Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Approximating the Throughput of Multiple Machines in Real-Time Scheduling
SIAM Journal on Computing
Priority algorithms for makespan minimization in the subset model
Information Processing Letters
Removable Online Knapsack Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Interval selection: applications, algorithms, and lower bounds
Journal of Algorithms
An efficient fully polynomial approximation scheme for the Subset-Sum problem
Journal of Computer and System Sciences
Approximation Algorithms for the Job Interval Selection Problem and Related Scheduling Problems
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Models of greedy algorithms for graph problems
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
A Note on Scheduling Equal-Length Jobs to Maximize Throughput
Journal of Scheduling
Priority algorithms for graph optimization problems
WAOA'04 Proceedings of the Second international conference on Approximation and Online Algorithms
Randomized priority algorithms
Theoretical Computer Science
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Greedy algorithms are simple, but their relative power is not well understood. The priority framework [5] captures a key notion of "greediness" in the sense that it processes (in some locally optimal manner) one data item at a time, depending on and only on the current knowledge of the input. This algorithmic model provides a tool to assess the computational power and limitations of greedy algorithms, especially in terms of their approximability. In this paper, we study priority algorithm approximation ratios for the Subset-Sum Problem, focusing on the power of revocable decisions. We first provide a tight bound of α ≈ 0.657 for irrevocable priority algorithms. We then show that the approximation ratio of fixed order revocable priority algorithms is between β; ≈ 0.780 and γ ≈ 0.852, and the ratio of adaptive order revocable priority algorithms is between 0.8 and δ ≈ 0.893.