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Why this course
Linear Programming (LP) problems form an important class of optimization problems with many practical applications in production planning, resource allocation, investment decisions, scheduling, transportation and logistics, inventory management, game theory and many other contexts. Solution methods for Linear Programming problems such as the Simplex algorithm (Dantzig, 1947) are routinely used within optimization packages to solve even very large problems, and form the basis for sophisticated algorithms to solve discrete optimization problems with a wide range of practical applications.
This course presents the general theory and characteristics of LP problems and some of the main algorithms for their solution. After completing this course the student
- Can model several practical optimization problems as linear programming problems
- Understands the mathematical foundations of linear programming and duality theory
- Understands and can apply the main algorithms for solving linear programming problems
- Can use optimization software for implementing and solving linear and mixed-integer linear programs
Practical matters
Teaching events: 12 lectures (12x2h) and 12 exercise sessions (12x2h)
Assessment: Home assignments, final exam
Grading: 0-5
Study material: Lecture slides and exercises material, course book
Course book: D. Bertsimas, J. N. Tsitsiklis: Introduction to Linear Optimization, Athena Scientific 1997
Software used: IBM ILOG CPLEX Studio, Matlab
Language of instruction: English
Prerequisites: MS-C2105 - Optimoinnin perusteet (or equivalent)