The University of Washington illustrating compiler
PLDI '90 Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementation
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A methodology for building application-specific visualizations of parallel programs
Journal of Parallel and Distributed Computing - Special issue on tools and methods for visualization of parallel systems and computations
LEDA: a platform for combinatorial and geometric computing
Communications of the ACM
The Stanford GraphBase: a platform for combinatorial algorithms
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Combinatorial algorithms test sets CATS: the ACM/EATCS platform for experimental research
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Software Visualization
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Concurrent Algorithms and Data Types Animation over the Internet
IFIP World Computer Congress on Fundamentals - Foundations of Computer Science
The path-transition paradigm: a practical methodology for adding animation to program interfaces
Journal of Visual Languages and Computing
Algorithm engineering: bridging the gap between algorithm theory and practice
Algorithm engineering: bridging the gap between algorithm theory and practice
| Hi-index | 0.00 |
Experimental Algorithmics is concerned with the design, implementation, tuning, debugging and performance analysis of computer programs for solving algorithmic problems. It provides methodologies and tools for designing, developing and experimentally analyzing efficient algorithmic codes and aims at integrating and reinforcing traditional theoretical approaches for the design and analysis of algorithms and data structures. In this paper we survey some relevant contributions to the field of Experimental Algorithmics and we discuss significant examples where the experimental approach helped in developing new ideas, in assessing heuristics and techniques, and in gaining a deeper insight about existing algorithms.