INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization
- ORNL
It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1055043
- Report Number(s):
- ORNL/TM-2012/176; KJ0401000; ERKJU04
- Country of Publication:
- United States
- Language:
- English
Similar Records
Parallel Algorithms for Graph Optimization using Tree Decompositions
Integrated Network Decompositions and Dynamic Programming for Graph Optimization (INDDGO)