Data-Driven Robust Chance Constrained Problems: A Mixture Model Approach
Data-Driven Robust Chance Constrained Problems: A Mixture Model Approach is a scholarly work, published in 2018 in ''Journal of Optimization Theory and Applications''. The main subjects of the publication include Fuzzy differential equation, piecewise linear function, convex optimization, uncertainty quantification, mathematical optimization, biological robustness, theory of computation, piecewise function, regular polygon, robust optimization, set, and mathematics. The authors construct a data-driven mixture distribution-based uncertainty set from the perspective of simultaneously estimating higher-order moments.