Using dual approximation algorithms for scheduling problems theoretical and practical results
Journal of the ACM (JACM)
There is no asymptotic PTAS for two-dimensional vector packing
Information Processing Letters
A 13/12 approximation algorithm for bin packing with extendable bins
Information Processing Letters
Approximation schemes for scheduling
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
On multi-dimensional packing problems
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Approximation algorithms for partitioning small items in unequal bins to minimize the total size
Proceedings of the third international conference on Graphs and optimization
Record Allocation for Minimizing Expected Retrieval Costs on Drum-Like Storage Devices
Journal of the ACM (JACM)
Algorithms for Scheduling Independent Tasks
Journal of the ACM (JACM)
Approximation algorithms for extensible bin packing
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
An efficient approximation scheme for the one-dimensional bin-packing problem
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
A polynomial-time approximation scheme for maximizing the minimum machine completion time
Operations Research Letters
| Hi-index | 0.00 |
We present a general framework for vector assignment problems. In such problems one aims at assigning n input vectors to m machines such that the value of a given target function is minimized. While previous approaches concentrated on simple target functions such as max-max, the general approach presented here enables us to design a PTAS for a wide class of target functions. In particular we are able to deal with non-monotone target functions and asymmetric settings where the cost functions per machine may be different for different machines. This is done by combining a graph-based technique and a new technique of preprocessing the input vectors.