The principal properties of optimization methods considered here are the `rate of convergence', `radius of convergence', `CPU time' and `conservativity'. The rate of convergence is the number of iterations of the method required to reach an optimum solution. The radius of convergence is a measure of the accuracy required of the starting model. The CPU time represents the amount of time required to reach the optimum. The conservativity is a measure of the tendency of a method of optimization to preserve the values of parameters when changes would not affect the fit of the model to the data. The locations of several optimization methods on these continuums are indicated by the placement of their names. The search method uses no derivatives and is located furthest to the left. The simulated-annealing method occupies a range of positions, which is controlled by the temperature of the slow-cooling protocol. Steepest descent (sd) uses only first derivatives, while the conjugate-gradient (cg), preconditioned conjugate-gradient (pcg) and full-matrix methods use progressively more second derivatives.