Pure mathematicians often encounter problems that cannot be solved using established rules and logic. In these cases, they turn to numerical methods, which provide an approximation or limit the solution.
Numerical methods have various applications in mathematics, such as solving differential equations, linear systems, and finding derivatives. At the A-level, the focus is on root finding and calculating the area under curves.
Some functions cannot be integrated, meaning there is no antiderivative available. However, this does not prevent us from estimating the area under these functions. We can achieve this by dividing the area into smaller, similar shapes, calculating the area of each shape, and summing them up for an approximation.
The more trapeziums we add, the more accurate our approximation becomes. For example, let's find an approximation for a definite integral using the trapezium rule with four strips of equal width.
To achieve this, we need five points: 0, 0.5, 1, 1.5, and 2. The following table shows the corresponding values of x and f(x) for each point:
Using the given formula, a(b-a)/n, we get an approximation of the integral as 1.8. If we were to evaluate this integral using other methods, we would get a value close to 5.5, proving that the trapezoidal method is an effective technique for approximation.
Not all equations can be solved using algebraic methods. That's where numerical methods come in. However, not all methods work for all equations, and it's crucial to choose the appropriate method.
Suppose we have a function and suspect that a root lies between two points, a and b. If there's a single root, the sign of f(a) will differ from that of f(b). However, if the interval is too large, there could be multiple roots, making it challenging to determine the sign using just two points.
By plotting the graph of the function, we can visually locate the points where the sign changes, indicating a root. For instance, by observing the graph, we can determine that there's a root of f between -1.5 and -1.4 because f(-1.5) is negative, and f(-1.4) is positive.
Iterative methods involve repeating a process to reach a solution. For example, to find a root of cos(x) - x, we can rearrange the equation as x = cos(x). Using the iteration with x0 = 0.5, we can find an approximation of the root to two decimal places. Continuing this iteration, we can arrive at the exact value of the root, which in this case is -1.
The Newton-Raphson method is another iterative method used for finding roots. Given the first approximation as x0 = 0.5, we can use this method to obtain a more precise approximation for a root of cos(x) - x. First, we find f'(x). Thus, f'(x) = -sin(x) - 1. The Newton-Raphson formula is then given by xn+1 = xn - (cos(xn) - xn)/(-sin(xn) - 1)
A numerical method simplifies complex math problems by providing an approximate solution.
An analytical method uses precise techniques to obtain an exact solution, while a numerical method relies on approximations to reach an approximate answer.
In numerical methods, convergence occurs when an iterative process reaches a stable value.
Numerical methods are used when other methods are not practical or feasible.
