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Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning.
Portfolio selection
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Self-improving reactive agents based on reinforcement learning, planning and teaching
L.-J. Lin · 1992
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Portfolio construction through mixed-integer programming at grantham, mayo, van otterloo and company
D. Bertsimas, C. Darnell, and R. Soucy · 1999
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Actor-critic algorithms
V. R. Konda and J. N. Tsitsiklis · 1999
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Arriving on time
Y. Y. Fan, R. E. Kalaba, and J. E. Moore · 2005
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Numerical Optimization
J. Nocedal and S. J. Wright · 2006
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SCIP: Solving constraint integer programs
T. Achterberg · 2009
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The offset tree for learning with partial labels
A. Beygelzimer and J. Langford · 2009
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Portfolio selection with higher moments
C. R. Harvey, J. C. Liechty, M. W. Liechty, and P. Muller · 2010
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Non-convex mixed-integer nonlinear programming: A survey
S. Burer and A. N. Letchford · 2012
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Generic methods for optimization-based modeling
J. Domke · 2012
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Practical route planning under delay uncertainty: Stochastic shortest path queries
S. Lim, C. Sommer, E. Nikolova, and D. Rus · 2012
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Mixed-integer nonlinear optimization
P. Belotti, C. Kirches, S. Leyffer, J. Linderoth, J. Luedtke, and A. Mahajan · 2013
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CVXPY: A Python-embedded modeling language for convex optimization
S. Diamond and S. Boyd · 2016
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S. Gould, B. Fernando, A. Cherian, P. Anderson, R. S. Cruz, and E. Guo · 2016
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Optnet: Differentiable optimization as a layer in neural networks
B. Amos and J. Z. Kolter · 2017
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Task-based end-to-end model learning in stochastic optimization
P. Donti, B. Amos, and J. Z. Kolter · 2017
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Smart “predict, then optimize”
A. N. Elmachtoub and P. Grigas · 2017
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Learning combinatorial optimization algorithms over graphs
E. Khalil, H. Dai, Y. Zhang, B. Dilkina, and L. Song · 2017
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BARON 21.1.13: Global Optimization of Mixed-Integer Nonlinear Programs, User’s Manual , 2017
N. V. Sahinidis · 2017
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Combinatorial Optimization: Theory and Algorithms
B. Korte and J. Vygen · 2018
Differentiation of blackbox combinatorial solvers
M. V. Pogančic̀, A. Paulus, V. Musil, G. Martius, and M. Rolinek · 2020
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Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization
B. Wilder, B. Dilkina, and M. Tambe · 2020
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Implicit mle: backpropagating through discrete exponential family distributions
M. Niepert, P. Minervini, and L. Franceschi · 2021
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Comboptnet: Fit the right np-hard problem by learning integer programming constraints
A. Paulus, M. Rolínek, V. Musil, B. Amos, and G. Martius · 2021
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Learning optimization proxies for large-scale security-constrained economic dispatch
W. Chen, S. Park, M. Tanneau, and P. Van Hentenryck · 2022
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Differentiable convex optimization layers
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter · 2019
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Deep declarative networks: A new hope
S. Gould, R. Hartley, and D. Campbell · 2019
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Gurobi optimizer reference manual, 2019
Gurobi · 2019
Cited alongside, same era.
Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
P.-W. Wang, P. Donti, B. Wilder, and Z. Kolter · 2019
Cited alongside, same era.
End to end learning and optimization on graphs
B. Wilder, E. Ewing, B. Dilkina, and M. Tambe · 2019
Cited alongside, same era.
Dynamic programming for Predict+Optimise
E. Demirović, P. J. Stuckey, J. Bailey, J. Chan, C. Leckie, K. Ramamohanarao, and T. Guns · 2020
Cited alongside, same era.
A. Ferber, T. Huang, D. Zha, M. Schubert, B. Steiner, B. Dilkina, and Y. Tian · 2022
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Branch & learn for recursively and iteratively solvable problems in predict+optimize
X. Hu, J. C. Lee, J. H. Lee, and A. Z. Zhong · 2022
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Directed regression
Y.-h. Kao, B. Roy, and X. Yan · 2022
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Decision-focused learning: Through the lens of learning to rank
J. Mandi, V. Bucarey, M. M. K. Tchomba, and T. Guns · 2022
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Wiki various end-of-day data, 2022
Quandl · 2022
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Decision-focused learning without decision-making: Learning locally optimized decision losses
S. Shah, K. Wang, B. Wilder, A. Perrault, and M. Tambe · 2022
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Pyepo: A pytorch-based end-to-end predict-then-optimize library for linear and integer programming
B. Tang and E. B. Khalil · 2022
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Bome! bilevel optimization made easy: A simple first-order approach
M. Ye, B. Liu, S. Wright, P. Stone, and Q. Liu · 2022
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Reinforcement learning from optimization proxy for ride-hailing vehicle relocation
E. Yuan, W. Chen, and P. Van Hentenryck · 2022
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