Theoretical aspects of Linear Programming
Basic Julia programming
Proficiency with linear and nonlinear solvers
Optimization is the search for the best and most effective solution. In this mathematics course, we will examine optimization through a Business Analytics lens. You will be introduced to the to the theory, algorithms, and applications of optimization. Linear and integer programming will be taught both algebraically and geometrically, and then applied to problems involving data. Students will develop an understanding of algebraic formulations, and use Julia/JuMP for computation. Theoretical components of the course are made approachable, and require no formal background in linear algebra or calculus.
The recommended audience for this course is undergraduates, as well as professionals interested in using optimization software. The content in this course has applications in logistics, marketing, project management, finance, statistics and machine learning.
Most of the course material will be covered in lecture and recitation videos, and only an optional textbook, available at no cost, will be used.
Students interested in the material prior to deciding on course enrollment can visit the MIT Open Courseware version of 15.053 Spring 2013. The topics of the 2013 subject were optimization modeling, algorithms, and theory. As a six week subject, 15.053x covers about half of the material of the 2013 subject. The primary focus of 15.053x is optimization modeling.
1. Linear programming
2. Geometry of linear programming
3. Integer programming I
4. Integer programming II
5. Sensitivity Analysis
6. Nonlinear programming
None, although a comfort with mathematics is expected.