Suppose we have an estimate a of the minimum of . We can refine this by choosing some direction vector u and searching for the minimum along the line . This is a 1-D minimization to find the value of which minimizes the function . a is then replaced with .

The simplest algorithm for locating the minima of is to repeat this
process, cycling through the **n** unit direction vectors which span the
parameter space. However, this can prove to be very inefficient if we
end up making many small steps, zig-zagging along a valley, when it would
be better to move in directions aligned with the valley (Figure 3).

**Figure 3:** Minimizing along each unit direction in turn can be inefficient.

Fri Mar 28 14:12:50 GMT 1997