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ABSTRACT:
This article describes a variable metric minimizer, a program that finds the set of parameters to a function that will produce the smallest output value from that function.
The program uses methods now accepted as both robust and efficient. These methods can handle a wide variety of functions and data without failing, and they can find a minimum in a shorter 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, starting on page 74 through page 86. The rest of the code listing is continued in the following month's issue, 11 (4) or #114, which appeared in April 1986, starting on page 84.
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STATISTICS
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