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math.OC2026★ 3 cited
Randomized Greedy Methods for Weak Submodular Sensor Selection with Robustness Considerations
Ege C. Kaya, Michael Hibbard, Takashi Tanaka +2
We study a pair of budget- and performance-constrained weak-submodular maximization problems. For computational efficiency, we explore the use of stochastic greedy algorithms which…
math.OC2025
Beyond Convexity: Proximal-Perturbed Lagrangian Methods for Efficient Functional Constrained Optimization
Sang Bin Moon, Jong Gwang Kim, Ashish Chandra +2
Non-convex functional constrained optimization problems have gained substantial attention in machine learning and data science, addressing broad requirements that typically go beyo…
math.OC2025
Optimization via First-Order Switching Methods: Skew-Symmetric Dynamics and Optimistic Discretization
Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi
Large-scale constrained optimization problems are at the core of many tasks in control, signal processing, and machine learning. Notably, problems with functional constraints arise…