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Solving optimization problems is the key to decision making in many real-life analytics applications.
Differentiating through a cone program
Akshay Agrawal, Shane Barratt, Stephen Boyd, Enzo Busseti, and Walaa M Moursi · 1904
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Lsqr: An algorithm for sparse linear equations and sparse least squares
Christopher C Paige and Michael A Saunders · 1982
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A simplified homogeneous and self-dual linear programming algorithm and its implementation
Xiaojie Xu, Pi-Fang Hung, and Yinyu Ye · 1996
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Using a financial training criterion rather than a prediction criterion
Yoshua Bengio · 1997
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Introduction to linear programming
Dimitris Bertsimas and John Tsitsiklis · 1997
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Primal-dual interior-point methods , volume 54
Stephen J Wright · 1997
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Combinatorial optimization: algorithms and complexity
Christos H Papadimitriou and Kenneth Steiglitz · 1998
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The mosek interior point optimizer for linear programming: An implementation of the homogeneous algorithm
Erling D. Andersen and Knud D. Andersen · 2000
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming
Hugo Morais, Péter Kádár, Pedro Faria, Zita A Vale, and HM Khodr · 2010
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Properties of energy-price forecasts for scheduling
Georgiana Ifrim, Barry O’Sullivan, and Helmut Simonis · 2012
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Learning to discover social circles in ego networks
Jure Leskovec and Julian J Mcauley · 2012
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Training deep and recurrent networks with hessian-free optimization
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Linear programming computation
PAN Ping-Qi · 2014
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A novel machine learning model for estimation of sale prices of real estate units
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Optnet: Differentiable optimization as a layer in neural networks
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An investigation into prediction+ optimisation for the knapsack problem
Emir Demirović, Peter J Stuckey, James Bailey, Jeffrey Chan, Chris Leckie, Kotagiri Ramamohanarao, and Tias Guns · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya Donti, Bryan Wilder, and Zico Kolter · 2019
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Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization
Bryan Wilder, Bistra Dilkina, and Milind Tambe · 2019
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Dynamic programming for predict+ optimise
Emir Demirović, Peter J Stuckey, James Bailey, Jeffrey Chan, Christopher Leckie, Kotagiri Ramamohanarao, and Tias Guns · 2020
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Differentiable convex optimization layers
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CSPLib problem 059: Energy-cost aware scheduling
Helmut Simonis, Barry O’Sullivan, Deepak Mehta, Barry Hurley, and Milan De Cauwer
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Mipaal: Mixed integer program as a layer
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Gurobi optimizer reference manual, 2020
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Smart predict-and-optimize for hard combinatorial optimization problems
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Differentiation of blackbox combinatorial solvers
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