Introduction to parallel algorithms and architectures: array, trees, hypercubes
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Horizons of parallel computation
Journal of Parallel and Distributed Computing
Robust Proximity Queries: An Illustration of Degree-Driven Algorithm Design
SIAM Journal on Computing
Robust Plane Sweep for Intersecting Segments
SIAM Journal on Computing
The cube-connected cycles: a versatile network for parallel computation
Communications of the ACM
The Art of Computer Programming Volumes 1-3 Boxed Set
The Art of Computer Programming Volumes 1-3 Boxed Set
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
STOC '79 Proceedings of the eleventh annual ACM symposium on Theory of computing
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Sequencing-by-Hybridization Revisited: The Analog-Spectrum Proposal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Algorithms for Reporting and Counting Geometric Intersections
IEEE Transactions on Computers
| Hi-index | 5.23 |
Computation models are central to algorithmic research. A model, designed to capture the essential features of a technology dispensing with irrelevant and burdensome details, is a judicious compromise between simplicity and fidelity (or reflectivity). This approach has unleashed an enormous amount of valuable algorithmic research over the years. However, the pursuit of simplicity may filter out details, once deemed irrelevant, which may later reassert their significance under either technological pressure or more careful scrutiny, in which case the inadequacy of the model cripples the validity of the derived results. Examples of this situation, drawn from computational geometry, numerical parallel computation, VLSI theory, and computational biology, will be reviewed and examined in detail.