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Optimizing nonconvex (NCVX) problems, especially nonsmooth and constrained ones, is an essential part of machine learning.
SDPT3—a Matlab software package for semidefinite programming, Version 1.3
Kim-Chuan Toh, Michael J. Todd, and Reha H. Tütüncü · 1999
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YALMIP : a toolbox for modeling and optimization in MATLAB
Johan Lofberg · 2004
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Large-Scale Nonlinear Optimization , volume 83 of Nonconvex Optimization and Its Applications
Gianni Pillo and Massimo Roma · 2006
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On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
Andreas Wächter and Lorenz T. Biegler · 2006
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CVX: Matlab software for disciplined convex programming
Michael Grant, Stephen Boyd, and Yinyu Ye · 2008
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V12. 1: User’s manual for CPLEX
IBM ILOG Cplex · 2009
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Templates for convex cone problems with applications to sparse signal recovery
Stephen R. Becker, Emmanuel J. Candès, and Michael C. Grant · 2011
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2011
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Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten, Eibe Frank, and Mark A. Hall · 2011
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Optimization for Machine Learning
Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright · 2012
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Manopt, a Matlab toolbox for optimization on manifolds
Nicolas Boumal, Bamdev Mishra, Pierre-Antoine Absil, and Rodolphe Sepulchre · 2014
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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The AMPL modeling language: An aid to formulating and solving optimization problems
David M. Gay · 2015
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CVXPY: A Python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd · 2016
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MLlib: Machine learning in Apache Spark
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, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Optimization for deep learning: theory and algorithms
Ruoyu Sun · 2019
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Conic optimization for control, energy systems, and machine learning: Applications and algorithms
Richard Y. Zhang, Cédric Josz, and Somayeh Sojoudi · 2019
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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What is local optimality in nonconvex-nonconcave minimax optimization?
Chi Jin, Praneeth Netrapalli, and Michael Jordan · 2020
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Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, and Ameet Talwalkar · 2016
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Pymanopt: A Python toolbox for optimization on manifolds using automatic differentiation
James Townsend, Niklas Koep, and Sebastian Weichwald · 2016
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A BFGS-SQP method for nonsmooth, nonconvex, constrained optimization and its evaluation using relative minimization profiles
Frank E Curtis, Tim Mitchell, and Michael L Overton · 2017
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Subgradient descent learns orthogonal dictionaries
Yu Bai, Qijia Jiang, and Ju Sun · 2018
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McTorch, a manifold optimization library for deep learning
Mayank Meghwanshi, Pratik Jawanpuria, Anoop Kunchukuttan, Hiroyuki Kasai, and Bamdev Mishra · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
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GENO – GENeric Optimization for classical machine learning
Sören Laue, Matthias Mitterreiter, and Joachim Giesen · 2019
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Geoopt: Riemannian optimization in PyTorch
Max Kochurov, Rasul Karimov, and Serge Kozlukov · 2020
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Perceptual adversarial robustness: Defense against unseen threat models
Cassidy Laidlaw, Sahil Singla, and Soheil Feizi · 2020
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Geomstats: A Python package for Riemannian geometry in machine learning
Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, and Xavier Pennec · 2020
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OSQP: an operator splitting solver for quadratic programs
Bartolomeo Stellato, Goran Banjac, Paul Goulart, Alberto Bemporad, and Stephen Boyd · 2020
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The ensmallen library for flexible numerical optimization
Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, and Conrad Sanderson · 2021
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Gurobi Optimizer Reference Manual, 2021
Gurobi Optimization, LLC · 2021
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