Randomized Minmax Regret for Combinatorial Optimization Under Uncertainty

Randomized Minmax Regret for Combinatorial Optimization Under Uncertainty

The minmax regret problem for combinatorial optimization under uncertainty can be viewed as a zero-sum game played between an optimizing player and an adversary, where the optimizing player selects a solution and the adversary selects costs with the intention of maximizing the regret of the player....

Full description

Bibliographic Details
Main Authors: Chin, Sang (Author), Mastin, Dana A. (Contributor), Jaillet, Patrick (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor), Massachusetts Institute of Technology. Operations Research Center (Contributor)
Format: Article
Language:English
Published: Springer-Verlag, 2017-08-17T15:20:47Z.
Subjects:
Article
Online Access:Get fulltext

Internet

Get fulltext

Similar Items