2019/11/20 by Fengqiao Luo, Sanjay Mehrotra, Luo, Fengqiao +1
Decision Sciences · Mathematics · #Advanced Optimization Algorithms Research #FOS: Mathematics #G.1.6 #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design #Risk and Portfolio Optimization
paper · pdf · doi:10.48550/arxiv.1911.08719
openalex publication_date 2019/11/20 · openalex created_date 2019/12/05 · openalex updated_date 2026/07/28
We investigate robust optimization problems defined for maximizing convex functions. For finite uncertainty set, we develop a geometric branch-and-bound algorithmic approach to solve this problem. The geometric branch-and-bound algorithm performs sequential piecewise-linear approximations of the convex objective, and solves linear programs to determine lower and upper bounds of nodes specified by the active linear pieces. Finite convergence of the algorithm to an ε-optimal solution is proved. Numerical results are used to discuss the performance of the developed algorithm. The algorithm developed in this paper can be used as an oracle in the cutting surface method for solving robust optimization problems with compact ambiguity sets.