Heuristics for the 0–1 min-knapsack problem
Acta Cybernetica
Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
Average-case analysis of off-line and on-line knapsack problems
Proceedings of the sixth annual ACM-SIAM symposium on Discrete 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
Optimal Resource Augmentations for Online Knapsack
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
Online minimization knapsack problem
WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
Finite-State online algorithms and their automated competitive analysis
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Approximate minimization algorithms for the 0/1 Knapsack and Subset-Sum Problem
Operations Research Letters
FAW-AAIM'11 Proceedings of the 5th joint international frontiers in algorithmics, and 7th international conference on Algorithmic aspects in information and management
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The (offline) maximization (resp., minimization) knapsack problem is given a set of items with weights and sizes, and the capacity of a knapsack, to maximize (resp., minimize) the total weight of selected items under the constraint that the total size of the selected items is at most (resp., at least) the capacity of the knapsack. In this paper, we study online maximization and minimization knapsack problems with limited cuts, in which 1) items are given one by one over time, i.e., after a decision is made on the current item, the next one is given, 2) items are allowed to be cut at most k ( 驴 1) times, and 3) items are allowed to be removed from the knapsack.We obtain the following three results. For the maximization knapsack problem, we propose a (k + 1)/k-competitive online algorithm, and show that it is the best possible, i.e., no online algorithm can have a competitive ratio less than (k + 1)/k. For the minimization knapsack problem, we show that no online algorithm can have a constant competitive ratio. We extend the result (i) to the resource augmentation model, where an online algorithm is allowed to use a knapsack of capacity m ( 1), while the optimal algorithm uses a unit capacity knapsack.