Target group
Those who want to develop the optimization foundation necessary to work as a data scientist, especially those with a strong background in applied mathematics
Course structure
Introduction to Mathematical Optimization
- Introduction.
- A simple example.
- The OPTMODEL procedure.
Linear Programming
- Introduction to linear programming.
- Formulating and solving linear programming problems using the OPTMODEL procedure.
- Using index sets and arrays in the OPTMODEL procedure.
- Dual values and reduced costs in the simplex method (self-study).
- Reading and writing data in the OPTMODEL procedure.
Nonlinear Programming
- Introduction to nonlinear programming.
- Solving nonlinear programming problems using the OPTMODEL procedure.
Integer and Mixed Integer Linear Programming
- Introduction to integer and mixed integer linear programming.
- Solving integer and mixed integer linear programming problems using PROC OPTMODEL.
Open Source Interactivity
- SAS Viya and open source integration
- SAS Viya Python APIs.
