Convex Model Databases—Solving Real-World OR Problems

Abstract

Many real-world optimization problems deal with uncertain data and several modelling approaches including robust optimization (convex uncertainty sets) and stochastic programming (probabilistic uncertainty sets) are used to handle the uncertainty. These approaches lead to more complex models, however, advances in the field of convex optimization Boyd and Vandenberghe (Convex Optimization, Cambridge University Press, [1]) have made many complex problems quite tractable and applicable in many branches of information processing, including operations research, data science, signal processing, optimal control, and more. Though much research has been undertaken to improve the problem solving mechanisms, relatively little has been done in the area of data representation—design of appropriate databases for storing and querying these models and using these models for data analysis, and this is the focus of our work.