Vishal Gupta, Dimitris Bertsimas, Boeing Professor of Operations Research
Enrollment: Limited: Advance sign-up required
Attendance: Participants welcome at individual sessions
Prereq: 15.093J
This is a multi-session course focusing on software tools specific to the practice of Operations Research. Sessions will not focus on the theory or background on techniques used, but rather on the mechanics of using software to apply those techniques. See individual descriptions for details.
Course 15.S60 - see Stellar page for more information: http://stellar.mit.edu/S/course/15/ia13/15.S60/index.html
Prerequisites: Familiarity with optimization at the level of 15.093J or permission from instructor. Proficiency in some mainstream programming language (C, C++, Python, Java, etc.)
Important: Participants should bring a laptop to all sessions or make prior arrangements to share with another student. All required software and datasets should be installed PRIOR to the start of the session. Instructors will not delay class to assist with installation issues. Detailed installation instructions are available on Stellar.
Sponsor(s): Operations Research Center
Contact: Vishal Gupta, 516 417 4290, VGUPTA1@MIT.EDU
| Jan/10 | Thu | 09:00AM-12:00PM | E62-221, Bring your laptop, please pre-install software |
Introduces the statistical programming environment [R]. Participants will learn basic functionality, including importing and storing data and performing basic analyses. Topics include linear/logistic regression, CART, random forests and clustering. This session focuses on applying these techniques and assumes participants are comfortable with basic statistics.
Allison O'Hair
| Jan/15 | Tue | 09:00AM-12:00PM | E62-221, Bring your laptop, please preinstall software |
Advanced tools and best programming practices in [R]. Topics include
This session builds on the previous session "[R] for Operations Researchers]
Andre Calmon
| Jan/22 | Tue | 09:00AM-12:00PM | E62-221, Bring your laptop, please preinstall software |
Introduce the Python-based linear/integer optimization problem modelling language "PuLP". Participants will implement models for the facility location and product mixing problems. They will also be introduced to more advanced techniques like column generation and the solver-specific CPLEX/GUROBI python interfaces.
Python experience preferred, but not required. See Stellar for some introductory Python material.
Iain Dunning
| Jan/24 | Thu | 09:00AM-12:00PM | E62-221, Bring your laptop. Preinstall software. |
Covers advanced cplex usage for integer optimization problems, specifically, the use of callbacks to:
TSP will be used as a running example. Code lab assumes familiarity with the java/cplex interface. .
Ross Anderson
| Jan/29 | Tue | 09:00AM-12:00PM | E62-221, Bring your laptop. Please preinstall software. |
Students will learn to distribute a single large-scale linear optimization problem across multiple computers. Topics covered include an overview of decomposition techniques and a guided development of distributed optimization programs. Students will leave the course with working code examples they can apply in their own research. Some software setup is required before the class, as detailed on the course stellar page.
John Silberholz
| Jan/31 | Thu | 09:00AM-12:00PM | E62-221, Bring your laptop. Preinstall software. |
Introduces the Matlab based modelling language CVX for convex optimization.
We also introduce the Python-based optimization software CVXOPT.
Vishal Gupta