Computational Geometry: Methods and Applications
by Jianer Chen
Number of pages: 227
In this book, we concentrate on four major directions in computational geometry: the construction of convex hulls, proximity problems, searching problems and intersection problems. Computational geometry is of practical importance because Euclidean space of two and three dimensions forms the arena in which real physical objects are arranged. A large number of applications areas such as pattern recognition, computer graphics, image processing, operations research, statistics, computer-aided design, robotics, etc., have been the incubation bed of the discipline since they provide inherently geo metric problems for which efficient algorithms have to be developed. A large number of manufacturing problems involve wire layout, facilities location, cutting-stock and related geometric optimization problems. Solving these efficiently on a high-speed computer requires the development of new geo metrical tools, as well as the application of fast-algorithm techniques, and is not simply a matter of translating well-known theorems into computer programs. From a theoretical standpoint, the complexity of geometric algo rithms is of interest because it sheds new light on the intrinsic difficulty of computation.
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by Ian Parberry, William Gasarch - Prentice Hall
A collection of problems on the design, analysis, and verification of algorithms for practicing programmers who wish to hone and expand their skills, as a supplementary text for students, and as a self-study text for graduate students.
by James Aspnes - Yale University
Topics include programming in C; data structures (arrays, stacks, queues, lists, trees, heaps, graphs); sorting and searching; storage allocation and management; data abstraction; programming style; testing and debugging; writing efficient programs.
A data structure is a particular way of storing and organizing data in a computer so that it can be used efficiently. Contents of the book: Sequences; Dictionaries; Sets; Priority queues; Successors and neighbors; Integer and string searching.
by Herbert Edelsbrunner - Duke University
The main topics to be covered in this course are: Design Techniques; Searching; Prioritizing; Graph Algorithms; Topological Algorithms; Geometric Algorithms; NP-completeness. The emphasis will be on algorithm design and on algorithm analysis.