hanxiongwei/L2O-Solver-Papers

Paper Collection of Learning-to-Optimization and Solvers

Contributed by Mingxuan Yuan, Huiling Zhen, Qiquan Shi, Xijun Li, Fangzhou Zhu, Zeren Huang, Xiongwei Han.

Organization: Huawei Technologies Noah's Ark Lab(华为诺亚方舟实验室).

Content

1. Tutorial and Books

2. Review Papers

3. Research Papers

 3.1. Production Planning

 3.2. Decomposition

 3.3. Simplex Method

 3.4. RL for Combinatorial Optimization

Tutorial and Books

• Maros, I., 2012. Computational techniques of the simplex method (Vol. 61). Springer Science & Business Media.
• Conejo, A.J., Castillo, E., Minguez, R. and Garcia-Bertrand, R., 2006. Decomposition techniques in mathematical programming: engineering and science applications. Springer Science & Business Media.

Review Papers

• Bengio, Y., Lodi, A. and Prouvost, A., 2018. Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon. arXiv preprint arXiv:1811.06128.

Research Papers

Production Planning

• Schewe, L., Schmidt, M. and Weninger, D., 2020. A decomposition heuristic for mixed-integer supply chain problems. Operations Research Letters.
• Degbotse, A., Denton, B.T., Fordyce, K., Milne, R.J., Orzell, R. and Wang, C.T., 2013. IBM blends heuristics and optimization to plan its semiconductor supply chain. Interfaces, 43(2), pp.130-141.
• Denton, B.T., Forrest, J. and Milne, R.J., 2006. IBM solves a mixed-integer program to optimize its semiconductor supply chain. Interfaces, 36(5), pp.386-399.

Decomposition

• Haeffele, B.D. and Vidal, R., 2019. Structured low-rank matrix factorization: Global optimality, algorithms, and applications. IEEE transactions on pattern analysis and machine intelligence.
• Lee, N. and Cichocki, A., 2015. Regularized Computation of Approximate Pseudoinverse of Matrices Using Low-Rank Tensor Train Decompositions. arXiv preprint arXiv:1506.01959.

Simplex Method

• Spielman DA, Teng SH. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. Journal of the ACM (JACM). 2004 May 1;51(3):385-463.
• De Farias, D.P. and Van Roy, B., 2004. On constraint sampling in the linear programming approach to approximate dynamic programming. Mathematics of operations research, 29(3), pp.462-478.

RL for Combinatorial Optimization

• Oriol Vinyals, Meire Fortunato, Navdeep Jaitly: Pointer Networks. NIPS 2015: 2692-2700.
• Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio: Neural Combinatorial Optimization with Reinforcement Learning. ICLR (Workshop) 2017.
• N. MohammadReza, O. Afshin, V. S. Lawrence and T. Martin, "Deep Reinforcement Learning for Solving the Vehicle Routing Problem," CoRR, 13 Aug 2018.
• E. Khalil, H. Dai, Y. Zhang, B. Dilkina and L. Song, "Learning Combinatorial Optimization Algorithms over Graphs," in Advances in Neural Information Processing Systems 30, 2017.
• J. Jiechuan, D. Chen and L. Zongqing, "Graph Convolutional Reinforcement Learning for Multi-agent Cooperation," in arxXiv, 2018.
• Wouter Kool, Herke van Hoof, Max Welling: Attention, Learn to Solve Routing Problems! ICLR (Poster) 2019.
• Yunhao Tang et.al: Reinforcement Learning for Integer Programming: Learning to Cut. CoRR abs/1906.04859 (2019).