Introductory university-level calculus, linear algebra, abstract algebra, probability, statistics, and stochastic processes.
Local maximum and local minimum
Get link
Facebook
X
Pinterest
Email
Other Apps
-
Derivatives can be used to find a function's local extremal values (i.e., local maximum or minimum values).
If \(f(x)\) is a continuous function defined on a closed interval \([a,b]\), then it always has a maximum value and a minimum value (c.f. the Extreme Value Theorem). Finding a maximum or minimum of a function on its entire domain is a global problem.
On the other hand, examining continuity or differentiability around \(x=a\) is a local problem in the sense that it only concerns the neighbor of the point \(x = a\). We may also define the notions of local maxima and local minima.
Definition (Local maximum and local minimum)
Let \(f(x)\) be a function defined on an interval \(I\) and let \(a \in I\).
\(f(a)\) is said to be a local maximum value of the function \(f(x)\) if there exists \(\delta > 0\) such that, for all \(x\in (a - \delta, a + \delta) \cap I\), \(x \neq a\) implies \(f(x) < f(a)\). \(x= a\) is said to be a local maximum point if \(f(a)\) is a local maximum value.
\(f(a)\) is said to be a local minimum value of the function \(f(x)\) if there exists \(\delta > 0\) such that, for all \(x\in (a - \delta, a + \delta) \cap I\), \(x \neq a\) implies \(f(x) > f(a)\). \(x= a\) is said to be a local minimum point if \(f(a)\) is a local minimum value.
We use the term extreme value to mean either a local maximum value or a local minimum value.
Remark. If a local maximum value is actually a maximum value, then the maximum value is called a global maximum value. A global minimum value is similarly defined. Note that a global maximum (minimum) value is a local maximum (minimum) value, but not vice versa. □
Example. The constant function \(f(x) = c\) (\(c\) is a constant) has the maximum value \(c\) and the minimum value \(c\), but it does not have a local maximum value or local minimum value. □
Example. Let \(f(x) = (x^2 - 1)(x^2 - 4)\) (draw the graph!). \(f(0) = 4\) is a local maximum value. In fact, take \(\delta = \sqrt{5} > 0\), then for all \(x\) such that \(0 < |x| < \delta\), we have
\[0 < x^2 < \delta^2\]
(i.e., \(x^2\) is strictly positive) and
\[-5 < x^2 - 5 < \delta^2 - 5 = 0\]
(i.e., \(x^2 - 5\) is strictly negative) so that
\[f(x) = x^2(x^2 - 5) + 4 < 4 = f(0).\]
This means that \(f(0) > f(x)\) for all \(x \in (-\delta, +\delta), x \neq 0\). That is, \(f(0)\) is a local maximum value (check the above definition again!). □
Definition (Stationary/critical point)
Let \(f(x)\) be a differentiable function. \(x=a\) is said to be a critical point or stationary point if \(f'(a) = 0\). In this case, \(f(a)\) is called a critical value or stationary value.
Theorem
If a differentiable function \(f(x)\) has a local extremum value (i.e., either a local maximum or a local minimum value) at \(x = a\), then \(x = a\) is a stationary point.
Proof. We prove only for the local maximum case (the local minimum case is similar).
Suppose \(f(x)\) takes a local maximum value at \(x=a\). Then there exists \(\delta > 0\) such that, for all \(x\in (a - \delta, a + \delta)\), \(f(x) \leq f(a)\). Since \(f(x)\) is differentiable at \(x = a\) (by assumption), we have
If \(0 < a - x < \delta\) (i.e., \(x \in (a - \delta, a)\)), then \(\frac{f(x) - f(a)}{x - a} \geq 0\) so, by taking the limit as \(x \to a-0\), we have \(f'(a)\geq 0\).
If \(0 < x - a < \delta\) (i.e., \(x\in (a, a+\delta)\)), then \(\frac{f(x) - f(a)}{x - a} \leq 0\) so, by taking the limit as \(x \to a+0\), we have \(f'(a) \leq 0\).
Therefore, we have \(f'(a) = 0\). ■
Remark. The converse of this theorem is not true. That is, \(f'(a) = 0\) does not imply \(f(a)\) is an extremum. For example, \(f(x) = x^3\) has \(f'(0) = 0\), but \(f(0)\) is neither a local maximum value nor a local minimum value (draw the graph!). □
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}...
Generational growth Consider the following scenario (see the figure below): A single individual (cell, organism, etc.) produces \(j (= 0, 1, 2, \cdots)\) descendants with probability \(p_j\), independently of other individuals. The probability of this reproduction, \(\{p_j\}\), is known. That individual produces no further descendants after the first (if any) reproduction. These descendants each produce further descendants at the next subsequent time with the same probabilities. This process carries on, creating successive generations. Figure 1. An example of the branching process. Let \(X_n\) be the random variable representing the population size (number of individuals) of generation \(n\). In the above figure, we have \(X_0 = 1\), \(X_1=4\), \(X_2 = 7\), \(X_3=12\), \(X_4 = 9.\) We shall assume \(X_0 = 1\) as the initial condition. Ideally, our goal would be to find how the population size grows through generations, that is, to find the probability \(\Pr(X_n = k)\) for e...
The birth-death process Combining birth and death processes with birth and death rates \(\lambda\) and \(\mu\), respectively, we expect to have the following differential-difference equations for the birth-death process : \[\begin{eqnarray}\frac{{d}p_0(t)}{{d}t} &=& \mu p_1(t),\\\frac{{d}p_n(t)}{{d}t} &=& \lambda(n-1)p_{n-1}(t) - (\lambda + \mu)np_n(t) + \mu(n+1)p_{n+1}(t),~~(n \geq 1).\end{eqnarray}\] You should derive the above equations based on the following assumptions: Given a population with \(n\) individuals, the probability that an individual is born in the population during a short period \(\delta t\) is \(\lambda n \delta t + o(\delta t)\). Given a population with \(n\) individuals, the probability that an individual dies in the population is \(\mu n \delta t + o(\delta t)\). The probability that multiple individuals are born or die during \(\delta t\) is negligible. (The probability of one birth and one death during \(\delta t\) is also negligible.) Consequ...
Comments
Post a Comment