P_1P_2\), \(\cdots\), \(P_{n-1}P_{n}\). By adding the lengths of these segments, we can approximate the length \(l_{\Delta}\) of the curve \(C\). The length of \(P_{i}P_{i+1}\) is given by
Since \(y(t)\) is a \(C^1\) function, \(\frac{d}{dt}y(t)\) is continuous so the difference between \(\frac{d}{dt}y(s_i')\) and \(\frac{d}{dt}y(s_i)\) becomes smaller as \(\Delta\) is more refined. Thus, we have the approximation
As we refine \(\Delta\), both sides of {eq:mMlen} converge to the same value that is \(\int_{a}^{b}h(t)dt\). Thus, it is natural to define the length \(l(C)\) of the curve \(C\) by
Open sets In \(\mathbb{R}\), we have the notion of an open interval such as \((a, b) = \{x \in \mathbb{R} | a < x < b\}\). We want to extend this idea to apply to \(\mathbb{R}^n\). We also introduce the notions of bounded sets and closed sets in \(\mathbb{R}^n\). Recall that the \(\varepsilon\)-neighbor of a point \(x\in\mathbb{R}^n\) is defined as \(N_{\varepsilon}(x) = \{y \in \mathbb{R}^n | d(x, y) < \varepsilon \}\) where \(d(x,y)\) is the distance between \(x\) and \(y\). Definition (Open set) A subset \(U\) of \(\mathbb{R}^n\) is said to be an open set if the following holds: \[\forall x \in U ~ \exists \delta > 0 ~ (N_{\delta}(x) \subset U).\tag{Eq:OpenSet}\] That is, for every point in an open set \(U\), we can always find an open ball centered at that point, that is included in \(U\). See the following figure. Perhaps, it is instructive to see what is not an open set. Negating (Eq:OpenSet), we have \[\exists x \in U ~ \forall \delta > 0 ~ (N_{\delta}(x) \not
We would like to study multivariate functions (i.e., functions of many variables), continuous multivariate functions in particular. To define continuity, we need a measure of "closeness" between points. One measure of closeness is the Euclidean distance. The set \(\mathbb{R}^n\) (with \(n \in \mathbb{N}\)) with the Euclidean distance function is called a Euclidean space. This is the space where our functions of interest live. The real line is a geometric representation of \(\mathbb{R}\), the set of all real numbers. That is, each \(a \in \mathbb{R}\) is represented as the point \(a\) on the real line. The coordinate plane , or the \(x\)-\(y\) plane , is a geometric representation of \(\mathbb{R}^2\), the set of all pairs of real numbers. Each pair of real numbers \((a, b)\) is visualized as the point \((a, b)\) in the plane. Remark . Recall that \(\mathbb{R}^2 = \mathbb{R}\times\mathbb{R} = \{(x, y) | x, y \in \mathbb{R}\}\) is the Cartesian product of \(\mathbb{R}\) with i
Newton's method (the Newton-Raphson method) is a very powerful numerical method for solving nonlinear equations. Suppose we'd like to solve a non-linear equation \(f(x) = 0\), where \(f(x)\) is a (twice) differentiable non-linear function. Newton's method generates a sequence of numbers \(c_1, c_2, c_3, \cdots\) that converges to a solution of the equation. That is, if \(\alpha\) is a solution (i.e., \(f(\alpha) = 0\)) then, \[\lim_{n\to\infty}c_n = \alpha,\] and this sequence \(\{c_n\}\) is generated by a series of linear approximations of the function \(f(x)\). Theorem (Newton's method) Let \(f(x)\) be a function that is twice differentiable on an open interval \(I\) that contains the closed interval \([a, b]\) (i.e., \([a,b]\subset I\)) and satisfy the following conditions: \(f(a) < 0\) and \(f(b) > 0\); For all \(x\in [a, b]\), \(f'(x) > 0\) and \(f''(x) > 0\). Let us define the sequence \(\{c_n\}\) by \[ \begin{eqnarray} c_1 &
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