Computing Partitions with Applications to the Knapsack Problem
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
A TcS2 = 0 (2n) time/space tradeoff for certain NP-complete problems
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Time-memory-processor trade-offs
IEEE Transactions on Information Theory
A parallel two-list algorithm for the knapsack problem
Parallel Computing
An Efficient Parallel Algorithm for Solving the Knapsack Problem on the Hypercube
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
An efficient parallel algorithm for solving the Knapsack problem on hypercubes
Journal of Parallel and Distributed Computing
A parallel O(n27n/8) time-memory-processor tradeoff for Knapsack-like problems
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
A note on developing optimal and scalable parallel two-list algorithms
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Proceedings of Programming Models and Applications on Multicores and Manycores
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A time-memory-processor tradeoff for the knapsack problem is proposed. While an exhaustive search over all possible solutions of an n-component knapsack requires T = 0(2n) running time, our parallel algorithm solves the problem in O(2n/2) operations and requires only 0(2n/6) processors and memory cells. It is an improvement over previous time-memory-processor tradeoffs, being the only one which outperforms the CmCs = 2" curve. Cm is the cost of the machine, i.e., the number of its processors and memory cells, and C, is the cost per solution, which is the product of the machine cost by the running time.