Linear Optimization: Principles and Algorithms
Master linear optimization techniques, including duality and simplex algorithm, in this introductory course.
Master linear optimization techniques, including duality and simplex algorithm, in this introductory course.
Dive into the world of linear optimization with this comprehensive course. Learn to formulate, transform, and solve optimization problems using advanced techniques. The curriculum covers constraint representation, duality theory, optimality conditions, and the simplex method. Gain practical skills in problem-solving and algorithm implementation, with optional Python programming exercises. Ideal for those seeking to enhance their mathematical and analytical abilities in optimization.
Instructors:
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What you'll learn
Formulate and characterize optimization problems using real-world examples
Represent constraints in linear optimization problems geometrically and algebraically
Derive and analyze dual problems in linear optimization
Apply sufficient and necessary conditions for optimal solutions
Implement the simplex algorithm to solve linear optimization problems
Transform complex optimization problems into solvable formats
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
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Limited Access access
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Module Description
This course provides a comprehensive introduction to linear optimization, covering key concepts and techniques essential for solving complex problems. Students will learn how to formulate and transform optimization problems, represent constraints both geometrically and algebraically, and derive dual problems. The course delves into optimality conditions and the simplex method, equipping learners with practical skills to solve linear optimization problems. While Python programming knowledge is beneficial for understanding algorithm details, the course is designed to be accessible to those without programming experience. Through a combination of theoretical foundations and practical applications, students will develop a strong understanding of linear optimization principles and their real-world applications in various fields such as operations research, economics, and engineering.
Fee Structure
Instructor
5 Courses
Pioneer in Transportation Systems and Operations Research
Michel Bierlaire, born in 1967 in Namur, Belgium, is a Belgian-Swiss applied mathematician and Full Professor at École polytechnique fédérale de Lausanne (EPFL), where he directs the Transport and Mobility Laboratory since 2006. After earning his Ph.D. in Mathematical Sciences from the University of Namur in 1996, he worked at MIT's Intelligent Transportation Systems Program (1995-1998) developing real-time traffic simulation tools. He joined EPFL in 1998 as a senior scientist, progressing to Associate Professor in 2006 and Full Professor in 2012. His research focuses on transportation modeling, discrete choice models, and operations research, with significant contributions to demand modeling and traffic management systems. He founded the European Association for Research in Transportation and developed Biogeme, an open-source project for discrete choice model estimation. His scholarly output includes over 150 papers in international journals, 4 books, and numerous book chapters and conference proceedings. He has served as director of TraCE Transportation Center (2009-2022) and head of the Civil Engineering Institute (2017-2021), while maintaining leadership roles in various international research organizations
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