A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principle
提出了一个广义的“原始最坏等于对偶最优”原则，建立了半无限原始最坏和非凸对偶最优形式之间的强对偶性，为鲁棒和分布鲁棒非线性优化问题提供了替代表征，无需依赖抽象半无限对偶理论。
A Primal-Worst-Equals-Dual-Best Perspective on Robust and Distributionally Robust Optimization In the paper “A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principle,” Jianzhe Zhen, Daniel Kuhn, and Wolfram Wiesemann develop a generalized “primal-worst-equals-dual-best” principle that establishes strong duality between semi-infinite primal worst and nonconvex dual best formulations of robust and distributionally robust nonlinear optimization problems. Their theory offers an alternative characterization of (distributionally) robust optimization problems that bypasses the need to mobilize the machinery of abstract semi-infinite duality theory. The paper will be of interest to researchers and practitioners in the field of optimization under uncertainty.