Bibliography

BV04

Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004. URL: https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf.

CJG+19

Andrew Cotter, Heinrich Jiang, Maya Gupta, Serena Wang, Taman Narayan, Seungil You, and Karthik Sridharan. Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. Journal of Machine Learning Research, 20(172):1–59, 2019. URL: http://jmlr.org/papers/v20/18-616.html.

GBV+19

Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, and Simon Lacoste-Julien. A Variational Inequality Perspective on Generative Adversarial Networks. In ICLR. 2019. URL: https://openreview.net/forum?id=r1laEnA5Ym.

Kor76

Galina M Korpelevich. The extragradient method for finding saddle points and other problems. Matecon, 1976. URL: https://cs.uwaterloo.ca/~y328yu/classics/extragrad.pdf.

LJJ20

Tianyi Lin, Chi Jin, and Michael Jordan. On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. In ICML. 2020. URL: https://proceedings.mlr.press/v119/lin20a.html.

NCZ+20

Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, and Wenshuo Guo. Approximate heavily-constrained learning with lagrange multiplier models. In NeurIPS. 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/62db9e3397c76207a687c360e0243317-Abstract.html.

RKK18

Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar. On the convergence of adam and beyondg. In ICLR. 2018. URL: https://openreview.net/forum?id=r1laEnA5Ym.

SMDH13

Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. On the importance of initialization and momentum in deep learning. In ICML. 2013. URL: https://proceedings.mlr.press/v28/sutskever13.html.

vN28

John von Neumann. Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, 100(1):295–320, 1928. URL: https://cs.uwaterloo.ca/~y328yu/classics/vonNeumann.pdf.