Machine speed scaling by adapting methods for convex optimization with submodular constraints
- Submitting institution
-
University of Greenwich
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 17696
- Type
- D - Journal article
- DOI
-
10.1287/ijoc.2017.0758
- Title of journal
- INFORMS Journal on Computing
- Article number
- -
- First page
- 724
- Volume
- 29
- Issue
- 4
- ISSN
- 1091-9856
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a new methodology for the speed-scaling problem based on its link to scheduling with controllable processing times and submodular optimization (SO). One major objective for this work was to make SO results more accessible to practitioners and researchers in scheduling. The new methodology results in faster algorithms for traditional speed-scaling models, characterised by a common speed/energy function, and the general version of the single-machine case is solvable by the new technique in O(n2) time. This output was part of an international collaboration with Tokyo Institute of Technology, and the UK contribution was funded through EPSRC project EP/J019755.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
