First order methods beyond convexity and Lipschitz gradient continuity with applications to quadratic inverse problems J Bolte, S Sabach, M Teboulle, Y Vaisbourd SIAM Journal on Optimization 28 (3), 2131-2151, 2018 | 225 | 2018 |
Novel Proximal Gradient Methods for Nonnegative Matrix Factorization with Sparsity Constraints M Teboulle, Y Vaisbourd SIAM Journal on Imaging Sciences 13 (1), 381-421, 2020 | 28 | 2020 |
Globally Solving the Trust Region Subproblem Using Simple First-Order Methods A Beck, Y Vaisbourd SIAM Journal on Optimization 28 (3), 1951-1967, 2018 | 25 | 2018 |
The sparse principal component analysis problem: Optimality conditions and algorithms A Beck, Y Vaisbourd Journal of Optimization Theory and Applications 170, 119-143, 2016 | 25 | 2016 |
An elementary approach to tight worst case complexity analysis of gradient based methods M Teboulle, Y Vaisbourd Mathematical Programming 201, 63–96, 2022 | 16 | 2022 |
Rate of convergence analysis of dual-based variables decomposition methods for strongly convex problems A Beck, L Tetruashvili, Y Vaisbourd, A Shemtov Operations Research Letters 44 (1), 61-66, 2016 | 2 | 2016 |
Maximum Entropy on the Mean and the Cram\'er Rate Function in Statistical Estimation and Inverse Problems: Properties, Models, and Algorithms Y Vaisbourd, R Choksi, A Goodwin, T Hoheisel, CB Schönlieb arXiv preprint arXiv:2211.05205, 2022 | 1 | 2022 |