Amortized efficiency of list update and paging rules
Communications of the ACM
Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
Optimal time-critical scheduling via resource augmentation (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Average-case analysis of off-line and on-line knapsack problems
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Speed is as powerful as clairvoyance
Journal of the ACM (JACM)
Resource augmentation for online bounded space bin packing
Journal of Algorithms
Removable Online Knapsack Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
An Online Partially Fractional Knapsack Problem
ISPAN '05 Proceedings of the 8th International Symposium on Parallel Architectures,Algorithms and Networks
Online Knapsack Problems with Limited Cuts
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Online removable knapsack with limited cuts
Theoretical Computer Science
FAW-AAIM'11 Proceedings of the 5th joint international frontiers in algorithmics, and 7th international conference on Algorithmic aspects in information and management
Online minimization knapsack problem
WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
Theoretical Computer Science
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It is known that online knapsack is not competitive. This negative result remains true even if the items are removable. In this paper we consider online removable knapsack with resource augmentation, in which we hold a knapsack of capacity R茂戮驴 1.0 and aim at maintaining a feasible packing to maximize the total weight of the items packed. Accepted items can be removed to leave room for newly arriving items. Once an item is rejected/removed it can not be considered again. We evaluate an online algorithm by comparing the resulting packing to an optimal packing that uses a knapsack of capacity one. Optimal online algorithms are derived for both the weighted case (items have arbitrary weights) and the un-weighted case (the weight of an item is equal to its size).