Publication: Min-Max-Min Optimization with Smooth and Strongly Convex Objectives
Min-Max-Min Optimization with Smooth and Strongly Convex Objectives
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Lamperski, J., Prokopyev, O. A., & Wrabetz, L. G. (2023). Min-Max-Min Optimization with Smooth and Strongly Convex Objectives. SIAM Journal on Optimization, 33(3), 2435–2456. https://doi.org/10.1137/22m1489940
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We consider min-max-min optimization with smooth and strongly convex objectives. Our motivation for studying this class of problems stems from its connection to the (k) -center problem and the growing literature on min-max-min robust optimization. In particular, the considered class of problems nontrivially generalizes the Euclidean (k) -center problem in the sense that distances in this more general setting do not necessarily satisfy metric properties. We present a (9 \kappa) -approximation algorithm (where (\kappa) is the ma
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- Lamperski, Jourdain
- Wrabetz, Luca G
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Lamperski, J., Prokopyev, O. A., & Wrabetz, L. G. (2023). Min-Max-Min Optimization with Smooth and Strongly Convex Objectives. SIAM Journal on Optimization, 33(3), 2435–2456. https://doi.org/10.1137/22m1489940
