Approximation Algorithms and Linear Programming

This course is part of Foundations of Data Structures and Algorithms Specialization

Instructor: Sriram Sankaranarayanan

What you'll learn

  •   Formulate linear and integer programming problems for solving commonly encountered optimization problems.
  •   Develop a basic understanding of how linear and integer programming problems are solved.
  •   Understand how approximation algorithms compute solutions that are guaranteed to be within some constant factor of the optimal solution

    • Skills you'll gain

  •   Applied Mathematics
  •   Computational Thinking
  •   Graph Theory
  •   Linear Algebra
  •   Theoretical Computer Science
  •   Algorithms
  •   Python Programming
  •   Mathematical Modeling
  •   Network Model
  •   Network Analysis
  •   Operations Research
  •   Combinatorics

    • There are 4 modules in this course

    This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

    Integer Linear Programming

    Approximation Algorithms : Scheduling, Vertex Cover and MAX-SAT

    Travelling Salesperson Problem (TSP) and Approximation Schemes

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