The Sparse Principal Component Analysis Problem: Optimality Conditions and Algorithms. - Amir Beck; Yakov Vaisbourd (J. Optim. Theory Appl., 2016)

Rate of convergence analysis of dual-based variables decomposition methods for strongly convex problems. - Amir Beck; Luba Tetruashvili; Yakov Vaisbourd; Ariel Shemtov (Oper. Res. Lett., 2016)

Globally Solving the Trust Region Subproblem Using Simple First-Order Methods. - Amir Beck; Yakov Vaisbourd (SIAM J. Optim., 2018)

First Order Methods Beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems. - Jérôme Bolte; Shoham Sabach; Marc Teboulle; Yakov Vaisbourd (SIAM J. Optim., 2018)

Novel Proximal Gradient Methods for Nonnegative Matrix Factorization with Sparsity Constraints. - Marc Teboulle; Yakov Vaisbourd (SIAM J. Imaging Sci., 2020)

Maximum Entropy on the Mean and the Cramér Rate Function in Statistical Estimation and Inverse Problems: Properties, Models, and Algorithms. - Yakov Vaisbourd; Rustum Choksi; Ariel Goodwin; Tim Hoheisel; Carola-Bibiane Schönlieb (CoRR, 2022)

An elementary approach to tight worst case complexity analysis of gradient based methods. - Marc Teboulle; Yakov Vaisbourd (Math. Program., 2023)