Algorithms + Data Structures = Programs is a 1976 book written by Niklaus Wirth covering some of the fundamental topics of computer programming, particularly that algorithms and data structures are inherently related. For example, if one has a sorted list one will use a search algorithm optimal for sorted lists.
This book is not intended to be a comprehensive introduction to algorithms and data structures. For this, there are other books. Instead, the authors have focused on a smattering of fundamental topics that provide the student with tools for the...read more
This book is not intended to be a comprehensive introduction to algorithms and data structures. For this, there are other books. Instead, the authors have focused on a smattering of fundamental topics that provide the student with tools for the study of other topics that were left out in the book. This book is not for beginners -- and it does not teach students how to program. Throughout the book, algorithmic and data structure-related ideas are cast in Pascal-style pseudo-code that has the benefit of being easy to assimilate and has none of the complications of \"modern\" programming languages.
As the title suggests, this is not a dry text on algorithms and data structures. There is a welcome emphasis on applying the algorithms and the data structures covered to real problems in computer graphics and geometry. In fact, Part VI of the book is intended to show the usefulness of data structures for the purpose of efficient implementation of algorithms that manipulate geometric objects.
In spite of being a comprehensive compendium of algorithms and data structures, the book can, and has been used successfully, in undergraduate courses dealing with algorithm design and data structures. It can also be used for self-study by all those who wish to broaden their horizon and wish to acquire a solid footing in the fundamentals of computer science.
Jürg Nievergelt has been full Professor of Computer Science at the ETH Zurich from 1975 until his retirement in 2003. J. Nievergelt received a degree in mathematics from the ETH in 1962, and a Ph.D. in mathematics from the University of Illinois in 1965. From 1965-77 he was on the faculty of Computer Science at the University of Illinois at Urbana-Champaign, from assistant professor to full professor. Since 1975 professor of computer science at ETH Zurich. On leave from ETH 1985-89 he was Kenan Professor and chairman of the Computer Science Dept. at the Univ. of North Carolina at Chapel Hill. Visiting appointments include NTT's Yokosuka Electrical Communications Lab, Visiting IBM Professor at Keio University, and Visiting Professor at the National University of Singapore. He is a Fellow of ACM, IEEE and AAAS. Research Interests: Algorithms and data structures; interactive systems; user interfaces; heuristic and exhaustive search, parallel computation.
Recursion comes with several advantages and disadvantages, so it is definitely not always the right solution. Developers should use it wisely and don't overuse it. It helps to beautifully handle some certain complex problems such as tree traversing or exploring hierarchical data structures, so it is always good to know the right time to use it.
Recursion is an important approach that is not complicated at all and every developer should be aware of it. All the recursive method does is to call itself, usually many times until certain criteria are met. It is clearly not suited for every problem, but it is more than helpful when traversing data trees and other hierarchical structures.
The course covers data structures and associated algorithms. Relationships among data structures, their utility in various situations, and factors affecting their performance in algorithms will be considered. You will learn to analyze the demands of algorithms, how to choose appropriate data structures, and how to integrate data structures into algorithms.
The course covers the fundamentals of implementation and data structures such as linked lists, stacks, queues, hash tables and trees as well as sorting and search algorithms. The course also takes up frequently occurring design patterns, for example, flyweight, observer, permit, abstract factory or visitor.
An introductory course in problem solving and computer programming using the programming language Java. The course focuses on the fundamental concepts of problem solving and the techniques associated with the development of algorithms and their implementation as computer programs. This course or its equivalent is required for all additional courses in CMPSCI. Three hours of lecture/recitation per week. About 6 programming problems are assigned. In addition there are assigned homework problems, a midterm exam and a final. No computer science prerequisite, although high school algebra and basic math skills (e.g. R1) are assumed. Use of computer is required. 3 credits.
The course introduces and develops methods for designing and implementing abstract data types, using the Java programming language. The main focus is on how to build and encapsulate data objects and their associated operations. Specific topics includepointer variables, linked structures, recursive structures and algorithms, binary trees, balanced trees, and hash tables. There will be weekly assignments, consisting of programming and written exercises, a midterm, and a final exam. Prerequisites: CMPSCI 121 (or equivalent-not necessarily in Java) and Basic Math Skills (R1). Basic Java language concepts are introduced quickly. Prior experience with an object-oriented language is very helpful; if unsure of background, contact instructor. 4 credits.
Lecture, discussion, programming projects, lab. Programming Language Paradigms provide a basic philosophy to support the construction of reliable large-scale systems. This course discusses four such paradigms. In the -functional paradigm- software is constructed in such a way as to minimize the use of side-effects, thereby simplifying the conceptual structure of programs and making them easier to analyze and understand. The -logic paradigm- supports the development of software in which the concept of -relation- (for example a relation between people) is more important than the concept of function. In the -imperative paradigm- we are concerned with principled ways in which software that is state-dependent can be constructed. Finally, in the -object-oriented- paradigm we are concerned with combining data with code to create \"objects\" which exhibit behavior. Prerequisite: CMPSCI 187. Corequisites: CMPSCI 250 and MATH 132. 4 credits.
This course provides an in-depth examination of principles of distributed operating systems. Covered topics include processes and threads, concurrent programming, distributed interprocess communication, distributed process scheduling, shared virtual memory, distributed file systems. In-depth examples will be taken from current operating systems such as UNIX and MACH. Some coverage of operating system principles for multiprocessors will also be included. Prerequisites: The student should be able to easily program in a high-level language such as C, have had a course on data structures, be familiar with elements of computer architecture and have had previous exposure to the operating system concepts of processes, virtual memory, and scheduling. A previous course on uniprocessor operating systems (e.g., CMPSCI 377) will be helpful but not required. 3 credits.
We study more advanced material in the theory and implementation of algorithms. Possible topics include approximate string matching, computational biology, computational geometry, cryptography, approximate solution to combinatorial optimization problems, advanced data structures, parallel algorithms, and data compression. Typically, students research an area of mutual interest, and present it to the rest of the class. 1 credit. 59ce067264