Course Summary
Learn how to design and analyze approximation algorithms for complex optimization problems in the second part of this course.Key Learning Points
- Discover the basics of designing and analyzing approximation algorithms
- Learn how to design algorithms to solve NP-hard problems
- Understand how to analyze the performance and guarantees of approximation algorithms
Job Positions & Salaries of people who have taken this course might have
- Algorithm Designer
- USA: $112,000
- India: ₹1,040,000
- Spain: €45,000
- Data Scientist
- USA: $120,000
- India: ₹1,500,000
- Spain: €51,000
- Research Scientist
- USA: $100,000
- India: ₹1,200,000
- Spain: €40,000
Related Topics for further study
Learning Outcomes
- Design and analyze approximation algorithms for complex optimization problems
- Understand the basics of designing and analyzing approximation algorithms
- Analyze the performance and guarantees of approximation algorithms
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of algorithms and data structures
- Familiarity with mathematical notation and algorithms analysis
Course Difficulty Level
AdvancedCourse Format
- Online
- Self-paced
Similar Courses
- Approximation Algorithms: Part I
- Algorithms and Data Structures
- Discrete Optimization
Related Education Paths
Notable People in This Field
- Christos Papadimitriou
- Nina Balcan
Related Books
Description
Approximation algorithms, Part 2
Outline
- Linear Programming Duality
- Linear programming duality - example
- Properties of LP duality
- Geometry of LP duality
- Proof of weak duality theorem
- Changing the form of the LP
- Complementary slackness
- Primal-dual algorithms
- Vertex cover by primal-dual
- Conclusion
- Slides
- Comment
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides-all
- Quiz 1
- Quiz 2
- Quiz 3
- Quiz 4
- Quiz 5
- Quiz 6
- Quiz 7
- Quiz 8
- Steiner Forest and Primal-Dual Approximation Algorithms
- Problem definition
- A special case: Steiner tree
- LP relaxation for Steiner forest
- ... and its dual
- Primal-dual algorithm, Part1
- Primal-dual algorithm,Part 2
- Analysis
- Proof of the main lemma
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides-all
- Quiz 1
- Quiz 2
- Quiz 3
- Quiz 4
- Quiz 5
- Quiz 6
- Quiz 7
- Quiz 8
- Facility Location and Primal-Dual Approximation Algorithms
- Problem definition
- A linear programming relaxation
- ...and its dual
- A primal-dual algorithm
- Analyzing the service cost
- Analyzing the facility opening cost
- A better algorithm
- Analysis
- Conclusion
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides-all
- Quiz 1
- Quiz 2
- Quiz 3
- Quiz 4
- Quiz 5
- Quiz 6
- Quiz 7
- Quiz 8
- Maximum Cut and Semi-Definite Programming
- Definition
- A 2-approximation
- A linear programming relaxation...
- ...with an integrality gap of almost 2
- Proof of Lemma
- A quadratic programming relaxation
- General facts about semidefinite programming
- A rounding algorithm
- Analysis
- General facts about MaxCut
- The end!
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Slides
- Sldies
- Slides
- Slides-all
- Comment
- Quiz 1
- Quiz 2
- Quiz 3
- Quiz 4
- Quiz 5
- Quiz 6
- Quiz 7
- Quiz 8
- Quiz 9
Summary of User Reviews
The Approximation Algorithms Part 2 course on Coursera has received positive reviews from many users. This course is highly recommended for anyone interested in approximation algorithms and the lectures are well-presented and easy to understand. The course is also well-structured and covers a broad range of topics in approximation algorithms.Key Aspect Users Liked About This Course
The course is well-presented and easy to understand.Pros from User Reviews
- The lectures are well-structured and cover a broad range of topics.
- The course is highly recommended for anyone interested in approximation algorithms.
- The instructors are knowledgeable and responsive to questions.
- The assignments are challenging and help reinforce the concepts taught in the lectures.
- The course is a great value for the price.
Cons from User Reviews
- The course assumes some prior knowledge of algorithms and mathematical concepts.
- The course can be challenging at times and requires a significant time commitment.
- The video lectures can be a bit dry and technical.
- The course could benefit from more interactive elements to help reinforce learning.
- The course could benefit from more real-world examples and applications.
Claire Mathieu
4.8 Raiting
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