PROC NLP: The NLP procedure (NonLinear Programming) offers a set of optimization techniques for minimizing or maximizing a continuous nonlinear function subject to linear and nonlinear, equality and inequality, and lower and upper bound constraints. Problems of this type are found in many settings ranging from optimal control to maximum likelihood estimation. Nonlinear programs can be input into the procedure in various ways. The objective, constraint, and derivative functions are specified using the programming statements of PROC NLP. In addition, information in SAS data sets can be used to define the structure of objectives and constraints, and to specify constants used in objectives, constraints, and derivatives.
References in zbMATH (referenced in 1 article )
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- Luo, Ji; Zhang, Jiajia; Sun, Han: Estimation of relative risk using a log-binomial model with constraints (2014)