This book describes the NLP procedure for nonlinear programming and is a complete user's guide including examples. Nonlinear programming or nonlinear optimization is used in many application areas ranging from finance, engineering, and operations research to statistics. The NLP procedure supports optimization of continuous nonlinear objective functions constrained by boundary, linear, and nonlinear constraints. It is a robust optimizer that offers many alternative solution algorithms including: Quasi-Newton, Conjugate Gradient, Levenberg-Marquardt, Newton-Raphson, Trust-Region, Simplex, and Linear Complementarity, to name a few.
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