Optimal Online Data-Driven Optimization with Multiple Time-Varying Non-Convex Objectives
Technical Report,11 Apr 2018,11 Oct 2019
Duke University Durham United States
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This report presents the development of optimization research methods and concepts for time-varying, real-world situations, which include factoring in more realistic assumptions on the dynamical systems underlying the objective functions and constraints, nonconvexity of objectives, reasonability of currently existing performance measures, time-varying constraints, and situations where the true objective function is unknown. Two scenarios were considered for training of statistical models for optimization 1. where changes in the objective function were smooth, and 2. where the dynamical system of objective functions was adversarial non-predictable. The problems considered in the body of work span a large number of subfields of optimization and are summarized in the remainder of the report.
- Operations Research