Approximation Algorithms Part II
Master advanced techniques in approximation algorithms, focusing on linear programming duality and semidefinite programming.
Master advanced techniques in approximation algorithms, focusing on linear programming duality and semidefinite programming.
This course is a continuation of Approximation Algorithms Part I, delving deeper into advanced techniques for solving combinatorial optimization problems. Students will learn to apply linear programming duality to design approximation algorithms for complex problems such as Steiner forest and facility location. The course also introduces semidefinite programming and its application to the maximum cut problem. With a focus on theoretical foundations, students will develop skills in recognizing problem structures, designing linear programming relaxations, and using randomized rounding techniques. This course is ideal for those seeking to expand their knowledge in theoretical computer science and algorithmic problem-solving.
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What you'll learn
Understand and apply linear programming duality in algorithm design
Design approximation algorithms for Steiner forest and facility location problems
Master the use of primal-dual techniques in algorithm analysis
Learn the fundamentals of semidefinite programming and its applications
Develop skills in designing and analyzing algorithms for complex optimization problems
Gain proficiency in recognizing problem structures and applying appropriate solution techniques
Skills you'll gain
This course includes:
3 Hours PreRecorded video
33 assignments
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There are 4 modules in this course
This course offers an in-depth exploration of advanced approximation algorithms, building upon the foundations laid in Part I. Students will master linear programming duality and its applications in designing algorithms for complex problems like Steiner forest and facility location. The curriculum also covers semidefinite programming, introducing its powerful applications in solving problems such as maximum cut. Throughout the course, emphasis is placed on theoretical understanding and analysis, equipping students with the skills to approach new combinatorial optimization problems systematically.
Linear Programming Duality
Module 1 · 8 Hours to complete
Steiner Forest and Primal-Dual Approximation Algorithms
Module 2 · 7 Hours to complete
Facility Location and Primal-Dual Approximation Algorithms
Module 3 · 7 Hours to complete
Maximum Cut and Semi-Definite Programming
Module 4 · 8 Hours to complete
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Instructor
1st class research director in Computer Science at Centre National de la Recherche Scientifique (CNRS)
Claire Mathieu is a French computer scientist and mathematician, known for her research on approximation algorithms, online algorithms, and auction theory. She works as a director of research at the Centre national de la recherche scientifique.
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4.8 course rating
44 ratings
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