ABSTRACT:
This chapter describes a variable metric minimizer, a program that finds the set of parameters to a function that will poduce the smallest output value from that function.
The program uses methods now accepted as both robust and efficient. That is, these methods can handle a wide variety of functions and data without failing, and they can find a minimum in a shorter period of time (fewer iterations) than other methods. They are called quasi-Newton positive definite secant update methods. The variable metric part comes from looking at the global topology of the problem in addition to the local information given by the derivatives.
Complete code is given in C. The user can input any function to be minimized against any set of data.
A simplified explanation of the method is also given.
