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Methods of Nonlinear Optimization*
The previous two chapters developed enough background to motivate a reader to approach nonlinear optimization in a systematic manner. Therefore, for an applied mathematics course with a broad agenda and limited time, the present chapter may be considered as optional. However, to save the reader the disappointment of stopping far behind the professional practice, this chapter attempts to provide a panoramic glimpse of the rich and beautiful mathematical theory underlying the practical methods of optimization. It also conveys a message that successful methods of nonlinear optimization typically do not rely completely on immediate local information. They learn from history.
Conjugate Direction Methods
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