[257] İ. Muter, Ş. İ. Birbil, and K. Bülbül. Simultaneous column-and-row generation for large-scale linear programs with column-dependent-rows. *Mathematical Programming*, 142(1):47–82, 2013.

[258] D. Naddef. Polyhedral theory and branch-and-cut algorithms for the symmetric tsp. In G. Gutin and A. P. Punnen, editors, *The Traveling Salesman Problem and Its Variations*, pages 29–116. Springer US, Boston, MA, 2007.

[259] J. Naoum-Sawaya. Recursive central rounding for mixed integer programs. *Computers & Operations Research*, 43:191–200, 2014.

[260] J. Naoum-Sawaya and S. Elhedhli. An interior-point Benders based branch-and-cut algorithm for mixed integer programs. *Annals of Operations Research*, 210(1):33–55, 2013.

[261] G. L. Nemhauser and L. A. Wolsey. A recursive procedure to generate all cuts for 0–1 mixed integer programs. *Mathematical Programming*, 46(1):379–390, 1990.

[262] J. Ostrowski, J. Linderoth, F. Rossi, and S. Smriglio. Orbital branching. *Mathematical Programming*, 126(1):147–178, 2011.

[263] M. Padberg and G. Rinaldi. Facet identification for the symmetric traveling salesman polytope. *Mathematical Programming*, 47:219–257, 1990.

[264] M. Padberg and G. Rinaldi. A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems. *SIAM review*, 33(1):60–100, 1991.

[265] M. Padberg and T. Sung. An analytical comparison of different formulations of the travelling salesman problem. *Mathematical Programming*, 52:315–357, 1991.

[266] M. W. Padberg, T. J. Van Roy, and L. A. Wolsey. Valid linear inequalities for fixed charge problems. *Operations Research*, 33(4):842–861, 1985.

[267] N. Papadakos. Practical enhancements to the Magnanti–Wong method. *Operations Research Letters*, 36(4):444–449, 2008.

[268] M. B. Paulus, G. Zarpellon, A. Krause, L. Charlin, and C. J. Maddison. Learning To Cut By Looking Ahead: Imitation Learning for Cutting Plane Selection. In Chaudhuri, Kamalika, Jegelka, Stefanie, Song, Le, Szepesvari, Csaba, Niu, Gang, and Sabato, Sivan, editors, *Proceedings of the 39th International Conference on Machine Learning*, volume 162, pages 17584–17600. PMLR, 2022.

[269] D. Pecin, A. Pessoa, M. Poggi, and E. Uchoa. Improved branch-cut-and-price for capacitated vehicle routing. *Mathematical Programming Computation*, 9(1):61–100, 2017.

[270] A. Pessoa, R. Sadykov, E. Uchoa, and F. Vanderbeck. Automation and Combination of Linear-Programming Based Stabilization Techniques in Column Generation. *INFORMS Journal on Computing*, 30(2):339–360, 2018.

[271] A. Pessoa, R. Sadykov, E. Uchoa, and F. Vanderbeck. A generic exact solver for vehicle routing and related problems. *Mathematical Programming*, 183(1):483–523, 2020.

[272] M. E. Pfetsch and T. Rehn. A computational comparison of symmetry handling methods for mixed integer programs. *Mathematical Programming Computation*, 11(1):37–93, 2019.

[273] T. Polzin and S. Vahdati Daneshmand. A comparison of Steiner tree relaxations. *Discrete Applied Mathematics*, 112(1):241–261, 2001.

[274] D. Porumbel and F. Clautiaux. Constraint aggregation in column generation models for resource-constrained covering problems. *INFORMS J. Comput.*, 29(1):170–184, 2017.

[275] D. C. Porumbel and G. Goncalves. Using dual feasible functions to construct fast lower bounds for routing and location problems. *Discrete Applied Mathematics*, 196:83–99, 2015.

[276] R. Rahmaniani, S. Ahmed, T. G. Crainic, M. Gendreau, and W. Rei. The Benders dual decomposition method. *Operations Research*, 68(3):878–895, 2020.

[277] R. Rahmaniani, T. G. Crainic, M. Gendreau, and W. Rei. The Benders decomposition algorithm: A literature review. *European Journal of Operational Research*, 259(3):801–817, 2017.

[278] R. Rahmaniani, T. G. Crainic, M. Gendreau, and W. Rei. Accelerating the Benders decomposition method: Application to stochastic network design problems. *SIAM Journal on Optimization*, 28(1):875–903, 2018.
[279] T. Ralphs, Y. Shinano, T. Berthold, and T. Koch. Parallel solvers for mixed integer linear programming. Technical report, Zuse Institute Berlin, 2016.

[280] C. Ramirez-Pico, I. Ljubić, and E. Moreno. Benders adaptive-cuts method for two-stage stochastic programs. *Transportation Science*, 57(5):1252–1275, 2023.

[281] C. Ramirez-Pico and E. Moreno. Generalized adaptive partition-based method for two-stage stochastic linear programs with fixed recourse. *Mathematical Programming*, 196:755–774, 2022.

[282] J. Renegar. A polynomial-time algorithm, based on Newton's method, for linear programming. *Mathematical programming*, 40(1):59–93, 1988.

[283] G. Righini and M. Salani. New dynamic programming algorithms for the resource constrained elementary shortest path problem. *Networks*, 51:155–170, 05 2008.

[284] E. Rothberg. An Evolutionary Algorithm for Polishing Mixed Integer Programming Solutions. *INFORMS Journal on Computing*, 19(4):534–541, 2007.

[285] L.-M. Rousseau, M. Gendreau, and D. Feillet. Interior point stabilization for column generation. *Operations Research Letters*, 35(5):660–668, 2007.

[286] D. M. Ryan and B. A. Foster. An integer programming approach to scheduling. In A. Wren, editor, *Computer scheduling of public transport urban passenger vehicle and crew scheduling*, pages 269–280. North-Holland Publishing Company, Amsterdam, 1981.

[287] A. Sabharwal, H. Samulowitz, and C. Reddy. Guiding Combinatorial Optimization with UCT. In N. Beldiceanu, N. Jussien, and É. Pinson, editors, *Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems*, pages 356–361, Berlin, Heidelberg, 2012. Springer.

[288] R. Sadykov. *Modern Branch-Cut-and-Price*. PhD thesis, Université de Bordeaux, 2019.

[289] R. Sadykov, E. Uchoa, and A. Pessoa. A Bucket Graph-Based Labeling Algorithm with Application to Vehicle Routing. *Transportation Science*, 55(1):4–28, 2021.

[290] R. Sadykov and F. Vanderbeck. Column generation for extended formulations. *EURO Journal on Computational Optimization*, 1(1):81–115, 2013.

[291] R. Sadykov, F. Vanderbeck, A. Pessoa, I. Tahiri, and E. Uchoa. Primal Heuristics for Branch and Price: The Assets of Diving Methods. *INFORMS Journal on Computing*, 31(2):251–267, 2019.

[292] A. Saken, E. Karlsson, S. J. Maher, and E. Rönnberg. Computational evaluation of cut-strengthening techniques in logic-based Benders' decomposition. *Operations Research Forum*, 4(3):62, 2023.

[293] H. Salemi and D. Davarnia. On the Structure of Decision Diagram-Representable Mixed-Integer Programs with Application to Unit Commitment. *Operations Research*, page opre.2022.2353, 2022.

[294] A. Schrijver. *Combinatorial Optimization*. Springer, 2002.

[295] SCIP. SCIP Optimization Suite, 2024. Accessed: 2024-06-30.

[296] K. Seo, S. Joung, C. Lee, and S. Park. A closest Benders cut selection scheme for accelerating the Benders decomposition algorithm. *INFORMS Journal on Computing*, 34(5):2804–2827, 2022.

[297] Y. Shen, Y. Sun, A. Eberhard, and X. Li. Learning primal heuristics for mixed integer programs. In *2021 International Joint Conference on Neural Networks (IJCNN)*, pages 1–8. IEEE, 2021.

[298] R. S. Shibasaki, M. Baiou, F. Barahona, P. Mahey, and M. C. de Souza. Lagrangian bounds for large-scale multicommodity network design: A comparison between Volume and Bundle methods. *International Transactions in Operational Research*, 28(1):296–326, 2021.

[299] Y. Shinano, S. Heinz, S. Vigerske, and M. Winkler. FiberSCIP—A Shared Memory Parallelization of SCIP. *INFORMS Journal on Computing*, 30(1):11–30, 2018.

[300] G. Song and R. Leus. Parallel machine scheduling under uncertainty: Models and exact algorithms. *INFORMS Journal on Computing*, 34(6):3059–3079, 2022.

[301] J. Song, r. lanka, Y. Yue, and B. Dilkina. A general large neighborhood search framework for solving integer linear programs. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, *Advances in Neural Information Processing Systems*, volume 33, pages 20012–20023. Curran Associates, Inc., 2020.
[302] J. Song, R. Lanka, A. Zhao, A. Bhatnagar, Y. Yue, and M. Ono. Learning to Search via Retrospective Imitation. https://arxiv.org/abs/1804.00846, 2019.

[303] J. Song, Y. Yue, B. Dilkina, et al. A general large neighborhood search framework for solving integer linear programs. *Advances in Neural Information Processing Systems*, 33:20012–20023, 2020.

[304] Y. Song and J. Luedtke. An adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse. *SIAM Journal on Optimization*, 25(3):1344–1367, 2015.

[305] S. Tahernejad, T. K. Ralphs, and S. T. DeNegre. A branch-and-cut algorithm for mixed integer bilevel lin-ear optimization problems and its implementation. *Mathematical Programming Computation*, 12(4):529–568, 2020.

[306] Y. Tang, S. Agrawal, and Y. Faenza. Reinforcement Learning for Integer Programming: Learning to Cut. In *Proceedings of the 37th International Conference on Machine Learning*, pages 9367–9376. PMLR, 2020.

[307] D. Thuerck, B. Sofranac, M. E. Pfetsch, and S. Pokutta. Learning cuts via enumeration oracles. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, editors, *Advances in Neural Information Processing Systems*, volume 36, pages 79108–79123. Curran Associates, Inc., 2023.

[308] S. Trukhanov, L. Ntaimo, and A. Schaefer. Adaptive multicut aggregation for two-stage stochastic linear programs with recourse. *European Journal of Operational Research*, 206(2):395–406, 2010.

[309] M. Turner, T. Berthold, M. Besançon, and T. Koch. Cutting Plane Selection with Analytic Centers and Multiregression. In A. A. Cire, editor, *Integration of Constraint Programming, Artificial Intelligence, and Operations Research*, pages 52–68, Cham, 2023. Springer Nature Switzerland.

[310] E. Uchoa and R. Sadykov. Kantorovich and Zalgaller (1951): The 0-th Column Generation Algorithm. https://optimization-online.org/2024/01/kantorovich-and-zalgaller-1951-the-0-th-column-generation-algorithm/, 2024.

[311] J. M. Valério de Carvalho. LP models for bin packing and cutting stock problems. *European Journal of Operational Research*, 141(2):253–273, 2002.

[312] R. M. Van Slyke and R. Wets. L-shaped linear programs with applications to optimal control and stochastic programming. *SIAM Journal on Applied Mathematics*, 17(4):638–663, 1969.

[313] F. Vanderbeck. On Dantzig-Wolfe Decomposition in Integer Programming and Ways to Perform Branching in a Branch-and-Price Algorithm. *Operations Research*, 48(1):111, 2000.

[314] F. Vanderbeck. Branching in branch-and-price: A generic scheme. *Mathematical Programming*, 130(2):249–294, 2011.

[315] F. Vanderbeck and M. W. P. Savelsbergh. A generic view of Dantzig–Wolfe decomposition in mixed integer programming. *Operations Research Letters*, 34(3):296–306, 2006.

[316] F. Vanderbeck and L. A. Wolsey. Reformulation and Decomposition of Integer Programs. In M. Jünger, T. M. Liebling, D. Naddef, G. L. Nemhauser, W. R. Pulleyblank, G. Reinelt, G. Rinaldi, and L. A. Wolsey, editors, *50 Years of Integer Programming 1958-2008: From the Early Years to the State-of-the-Art*, pages 431–502. Springer, Berlin, Heidelberg, 2010.

[317] J. Witzig and A. Gleixner. Conflict-driven heuristics for mixed integer programming. *INFORMS Journal on Computing*, 33(2):706–720, 2021.

[318] L. A. Wolsey and G. L. Nemhauser. *Integer and combinatorial optimization*. John Wiley & Sons, 2014.

[319] Y. Yang. DeLuxing: Deep Lagrangian Underestimate Fixing for Column-Generation-Based Exact Methods. *Optimization Online*, (24217), 2023.

[320] Y. Yang, N. Boland, B. Dilkina, and M. Savelsbergh. Learning generalized strong branching for set covering, set packing, and 0-1 knapsack problems. *European Journal of Operational Research*, 301(3):828–840, 2022.

[321] K. Yilmaz and N. Yorke-Smith. A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in *SCIP*. AI, 2(2):150–178, 2021.

[322] A. Zaghrouti, I. El Hallaoui, and F. Soumis. Improved integral simplex using decomposition for the set partitioning problem. *EURO Journal on Computational Optimization*, 6(2):185–206, 2018.
[323] G. Zakeri, A. B. Philpott, and D. M. Ryan. Inexact cuts in Benders decomposition. *SIAM Journal on Optimization*, 10(3):643–657, 2000.

[324] G. Zarpellon, J. Jo, A. Lodi, and Y. Bengio. Parameterizing branch-and-bound search trees to learn branching policies. In *Proceedings of the AAAI conference on artificial intelligence*, volume 35, pages 3931–3939, 2021.

[325] C. A. Zetina, I. Contreras, and J.-F. Cordeau. Exact algorithms based on Benders decomposition for multicommodity uncapacitated fixed-charge network design. *Computers & Operations Research*, 111:311–324, 2019.

[326] M. Zhang and S. Küçükyavuz. Finitely convergent decomposition algorithms for two-stage stochastic pure integer programs. *SIAM Journal of Optimization*, 24(4):1933–1951, 2014.