NP-Complete Problems: Advanced Algorithmic Techniques
This course is part of Algorithms and Data Structures.
This advanced algorithms course explores NP-Complete problems - a class of computational challenges that lack known efficient solutions. As part of the Algorithms and Data Structures MicroMasters program, students learn practical approaches to tackle these inherently hard problems. The course covers specialized techniques including SAT-solvers, approximate algorithms, and heuristic methods. Students gain hands-on experience with efficient software tools and learn to identify special cases where polynomial-time solutions exist. Through practical examples and exercises, participants develop skills to handle real-world computational challenges despite their theoretical complexity.
Instructors:
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What you'll learn
Understand NP-completeness and its implications in computing
Master techniques for approximating solutions to hard problems
Implement heuristic algorithms for practical problem solving
Identify and solve special cases of NP-complete problems efficiently
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 3 modules in this course
This course delves into the challenging world of NP-Complete problems, which are fundamental to computer science but lack efficient algorithmic solutions. Students explore various strategies for handling these computationally intensive problems, including special case analysis, approximation algorithms, and heuristic approaches. The curriculum covers both theoretical aspects and practical implementations, helping students understand when and how to apply different solution techniques effectively. The course emphasizes real-world applications and provides hands-on experience with specialized software tools.
NP-Complete Problems
Module 1
Coping with NP-completeness: special cases
Module 2
Coping with NP-completeness: exact and approximate algorithms
Module 3
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Algorithms and Data Structures
Instructors
8 Courses
Mathematics and Theoretical Computer Science Prodigy
Daniel Mertz Kane serves as a full professor with a joint appointment in Mathematics and Computer Science at the University of California, San Diego, where he has established himself as a leading researcher in theoretical computer science, combinatorics, and number theory. Born in 1986 to academic parents in Madison, Wisconsin, his extraordinary mathematical talent emerged early, mastering K-9 mathematics by third grade and conducting university-level research under Ken Ono while still in high school. His academic achievements include two gold medals in the International Mathematical Olympiad (2002, 2003), being one of only eight people in history to become a four-time Putnam Fellow, and winning the 2007 Morgan Prize. After earning dual bachelor's degrees from MIT in mathematics with computer science and physics (2007), he completed his Ph.D. at Harvard under Barry Mazur in 2011. His research contributions span multiple areas, including groundbreaking work in computational statistics, Boolean functions, and machine learning, earning him numerous awards including the IBM Pat Goldberg Memorial and PODS best paper awards. He recently co-authored a book on robust statistics with Ilias Diakonikolas, to be published by Cambridge University Press
13 Courses
Distinguished Computer Scientist and Algorithms Expert
Alexander S. Kulikov serves as a visiting professor at the University of California, San Diego, and a leading research fellow at the Steklov Institute of Mathematics in St. Petersburg. His academic journey includes earning his Ph.D. in 2009 and Dr.Sci. in 2017 from the St. Petersburg Department of Steklov Institute of Mathematics. His research focuses on algorithms for NP-hard problems and circuit complexity, with significant contributions to computational complexity theory and algorithm design. He has authored several influential educational resources, including "Learning Algorithms Through Programming and Puzzle Solving" and co-created major online courses on platforms like Coursera and edX. His teaching experience spans more than eight years, during which he has developed innovative approaches to algorithms education. Currently at JetBrains as a researcher, he continues to advance the field through his work on algorithmic problem-solving and computational complexity, while maintaining his academic connections through his visiting professorship at UCSD and research position at Steklov Institute.
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