运筹学学报 ›› 2023, Vol. 27 ›› Issue (4): 153-165.doi: 10.15960/j.cnki.issn.1007-6093.2023.04.008
收稿日期:
2023-04-30
出版日期:
2023-12-15
发布日期:
2023-12-07
通讯作者:
王晓
E-mail:wangx07@pcl.ac.cn
作者简介:
王晓, E-mail: wangx07@pcl.ac.cn基金资助:
Received:
2023-04-30
Online:
2023-12-15
Published:
2023-12-07
Contact:
Xiao WANG
E-mail:wangx07@pcl.ac.cn
摘要:
在人工智能、科学计算等领域,众多应用驱动的数学优化模型因依赖于庞大的数据集和/或不确定的信息而呈现出随机性、且伴有复杂非凸算子约束。于是精确计算模型中的函数信息往往代价高昂,同时非凸约束的存在也给模型求解和算法分析带来极大的挑战。近年来,结合模型的结构、利用函数的随机近似信息来设计、分析非凸约束优化算法开始引起关注。目前主流的求解非凸约束优化的随机近似算法主要分为三类:基于随机近似的罚方法、邻近点算法和随机序列二次规划算法。本文对这几类算法的研究进展进行梳理和总结,简要地介绍相关算法的设计思想和基本的理论性质,如渐近收敛性理论、复杂度理论等。
中图分类号:
王晓. 非凸约束优化的随机近似算法[J]. 运筹学学报, 2023, 27(4): 153-165.
Xiao WANG. Stochastic approximation methods for nonconvex constrained optimization[J]. Operations Research Transactions, 2023, 27(4): 153-165.
1 | Ibaraki T , Katoh N . Resource Allocation Problems: Algorithmic Approaches[M]. Cambridge: MIT press, 1988. |
2 | Lan G . First-order and Stochastic Optimization Methods for Machine Learning[M]. Berlin: Springer, 2020. |
3 | Bertsekas D . Network Optimization: Continuous and Discrete Models[M]. Belmont: Athena Scientific, 1998. |
4 |
Summers T , Warrington J , Morari M , et al. Stochastic optimal power flow based on conditional value at risk and distributional robustness[J]. International Journal of Electrical Power and Energy Systems, 2015, 72, 116- 125.
doi: 10.1016/j.ijepes.2015.02.024 |
5 | Shapiro A , Dentcheva D , Ruszczyński A . Lectures on Stochastic Programming: Modeling and Theory[M]. Philadelphia: Society for Industrial and Applied Mathematics, 2021. |
6 | Ravi S, Dinh T, Lokhande V, et al. Explicitly imposing constraints in deep networks via conditional gradients gives improved generalization and faster convergence[C]//Proceedings of the Thirty-Third AAAI, 2019: 4772-4779. |
7 | Nandwani Y, Pathak A, Singla P. A primal-dual formulation for deep learning with constraints[C]//Proceedings of Neural Information Processing Systems, 2019: 12157-12168. |
8 | Roy S K, Mhammedi Z, Harandi M. Geometry aware constrained optimization techniques for deep learning[C]//Proceedings of Computer Vision and Pattern Recognition, 2018: 4460-4469. |
9 | Chen C, Tung F, Vedula N, et al. Constraint-aware deep neural network compression[C]//Proceedings of the European Conference on Computer Vision, 2018: 400-415. |
10 |
Fablet R , Chapron B , Drumetz L , et al. Learning variational data assimilation models and solvers[J]. Journal of Advances in Modeling Earth Systems, 2021, 13 (10): e2021MS002572.
doi: 10.1029/2021MS002572 |
11 | Nocedal J , Wright S . Numerical Optimization[M]. New York: Springer, 2006. |
12 | 王奇超, 文再文, 蓝光辉, 等. 优化算法的复杂度分析[J]. 中国科学: 数学, 2020, 50 (9): 1271- 1336. |
13 | Cartis C , Gould N I , Toint P L . On the evaluation complexity of composite function minimization with applications to nonconvex nonlinear programming[J]. SIAM Journal on Optimization, 2011, 4, 1721- 1739. |
14 | Cartis C , Gould N I , Toint P L . On the complexity of finding first-order critical points in constrained nonlinear optimization[J]. Mathematical Programming, 2014, 144 (1): 93- 106. |
15 |
Facchinei F , Kungurtsev V , Lampariello L , et al. Ghost penalties in nonconvex constrained optimization: Diminishing stepsizes and iteration complexity[J]. Mathematics of Operations Research, 2021, 46 (2): 595- 627.
doi: 10.1287/moor.2020.1079 |
16 |
Lan G , Zhou Z . Algorithms for stochastic optimization with function or expectation constraints[J]. Computational Optimization and Applications, 2020, 76 (2): 461- 498.
doi: 10.1007/s10589-020-00179-x |
17 | Wang R, Ding C. Robins-monro augmented lagrangian method for stochastic convex optimization[J]. 2022, arXiv: 2208.14019. |
18 |
Xu Y . Primal-dual stochastic gradient method for convex programs with many functional constraints[J]. SIAM Journal on Optimization, 2020, 30 (2): 1664- 1692.
doi: 10.1137/18M1229869 |
19 |
Zhang L , Zhang Y , Xiao X , et al. Stochastic approximation proximal method of multipliers for convex stochastic programming[J]. Mathematics of Operations Research, 2023, 48 (1): 177- 193.
doi: 10.1287/moor.2022.1257 |
20 |
Zhang L , Zhang Y , Wu J , et al. Solving stochastic optimization with expectation constraints efficiently by a stochastic augmented lagrangian-type algorithm[J]. Informs Journal on Computing, 2022, 34 (6): 2989- 3006.
doi: 10.1287/ijoc.2022.1228 |
21 | Wang X , Ma S , Yuan Y . Penalty methods with stochastic approximation for stochastic nonlinear programming[J]. Mathematics of Computation, 2017, 86, 1793- 1820. |
22 | Li Z, Chen P, Liu S, et al. Stochastic inexact augmented lagrangian method for nonconvex expectation constrained optimization[J]. 2022, arXiv: 2212.09513. |
23 |
Wang X , Yuan Y . An augmented Lagrangian trust region method for equality constrained optimization[J]. Optimization Methods and Software, 2015, 30 (3): 559- 582.
doi: 10.1080/10556788.2014.940947 |
24 |
Wang X , Zhang H . An augmented Lagrangian affine scaling method for nonlinear programming[J]. Optimization Methods and Software, 2015, 30 (5): 934- 964.
doi: 10.1080/10556788.2015.1004332 |
25 |
Xu Y . First-order methods for constrained convex programming based on linearized augmented Lagrangian function[J]. INFORMS Journal on Optimization, 2021, 3 (1): 89- 117.
doi: 10.1287/ijoo.2019.0033 |
26 |
Jin L , Wang X . A stochastic primal-dual method for a class of nonconvex constrained optimization[J]. Computational Optimization and Applications, 2022, 83, 143- 180.
doi: 10.1007/s10589-022-00384-w |
27 |
Campi M C , Garatti S . A sampling-and-discarding approach to chance-constrained optimization: Feasibility and optimality[J]. Journal of Optimization and Theory Applications, 2011, 148 (2): 257- 280.
doi: 10.1007/s10957-010-9754-6 |
28 |
Seri R , Choirat C . Scenario approximation of robust and chance-constrained programs[J]. Journal of Optimization Theory and Applications, 2013, 158 (2): 590- 614.
doi: 10.1007/s10957-012-0230-3 |
29 | Jin L, Wang X. Stochastic nested primal-dual method for nonconvex constrained composition optimization[EB/OL]. (2022-08-14)[2023-04-19]. https://optimization-online.org/?p=19899. |
30 | Shi Q, Wang X, Wang H. A momemtum-based linearized augmented lagrangian method for nonconvex constrained stochastic optimization[EB/OL]. (2022-08-11)[2023-04-19]. https://optimization-online.org/?p=19870. |
31 | Boob D , Deng Q , Lan G . Stochastic first-order methods for convex and nonconvex functional constrained optimization[J]. Mathematical Programming, 2022, 197, 215- 279. |
32 | Ma R, Lin Q, Yang T. Quadratically regularized subgradient methods for weakly convex optimization with weakly convex constraints[C]//Proceedings of the 37th International Conference on Machine Learning, 2020: 6554-6564. |
33 | Boob D, Deng Q, Lan G. Level constrained first order methods for function constrained optimization[J]. 2022, arXiv: 2205.08011. |
34 |
Berahas A , Curtis F , Robinson D , et al. Sequential quadratic optimization for nonlinear equality constrained stochastic optimization[J]. SIAM Journal on Optimization, 2021, 31, 1352- 1379.
doi: 10.1137/20M1354556 |
35 | Curtis F, O'Neill M J, Robinson D. A stochastic sequential quadratic optimization algorithm for nonlinear equality constrained optimization with rank-deficient jacobians[J]. 2021, arXiv: 2106.13015. |
36 | Curtis F, O'Neill M J, Robinson D. Inexact sequential quadratic optimization for minimizing a stochastic objective function subject to deterministic nonlinear equality constraints[J]. 2021, arXiv: 2107.03512. |
37 |
Berahas A , Shi J , Yi Z , et al. Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction[J]. Computational Optimization and Applications, 2023, 86 (1): 79- 116.
doi: 10.1007/s10589-023-00483-2 |
38 | Na S , Anitescu M , Kolar M . An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians[J]. Mathematical Programming, 2022, 199, 721- 791. |
39 | Fang Y, Na S, Kolar M. Fully stochastic trust-region sequential quadratic programming for equality-constrained optimization problems[J]. 2022, arXiv: 2211.15943. |
40 | Na S, Anitescu M, Kolar M. Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming[J/OL]. (2023-03-02)[2023-04-19]. Mathematical Programming, DOI: 10.1007/s10107-023-01935-7. |
41 | Curtis F, O'Neill M J, Robinson D. Sequential quadratic optimization for stochastic optimization with deterministic nonlinear inequality and equality constraints[J]. 2023, arXiv: 2302.14790. |
42 | Curtis F, O'Neill M J, Robinson D. Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization[J]. 2022, arXiv: 2112.14799. |
43 | Berahas A, Xie M, Zhou B. A sequential quadratic programming method with high probability complexity bounds for nonlinear equality constrained stochastic optimization[J]. 2023, arXiv: 2301.00477. |
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