There is a new version of this course. Please see Optimization Concepts for Data Science and Artificial Intelligence.

This course focuses on linear, nonlinear, and mixed-integer linear optimization concepts in SAS Viya. Participants learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. The demonstrations in the course include examples of diet formulation and portfolio optimization. The OPTMODEL procedure is used to solve optimization problems that reinforce concepts introduced in the course.

Learn how to

• Identify and formulate appropriate approaches to solving various linear, mixed-integer linear, and nonlinear optimization problems.
• Create optimization models commonly used in industry.
• Solve optimization problems using the OPTMODEL procedure in SAS.

Who should attend

Those who want to develop the optimization foundation necessary to work as a data scientist, especially those with a strong background in applied mathematics

Formats available	Standard Duration (duration can vary, see event schedule for details)
Live Web Classroom:	2 half-day session(s)	System Requirements

Before enrolling in this course, you should be comfortable with data manipulation using basic SAS tools. You can gain this course-specific knowledge in data manipulation by completing the SAS Programming 1: Essentials course. Some knowledge of linear programming concepts and matrix algebra is helpful but is not required.

This course addresses SAS Optimization software.

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).
• Applied data envelopment analysis (self-study).
• Reading SAS data sets (self-study).

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.