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The IML procedure offers a set of optimization subroutines for minimizing or maximizing a continuous nonlinear function f = f(x) of n parameters, where x = (x_1, ... ,x_n)^T. The parameters can be subject to boundary constraints and linear or nonlinear equality and inequality constraints.
Most of the results obtained from the IML procedure optimization and least-squares subroutines can also be obtained by using the NLP procedure in the SAS/OR product.
The advantages of the IML procedure are as follows:
You can use matrix algebra to specify the objective function, nonlinear constraints, and their derivatives in IML modules.
The IML procedure offers several subroutines that can be used to specify the objective function or nonlinear constraints, many of which would be very difficult to write for the NLP procedure.
You can formulate your own termination criteria by using the "ptit" module argument.
Nonlinear Optimization Examples
Example 11.1: Chemical Equilibrium
Example 11.2: Network Flow and Delay
Example 11.3: Compartmental Analysis
Example 11.4: MLEs for Two-Parameter Weibull Distribution
Example 11.5: Profile-Likelihood-Based Confidence Intervals
Example 11.6: Survival Curve for Interval Censored Data
Example 11.7: A Two-Equation Maximum Likelihood Problem
Example 11.8: Time-Optimal Heat Conduction
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