From Zero to Cybersecurity Analyst

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Princeton University

Analytic Combinatorics

Princeton University via Coursera

Go to class Write review

Overview

Class Central Tips
Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Analytic Combinatorics (upon which the course is based) or to visit the website ac.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.

Syllabus

  • Combinatorial Structures and OGFs
    • Our first lecture is about the symbolic method, where we define combinatorial constructions that we can use to define classes of combinatorial objects. The constructions are integrated with transfer theorems that lead to equations that define generating functions whose coefficients enumerate the classes. We consider numerous examples from classical combinatorics.
  • Labelled Structures and EGFs
    • This lecture introduces labelled objects, where the atoms that we use to build objects are distinguishable. We use exponential generating functions EGFs to study combinatorial classes built from labelled objects. As in Lecture 1, we define combinatorial constructions that lead to EGF equations, and consider numerous examples from classical combinatorics.
  • Combinatorial Parameters and MGFs
    • This lecture describes the process of adding variables to mark parameters and then using the constructions form Lectures 1 and 2 and natural extensions of the transfer theorems to define multivariate GFs that contain information about parameters. We concentrate on bivariate generating functions (BGFs), where one variable marks the size of an object and the other marks the value of a parameter. After studying ways of computing the mean, standard deviation and other moments from BGFs, we consider several examples in some detail.
  • Complex Analysis, Rational and Meromorphic Asymptotics
    • This week we introduce the idea of viewing generating functions as analytic objects, which leads us to asymptotic estimates of coefficients. The approach is most fruitful when we consider GFs as complex functions, so we introduce and apply basic concepts in complex analysis. We start from basic principles, so prior knowledge of complex analysis is not required.
  • Applications of Rational and Meromorphic Asymptotics
    • We consider applications of the general transfer theorem of the previous lecture to many of the classic combinatorial classes that we encountered in Lectures 1 and 2. Then we consider a universal law that gives asymptotics for a broad swath of combinatorial classes built with the sequence construction.
  • Singularity Analysis
    • This lecture addresses the basic Flajolet-Odlyzko theorem, where we find the domain of analyticity of the function near its dominant singularity, approximate using functions from standard scale, and then transfer to coefficient asymptotics term-by-term.
  • Applications of Singularity Analysis
    • We see how the Flajolet-Odlyzko approach leads to universal laws covering combinatorial classes built with the set, multiset, and recursive sequence constructions. Then we consider applications to many of the classic combinatorial classes that we encountered in Lectures 1 and 2.
  • Saddle Point Asymptotics
    • We consider the saddle point method, a general technique for contour integration that also provides an effective path to the development of coefficient asymptotics for GFs with no singularities. As usual, we consider the application of this method to several of the classic problems introduced in Lectures 1 and 2.

Taught by

Robert Sedgewick

Tags

Reviews

4.0 rating, based on 3 Class Central reviews

4.6 rating at Coursera based on 65 ratings

Start your review of Analytic Combinatorics

  • Anonymous
    This course is very hard but its a really beautiful and interesting subject. Pr Sedgewick really knows what he talks about. It looks like there are prior courses that would make it easier to access this material, but I took the course without atten…
  • Anonymous
    I enjoy the contents from Prof Sedgewick, the exercises are especially challenging. Unfortunately this is basically self-learning with no staff. There were lots of questions left unanswered, and I decided to stop on Week 5 at the moment, but I plan to revisit this in the future.
  • Marat Minshin
Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.