# Finding the Roots of Equations

## The Bisection Method

The simplest way to solve an algebraic equation of the form g(z) = 0, for some function g is known as bisection. The method assumes that we start with two values of z that bracket a root: z1 (to the left) and z2 (to the right), say. Then, we iteratively narrow the range as follows. First, define zm to be the midpoint of the range: zm = (z1 + z2)/2. Then, if g(zm) has the same sign as g(z1), and assuming that there is only one root in the range, then we know that the root must lie in [zm,z2], and we can replace z1 by zm. If not, then the root lies in [z1,zm], so zm can replace z2.

A C fragment to implement this logic is:

```	if (g(zm)*g(z1) > 0)
z1 = zm;
else
z2 = zm;
```

The process continues until |z2-z1| becomes less than some specified tolerance. Obviously, the uncertainty in the value of the root (the width of the range) decreases by a factor of two with each iteration, that is, we gain approximately 1 decimal digit of accuracy every 3 iterations.

## The False-Position and Secant Methods

The bisection method relies solely on the assumption that the function g is continuous, so its value at the midpoint (eventually) lies between its values at the end of the range. If g is differentiable, we can do better. In that case, a straight-line approximation to g in the range [z1,z2] is appropriate, and we can estimate the root by linear interpolation (or extrapolation). After each iteration, the range is refined to incorporate the improved information.

In C, we can write: zs = z1 + (z2 - z1) * (-g(z1)) / (g(z2) - g(z1));

Both the false-position and the secant methods use this approach. The difference between the two is simply what you so with the information once you have it. In the method of false position (sometimes called regula falsi), we refine our range so that [z1,z2] always spans the root, as with bisection. That is, we replace z1 or z2 by zs depending on the sign of g(zs):

```	/* False position iteration. */

zs = z1 + (z2 - z1) * (-g(z1)) / (g(z2) - g(z1));

if (g(zs)*g(z1) > 0)
z1 = zs;
else
z2 = zs;
```

In the secant method, we always use zs and the previous estimate of the root (z2 say):

```	/* Secant iteration. */

zs = z1 + (z2 - z1) * (-g(z1)) / (g(z2) - g(z1));

z1 = z2;
z2 = zs;
```
Note that, with false position, we are guaranteed that our range always spans the root, and convergence is assured, although the method is generally slower than the secant method. Specifically, in the configuration shown above, the iterates would approach the solution from the right, leaving z1 unchanged and slowing the convergence. As illustrated below, with the secant scheme, the root may not remain bracketed, and there is always the possiblilty of an instability causing the method to fail. (This is usually not a problem if we are close to the root and g is almost linear in [z1,z2], but it is a real possibility if the function has several ``wiggles'' in the range.)

Generally speaking, these methods work best when we are already close to the solution. One can construct situations where the secant method performs far worse than bisection but, as a rule of thumb, it can be shown that, once we are close to a root, the secant method more than doubles the number of digits of accuracy of the answer every two iterations. False position, while slower, still converges substantially faster than bisection.

## The Newton-Raphson Method

The most efficient method for finding a root of an equation is known as Newton-Raphson. In this method, instead of doing linear interpolation between two points known to straddle the root, as in the secant method, we use the value of the function g and its derivative g' at some point z, and simply follow the tangent to the point where it crosses the axis.

Obviously, zn = z - g(z)/g'(z). In this example, you can see that the next iteration (starting at zn) will already bring us very close to the root.

When it can be used, Newton-Raphson converges extremely rapidly. In fact, the number of digits of accuracy doubles at each iteration. However, the method requires that we know g', which may not always be the case (for example, if g is known only as a tabulated or numerically defined function). Note, though, that it is possible to make numerical estimates of g' by evaluating g at two nearby points. The method also suffers from the fact that it can be wildly unstable if we start far from the root (for example, if we started at the point labeled z2 in the earlier figures, Newton Raphson would send us off in entirely the wrong direction). And of course, if happen to land near a turning point, where g'(z) = 0, the method will fail badly.

Like the secant method, Newton-Raphson is often used to refine our results after bisection has already brought us fairly close to the desired solution.