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Viewing: DNSC 8392 : Computational Optimization

Last approved: Tue, 18 Jul 2017 08:04:57 GMT

Last edit: Mon, 10 Jul 2017 19:08:38 GMT

Catalog Pages referencing this course
School of Business
Decision Sciences (DNSC)
DNSC
8392
Computational Optimization
Computational Optimization
Fall 2017
3
Course Type
Lecture
Default Grading Method
Letter Grade

No
No

Corequisites

10
Lejeune
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
No
 
No
Fee Type


No


The description, design, and programming of efficient computational methods for large-scale optimization problems; introduction to software, optimization solvers, and programming languages used by professionals to code and model industry-size optimization problems.
Students will:

• develop the ability to formulate good mathematical optimization formulations for large-scale problems;
• learn key concepts and the basic theory underlying the numerical solution of mathematical optimization problems;
• detect and exploit the properties of mathematical optimization problems to design computationally efficient methods for large-scale data-driven optimization problems;
• develop programming and coding skills to implement reformulation and algorithmic methods and to interact with off-the-shelf optimization solvers;
• incorporate modeling and computational optimization techniques into your research;
• get acquainted with the software, programming and modeling languages, and methods used by professionals to solve large-scale optimization problems. In that respect,

Course Attribute


Key: 10938