Approximation Algorithms

Many real-world algorithmic problems cannot be solved efficiently using traditional algorithmic tools, for example, because the problems are NP-hard. The goal of the Approximation Algorithms course is to become familiar with important algorithmic concepts and techniques needed to effectively deal with such problems. These techniques apply when we don't require the optimal solution to certain problems, but an approximation that is close to the optimal solution. We will see how to efficiently find such approximations. Prerequisites: In order to successfully take this course, you should already have a basic knowledge of algorithms and mathematics. Here's a short list of what you are supposed to know: - O-notation, Ω-notation, Θ-notation; how to analyze algorithms - Basic calculus: manipulating summations, solving recurrences, working with logarithms, etc. - Basic probability theory: events, probability distributions, random variables, expected values etc. - Basic data structures: linked lists, stacks, queues, heaps - (Balanced) binary search trees - Basic sorting algorithms, for example MergeSort, InsertionSort, QuickSort - Graph terminology, representations of graphs (adjacency lists and adjacency matrix), basic graph algorithms (BFS, DFS, topological sort, shortest paths) The material for this course is based on the course notes that can be found under the resources tab.

Status: Theoretical Computer Science

Status: Operations Research

IntermediateCourse15 hours

Featured reviews

4.0Reviewed Oct 10, 2020

Please try to include some more numeric example like load balancing problem in the vertex cover and rest topics

4.0Reviewed Feb 24, 2021

Very good course! A nice introduction to approximation algorithms.

All reviews

Showing: 9 of 9

Suryendu Dalal

3.0

Reviewed Jul 18, 2020

The course was no doubt excellent. At the end of the day you are going to earn a mouth watering certificate signed by one of the best computer scientists in the world. Prof. Mark de Berg. Professor speaks english very well and hence no one will face any problem related to language. Also professor taught the course extremely well. But unfortunately this course is completely inactive. All the questions in discussion forums remains unanswered. There was a problem is Week 2 Assignment 'PTAS for Load Balancing', where your correct answer will be considered wrong. Mentors of this course are sitting idle. They do not provide any assistance to the students. This course really needs a mentor who is active.

Dongyun Kim

5.0

Reviewed May 4, 2021

Short but compact course that discusses important topics. The quizes and programming homeworks are challenging enough to help to check your studying procedure. Prof. Mark de Berg is an amazing instructor and gives clear lecture videos. One small tip will be to check the Errata sheet before studying. Overall a compact and helpful course.

ChocolateCharlie

5.0

Reviewed Nov 23, 2020

Nice introductory course which combines both theory and practice. Though these algorithms are covered in the course, a previous experience with greedy algorithms and dynamic programming might be helpful.

Jakob B.

5.0

Reviewed Jan 27, 2021

Excellent short course on approximation algorithms. Good course material, presentations and exercises.

Kalina Bakardzhieva

5.0

Reviewed Sep 23, 2023

The material is explained very clearly. The examples are very useful. Thank you for this content!

周柏宇

5.0

Reviewed Aug 13, 2020

A great introductory course to the approximation algorithms.