New pseudopolynomial complexity bounds for the bounded and other integer knapsack related problems
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Publication:1038094
DOI10.1016/j.orl.2009.05.003zbMath1173.90523OpenAlexW1974761916MaRDI QIDQ1038094
Publication date: 17 November 2009
Published in: Operations Research Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.orl.2009.05.003
bounded knapsack problembounded multiple-choice knapsack problemmulticover problempseudopolynomial algorithms
Abstract computational complexity for mathematical programming problems (90C60) Combinatorial optimization (90C27)
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Cites Work
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- An O(n) algorithm for the linear multiple choice knapsack problem and related problems
- Cyclical scheduling and multi-shift scheduling: complexity and approximation algorithms
- Heuristic algorithms for the multiple knapsack problem
- The relationship between integer and real solutions of constrained convex programming
- Dynamic programming revisited: Improving knapsack algorithms
- A Strongly Polynomial Algorithm to Solve Combinatorial Linear Programs
- An O(n) algorithm for the multiple-choice knapsack linear program
- Applying Parallel Computation Algorithms in the Design of Serial Algorithms
- Sensitivity theorems in integer linear programming
- An Algorithm for Large Zero-One Knapsack Problems
- Slowing down sorting networks to obtain faster sorting algorithms
- The interval subset sum problem
- Linear Time Algorithms for Knapsack Problems with Bounded Weights
- Convex separable optimization is not much harder than linear optimization
- Budgeting with bounded multiple-choice constraints.