Robust optimization in the presence of uncertainty: A generic approach
作者：
Highlights：
• A new approach to robust optimization in presence of uncertainty ROPU is proposed.
• ROPU takes two typical instances and doesn't assume any special noise model.
• ROPU measures task specific similarity of instances, i.e., its input relevance.
• The similarity measure detects if given instances are not similar or too noisy.
• Instance similarity favors good localization (!) of solutions rather than costs.
摘要
•A new approach to robust optimization in presence of uncertainty ROPU is proposed.•ROPU takes two typical instances and doesn't assume any special noise model.•ROPU measures task specific similarity of instances, i.e., its input relevance.•The similarity measure detects if given instances are not similar or too noisy.•Instance similarity favors good localization (!) of solutions rather than costs.
论文关键词：Optimization,Uncertainty,Noise,Robustness,Instance similarity
论文评审过程：Received 24 April 2017, Revised 25 October 2017, Accepted 27 October 2017, Available online 8 November 2017, Version of Record 14 March 2018.
论文官网地址：https://doi.org/10.1016/j.jcss.2017.10.004
