Optimal speedup of Las Vegas algorithms
Information Processing Letters
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Optimal constructions of hybrid algorithms
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
RESTART Technology for Solving Discrete Optimization Problems
Cybernetics and Systems Analysis
Optimal Parallelization of Las Vegas Algorithms
STACS '94 Proceedings of the 11th Annual Symposium on Theoretical Aspects of Computer Science
Formal Models of Heavy-Tailed Behavior in Combinatorial Search
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Metaheuristics: computer decision-making
Metaheuristics: computer decision-making
Optimization Parallelizing for Discrete Programming Problems
Cybernetics and Systems Analysis
An Advanced Tabu Search Algorithm for the Job Shop Problem
Journal of Scheduling
Restart strategies in optimization: parallel and serial cases
Parallel Computing
On algorithm portfolios and restart strategies
Operations Research Letters
| Hi-index | 0.00 |
We discuss two possible parallel strategies for randomized restart algorithms. Given a set of available algorithms, one can either choose the best performing algorithm and run its multiple copies in parallel single algorithm portfolio or choose some subset of algorithms to run in parallel mixed algorithm portfolio. It has been previously shown that the latter approach may provide better results computationally. In this paper, we provide theoretical investigation of the extent of such improvement generalizing some of the known results from the literature. In particular, we estimate the computational value of mixing randomized restart algorithms with different properties. Under some mild assumptions, we prove that in the best case the mixed algorithm portfolio may perform approximately up to 1.58 times faster than the best single algorithm portfolio. We also show that the obtained upper bound is sharp. Furthermore, the constructive proof of the main result allows us to characterize algorithms that are likely to form an effective mixed algorithm portfolio.