SAS/IML software has many linear operators that perform high-level operations commonly needed in applying linear algebra techniques to data analysis. The similarity of the Interactive Matrix Language notation and matrix algebra notation makes translation from algorithm to program a straightforward task. The examples in this chapter show a variety of matrix operators at work.

You can use these examples to gain insight into the more complex problems you may need to solve. Some of the examples perform the same analyses as performed by procedures in SAS/STAT software and are not meant to replace them. The examples are included as learning tools.

Example 8.1: Correlation
Example 8.2: Newton's Method for Solving Nonlinear Systems of Equations
Example 8.3: Regression
Example 8.4: Alpha Factor Analysis
Example 8.5: Categorical Linear Models
Example 8.6: Regression of Subsets of Variables
Example 8.7: Response Surface Methodology
Example 8.8: Logistic and Probit Regression for Binary Response Models
Example 8.9: Linear Programming
Example 8.10: Quadratic Programming
Example 8.11: Regression Quantiles
Example 8.12: Simulations of a Univariate ARMA Process
Example 8.13: Parameter Estimation for a Regression Model with ARMA Errors
Example 8.14: Iterative Proportional Fitting
Example 8.15: Full-Screen Nonlinear Regression

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

The grammer of  SAS/IML  is very similar to that of MATLAB.   And these two are interconvertible for a sophiscated programmer.

Thank you, I found it can also be applied to pricing financial products, with the method such as finite element and finite difference.
http://blog.sina.com.cn/s/blog_62615f170100fbw5.html
It's very similar to the matlab.^^