Approximation Algorithms and Linear Programming
Master optimization techniques with linear programming and approximation algorithms for NP-hard problems.
Master optimization techniques with linear programming and approximation algorithms for NP-hard problems.
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 Foundations of Data Structures and Algorithms 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
Formulate linear and integer programming problems
Implement optimization algorithms in Python
Analyze approximation algorithm guarantees
Solve complex scheduling and network problems
Understand NP-hardness and complexity theory
Master the Traveling Salesperson Problem
Skills you'll gain
This course includes:
9.1 Hours PreRecorded video
18 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores advanced optimization techniques through linear and integer programming. Students learn to formulate and solve complex problems in resource allocation, scheduling, and network optimization. The course covers approximation algorithms for NP-hard problems, providing guaranteed bounds on solution quality. Special focus is given to practical implementation using Python and solving real-world optimization challenges.
Linear Programming
Module 1 · 11 Hours to complete
Integer Linear Programming
Module 2 · 13 Hours to complete
Approximation Algorithms : Scheduling, Vertex Cover and MAX-SAT
Module 3 · 10 Hours to complete
Travelling Salesperson Problem (TSP) and Approximation Schemes
Module 4 · 12 Hours to complete
Fee Structure
Instructor
Professor
Sriram Sankaranarayanan is a professor of Computer Science at the University of Colorado Boulder, where he teaches a variety of courses on algorithms, theory of computation, mathematical optimization and programming languages. His research studies how computers can be used to verify and design other computer systems by combining ideas from mathematical logic, theory of computation and control theory. He uses these to analyze "safety-critical" systems ranging from autonomous vehicles to artificial pancreas devices for patients with type-1 diabetes. Sriram obtained a PhD in computer science from Stanford University. Subsequently he worked as a research staff member at NEC research labs in Princeton, NJ. He has been on the faculty at CU Boulder since 2009. Sriram has been the recipient of awards including the CAREER award from NSF, and the Provost's faculty achievement award at CU Boulder.
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