This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
Offert par
Algorithmes, Partie II
Université de PrincetonÀ propos de ce cours
Compétences que vous acquerrez
- Graphs
- Data Structure
- Algorithms
- Data Compression
Offert par

Université de Princeton
Princeton University is a private research university located in Princeton, New Jersey, United States. It is one of the eight universities of the Ivy League, and one of the nine Colonial Colleges founded before the American Revolution.
Programme de cours : ce que vous apprendrez dans ce cours
Introduction
Welcome to Algorithms, Part II.
Undirected Graphs
We define an undirected graph API and consider the adjacency-matrix and adjacency-lists representations. We introduce two classic algorithms for searching a graph—depth-first search and breadth-first search. We also consider the problem of computing connected components and conclude with related problems and applications.
Directed Graphs
In this lecture we study directed graphs. We begin with depth-first search and breadth-first search in digraphs and describe applications ranging from garbage collection to web crawling. Next, we introduce a depth-first search based algorithm for computing the topological order of an acyclic digraph. Finally, we implement the Kosaraju−Sharir algorithm for computing the strong components of a digraph.
Minimum Spanning Trees
In this lecture we study the minimum spanning tree problem. We begin by considering a generic greedy algorithm for the problem. Next, we consider and implement two classic algorithm for the problem—Kruskal's algorithm and Prim's algorithm. We conclude with some applications and open problems.
Shortest Paths
In this lecture we study shortest-paths problems. We begin by analyzing some basic properties of shortest paths and a generic algorithm for the problem. We introduce and analyze Dijkstra's algorithm for shortest-paths problems with nonnegative weights. Next, we consider an even faster algorithm for DAGs, which works even if the weights are negative. We conclude with the Bellman−Ford−Moore algorithm for edge-weighted digraphs with no negative cycles. We also consider applications ranging from content-aware fill to arbitrage.
Maximum Flow and Minimum Cut
In this lecture we introduce the maximum flow and minimum cut problems. We begin with the Ford−Fulkerson algorithm. To analyze its correctness, we establish the maxflow−mincut theorem. Next, we consider an efficient implementation of the Ford−Fulkerson algorithm, using the shortest augmenting path rule. Finally, we consider applications, including bipartite matching and baseball elimination.
Radix Sorts
In this lecture we consider specialized sorting algorithms for strings and related objects. We begin with a subroutine to sort integers in a small range. We then consider two classic radix sorting algorithms—LSD and MSD radix sorts. Next, we consider an especially efficient variant, which is a hybrid of MSD radix sort and quicksort known as 3-way radix quicksort. We conclude with suffix sorting and related applications.
Tries
In this lecture we consider specialized algorithms for symbol tables with string keys. Our goal is a data structure that is as fast as hashing and even more flexible than binary search trees. We begin with multiway tries; next we consider ternary search tries. Finally, we consider character-based operations, including prefix match and longest prefix, and related applications.
Substring Search
In this lecture we consider algorithms for searching for a substring in a piece of text. We begin with a brute-force algorithm, whose running time is quadratic in the worst case. Next, we consider the ingenious Knuth−Morris−Pratt algorithm whose running time is guaranteed to be linear in the worst case. Then, we introduce the Boyer−Moore algorithm, whose running time is sublinear on typical inputs. Finally, we consider the Rabin−Karp fingerprint algorithm, which uses hashing in a clever way to solve the substring search and related problems.
Avis
- 5 stars94 %
- 4 stars4,94 %
- 3 stars0,46 %
- 2 stars0,29 %
- 1 star0,29 %
Meilleurs avis pour ALGORITHMES, PARTIE II
Another great course and perfect follow-up to Algorithms Part 1. I liked that we used the algorithms built in the first part to make other powerful algorithms and tools.
The algorithms are more difficult than part I, nevertheless Sedgewick's vids are still easy to understand. The only drawback maybe chapter 3, max flow min cut part, which is not very clarified.
Excellent follow-on from Part I - covers additional algorithms in depth and the excellent examples really help to learn and understand the material.
The exercise, while is very hard, is very well-prepared and selected for students to understand and appreciate the algorithm. The explanation is descriptive and detailed.
Foire Aux Questions
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Can I earn a certificate in this course?
I have no familiarity with Java programming. Can I still take this course?
Which algorithms and data structures are covered in this course?
What kinds of assessments are available in this course?
I am/was not a Computer Science major. Is this course for me?
How does this course differ from Design and Analysis of Algorithms?
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