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Bilevel optimization has recently attracted growing interests due to its wide applications in modern machine learning problems.
Mathematical programs with optimization problems in the constraints
Jerome Bracken and James T McGill · 1973
Earlier work this paper cites.
A solution method for the static constrained stackelberg problem via penalty method
Eitaro Aiyoshi and Kiyotaka Shimizu · 1984
Earlier work this paper cites.
Algorithms for nonlinear bilevel mathematical programs
Thomas Arthur Edmunds and Jonathan F Bard · 1991
Earlier work this paper cites.
Global optimization of concave functions subject to quadratic constraints: an application in nonlinear bilevel programming
Faiz A Al-Khayyal, Reiner Horst, and Panos M Pardalos · 1992
Earlier work this paper cites.
New branch-and-bound rules for linear bilevel programming
Pierre Hansen, Brigitte Jaumard, and Gilles Savard · 1992
Earlier work this paper cites.
Some bounds on the complexity of gradients, jacobians, and hessians
Andreas Griewank · 1993
Earlier work this paper cites.
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Earlier work this paper cites.
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Yurii Nesterov · 2003
Earlier work this paper cites.
Convex Optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Chuan-sheng Foo, Chuong B Do, and Andrew Y Ng · 2008
Earlier work this paper cites.
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Gregory M Moore · 2010
Earlier work this paper cites.
Generic methods for optimization-based modeling
Justin Domke · 2012
Earlier work this paper cites.
Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
Earlier work this paper cites.
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
Saeed Ghadimi and Guanghui Lan · 2016
Earlier work this paper cites.
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Fabian Pedregosa · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Lectures on Convex Optimization , volume 137
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