Convex Optimization, Boyd, Stephen and Vandenberghe, Lieven, 2004 (Cambridge University Press) - Classic textbook on convex optimization, providing theoretical foundations and algorithms including projected gradient methods and projection operators.
Towards Deep Learning Models Resistant to Adversarial Attacks, Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu, 2018International Conference on Learning Representations (ICLR)DOI: 10.48550/arXiv.1706.06083 - Introduces the PGD attack as a strong adversarial attack method, illustrating a key application of projected gradient ascent in machine learning security.
Efficient Projections onto the L1-Ball for Learning, John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra, 2008Proceedings of the 25th International Conference on Machine Learning (ACM)DOI: 10.1145/1390156.1390191 - Provides efficient algorithms for projecting onto the L1-ball, a crucial component for L1-constrained optimization methods often used in sparse learning.