Function minimization in N dimensions

(1) Downhill simplex, a.k.a. amoeba (NR §10.4)
	• O(N^2) storage
	• robust, simple
	• Lecture/handout already prepared
(2) Conjugate gradient (NR §10.6)
	• Uses Brent's method (NR §10.3) in 1-D
	• O(N) storage ==> large problems
	• fast/efficient
	• requires derivatives (==> smoothness assumed)
(3) Simulated annealing (NR §10.9)
	• most robust and simplest of algorithms!
	• often finds global minima, even of rough functions
	• O(N) storage ==> large problems
	• slow as molasses in January
	• independent variables need not be continuous!