Delivery Problem
This course is part of Introduction to Discrete Mathematics for Computer Science.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Introduction to Discrete Mathematics for Computer Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Implement TSP solutions using different algorithms
Analyze algorithm efficiency and trade-offs
Apply optimization techniques to real problems
Develop approximation algorithms
Understand computational complexity concepts
Skills you'll gain
This course includes:
1.85 Hours PreRecorded video
8 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This comprehensive course focuses on solving the Traveling Salesman Problem using various algorithmic approaches. Students learn to implement exact and approximation algorithms in Python, including brute force, branch and bound, dynamic programming, and local search methods. The curriculum covers theoretical foundations and practical implementations with real-world applications.
Traveling Salesman Problem
Module 1 · 5 Hours to complete
Exact Algorithms
Module 2 · 3 Hours to complete
Approximation Algorithms
Module 3 · 4 Hours to complete
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: Introduction to Discrete Mathematics for Computer Science
Instructor
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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Frequently asked questions
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