Funder
Peng Cheng Laboratory
national natural science foundation of china
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computational Mathematics,Control and Optimization
Reference43 articles.
1. Boob, D., Deng, Q., Lan, G.: Stochastic first-order methods for convex and nonconvex functional constrained optimization. Math. Program. (2022)
2. Campi, M.C., Garatti, S.: A sampling-and-discarding approach to chance-constrained optimization: Feasibility and optimality. J. Optim. Theory App. 148(2), 257–280 (2011)
3. Defazio, A., Bach, F., Lacoste-Julien, S.: SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. In: 28th NIPS, vol. 27 (2014)
4. Ghadimi, S.: Conditional gradient type methods for composite nonlinear and stochastic optimization. Math. Program. 173(1–2), 431–464 (2019)
5. Haines, S., Loeppky, J., Tseng, P., Wang, X.: Convex relaxations of the weighted maxmin dispersion problem. SIAM J. Optim. 23(4), 2264–2294 (2013)
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