A survey of non-gradient optimization methods in structural engineering. - Warren L. Hare; Julie Nutini; Solomon Tesfamariam (Adv. Eng. Softw., 2013)

A derivative-free approximate gradient sampling algorithm for finite minimax problems. - Warren L. Hare; Julie Nutini (Comput. Optim. Appl., 2013)

Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. - Julie Nutini; Mark Schmidt 001; Issam H. Laradji; Michael P. Friedlander; Hoyt A. Koepke (ICML, 2015)

Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition. - Hamed Karimi; Julie Nutini; Mark Schmidt 001 (ECML/PKDD (1), 2016)

Convergence Rates for Greedy Kaczmarz Algorithms, and Randomized Kaczmarz Rules Using the Orthogonality Graph. - Julie Nutini; Behrooz Sepehry; Issam H. Laradji; Mark Schmidt 001; Hoyt A. Koepke; Alim Virani (UAI, 2016)

Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph. - Julie Nutini; Behrooz Sepehry; Issam H. Laradji; Mark Schmidt 001; Hoyt A. Koepke; Alim Virani (CoRR, 2016)

Are we there yet? Manifold identification of gradient-related proximal methods. - Yifan Sun 001; Halyun Jeong; Julie Nutini; Mark Schmidt 001 (AISTATS, 2019)

"Active-set complexity" of proximal gradient: How long does it take to find the sparsity pattern? - Julie Nutini; Mark Schmidt 001; Warren L. Hare (Optim. Lett., 2019)

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence. - Julie Nutini; Issam H. Laradji; Mark Schmidt 001 (J. Mach. Learn. Res., 2022)