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. All the features of this course are available for free. It does not offer a certificate upon completion. It is one of the eight universities of the Ivy League, and one of the nine Colonial Colleges founded before the American Revolution.
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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. All the features of this course are available for free. It does not offer a certificate upon completion. It is one of the eight universities of the Ivy League, and one of the nine Colonial Colleges founded before the American Revolution. We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem.
The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs.
We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singly-linked list or a resizing array.
We introduce two advanced Java features—generics and iterators—that simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems.
We introduce the sorting problem and Java's Comparable interface. We study two elementary sorting methods selection sort and insertion sort and a variation of one of them shellsort. We also consider two algorithms for uniformly shuffling an array. We conclude with an application of sorting to computing the convex hull via the Graham scan algorithm.
We study the mergesort algorithm and show that it guarantees to sort any array of n items with at most n lg n compares. We also consider a nonrecursive, bottom-up version.
We prove that any compare-based sorting algorithm must make at least n lg n compares in the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability. We introduce and implement the randomized quicksort algorithm and analyze its performance.
We also consider randomized quickselect, a quicksort variant which finds the kth smallest item in linear time. Finally, we consider 3-way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys. We introduce the priority queue data type and an efficient implementation using the binary heap data structure. This implementation also leads to an efficient sorting algorithm known as heapsort. We conclude with an applications of priority queues where we simulate the motion of n particles subject to the laws of elastic collision.
We define an API for symbol tables also known as associative arrays, maps, or dictionaries and describe two elementary implementations using a sorted array binary search and an unordered list sequential search.
When the keys are Comparable, we define an extended API that includes the additional methods min, max floor, ceiling, rank, and select. To develop an efficient implementation of this API, we study the binary search tree data structure and analyze its performance. In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert and many other operations.
We start with 1d and 2d range searching, where the goal is to find all points in a given 1d or 2d interval. To accomplish this, we consider kd-trees, a natural generalization of BSTs when the keys are points in the plane or higher dimensions. We also consider intersection problems, where the goal is to find all intersections among a set of line segments or rectangles.
We begin by describing the desirable properties of hash function and how to implement them in Java, including a fundamental tenet known as the uniform hashing assumption that underlies the potential success of a hashing application. Then, we consider two strategies for implementing hash tables—separate chaining and linear probing. Both strategies yield constant-time performance for search and insert under the uniform hashing assumption. We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors.
As per Princeton University policy, no certificates, credentials, or reports are awarded in connection with this course. Our central thesis is that algorithms are best understood by implementing and testing them.
Our use of Java is essentially expository, and we shy away from exotic language features, so we expect you would be able to adapt our code to your favorite language. However, we require that you submit the programming assignments in Java. Part I focuses on elementary data structures, sorting, and searching. Part II focuses on graph and string-processing algorithms. The exercises are primarily composed of short drill questions such as tracing the execution of an algorithm or data structure , designed to help you master the material.
The programming assignments involve either implementing algorithms and data structures deques, randomized queues, and kd-trees or applying algorithms and data structures to an interesting domain computational chemistry, computational geometry, and mathematical recreation. The assignments are evaluated using a sophisticated autograder that provides detailed feedback about style, correctness, and efficiency.
The interview questions are similar to those that you might find at a technical job interview. They are optional and not graded. This course is for anyone using a computer to address large problems and therefore needing efficient algorithms. The two courses are complementary. This one is essentially a programming course that concentrates on developing code; that one is essentially a math course that concentrates on understanding proofs.
This course is about learning algorithms in the context of implementing and testing them in practical applications; that one is about learning algorithms in the context of developing mathematical models that help explain why they are efficient.
In typical computer science curriculums, a course like this one is taken by first- and second-year students and a course like that one is taken by juniors and seniors. More questions? Visit the Learner Help Center. Loupe Copy. Computer Science. Algorithms, Part I. Offered By.
Algorithms, Part I Princeton University. About this Course 1,, recent views. Career direction. Career Benefit. Career promotion. Flexible deadlines. Flexible deadlines Reset deadlines in accordance to your schedule. Intermediate Level. Hours to complete. Available languages. English Subtitles: English, Korean, Russian.
Instructor rating 4. Kevin Wayne Phillip Y. Goldman '86 Senior Lecturer Computer Science. Robert Sedgewick William O. Offered by. Week 1. Welcome to Algorithms, Part I. Video 1 video. Course Introduction 9m. Reading 2 readings. Welcome to Algorithms, Part I 1m. Video 5 videos. Dynamic Connectivity 10m. Quick Find 10m. Quick Union 7m. Quick-Union Improvements 13m. Overview 1m. Quiz 1 practice exercise. Video 6 videos.
Analysis of Algorithms Introduction 8m. Observations 10m. Mathematical Models 12m. Order-of-Growth Classifications 14m. Theory of Algorithms 11m. Memory 8m.
Algorithms, Part I
Explore a preview version of Algorithms, Fourth Edition right now. This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The course offers more than video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching.
Algorithms, Fourth Edition
Would you like to tell us about a lower price? If you are a seller for this product, would you like to suggest updates through seller support? This fourth edition of Robert Sedgewick and Kevin Wayne's Algorithms is the leading textbook on algorithms today and is widely used in colleges and universities worldwide. This book surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing--including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use.