Two measures of the efficiency of an algorithm are the number of floating point operations (flops) performed and the elapsed time.

The MATLAB function `flops` keeps a running total of the flops
performed. The command `flops(0)` (not `flops = 0`!) will
reset flops to 0. Hence, entering `flops(0)` immediately before
executing an algorithm and `flops` immediately after gives the
flop count for the algorithm.

The MATLAB function `clock` gives the current time accurate to a hundreth
of a second (see `help clock`). Given two such times `t1` and
`t2`, `etime(t2,t1)` gives the elapsed time from `t1` to `t2`.
One can, for example, measure the time required to solve a given linear system
`Ax=b` using Gaussian elimination as follows:

You may wish to compare this time-and flop count-with that for solving the system usingt = clock; x = A \ b; time = etime(clock,t)

It should be noted that, on timesharing machines, `etime` may not
be a reliable measure of the efficiency of an algorithm since the rate
of execution depends on how busy the computer is at the time.

Wed Mar 13 19:15:55 MET 1996