Approximation Algorithms
Master techniques for solving NP-hard problems: Learn approximation algorithms to find near-optimal solutions efficiently.
Master techniques for solving NP-hard problems: Learn approximation algorithms to find near-optimal solutions efficiently.
This course introduces approximation algorithms as a powerful tool for tackling real-world algorithmic problems that cannot be solved efficiently using traditional methods. It focuses on techniques for finding near-optimal solutions to NP-hard problems efficiently. The curriculum covers key concepts such as approximation ratios, LP relaxation, and Polynomial-Time Approximation Schemes (PTAS). Through practical examples like load balancing, vertex cover, and knapsack problems, students will learn to design and analyze approximation algorithms. The course emphasizes both theoretical understanding and practical implementation, providing a solid foundation for dealing with complex optimization problems in various fields of computer science and engineering.
4.7
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What you'll learn
Understand the concept and importance of approximation algorithms for NP-hard problems
Learn to analyze the quality of approximation algorithms using approximation ratios
Master techniques for designing approximation algorithms, including greedy approaches and LP relaxation
Develop skills in implementing and analyzing Polynomial-Time Approximation Schemes (PTAS)
Gain practical experience with real-world problems such as load balancing, vertex cover, and knapsack
Learn to apply lower and upper bounds in the analysis of approximation algorithms
Skills you'll gain
This course includes:
3 Hours PreRecorded video
4 assignments
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There are 4 modules in this course
This course provides a comprehensive introduction to approximation algorithms, a crucial technique for solving NP-hard problems efficiently. Students will learn about the motivation behind approximation algorithms and their applications in real-world scenarios. The course covers key concepts such as approximation ratios, LP relaxation, and Polynomial-Time Approximation Schemes (PTAS). Through practical examples like load balancing, vertex cover, and knapsack problems, students will gain hands-on experience in designing and analyzing approximation algorithms. The curriculum emphasizes both theoretical understanding and practical implementation, providing a solid foundation for dealing with complex optimization problems in various fields of computer science and engineering.
Introduction to Approximation algorithms
Module 1 · 1 Hours to complete
The Load Balancing problem
Module 2 · 4 Hours to complete
LP Relaxation
Module 3 · 2 Hours to complete
Polynomial-time approximation schemes
Module 4 · 6 Hours to complete
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Instructor
Leading Expert in Algorithms and Spatial Data at TU Eindhoven
Mark de Berg earned his MSc in computer science from Utrecht University in 1988 and completed his PhD there in 1992. He is currently a full professor at TU Eindhoven, specializing in algorithms and data structures with a particular focus on spatial data.
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4.7 course rating
32 ratings
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