269 citations · 551 across the 43 of their papers we have counts for
21 papers · 1 filter
Accelerated Proximal Gradient Methods in the affine-quadratic case: Strong convergence and limit identification
Walaa M. Moursi, Andrew Naguib, Viktor Pavlovic +1
Recent works by Bot-Fadili-Nguyen (arXiv:2510.22715) and by Jang-Ryu (arXiv:2510.23513) resolve long-standing iterate convergence questions for accelerated (proximal) gradient meth…
A Primal-Dual Frank-Wolfe Algorithm for Linear Programming
Matthew Hough, Stephen A. Vavasis
We present two first-order primal-dual algorithms for solving saddle point formulations of linear programs, namely FWLP (Frank-Wolfe Linear Programming) and FWLP-P. The former iter…
Accelerated gradient descent: A guaranteed bound for a heuristic restart strategy
Walaa M. Moursi, Viktor Pavlovic, Stephen A. Vavasis
The convergence rate in function value of accelerated gradient descent is optimal, but there are many modifications that have been used to speed up convergence in practi…
Range of the displacement operator of PDHG with applications to quadratic and conic programming
Tao Jiang, Walaa M. Moursi, Stephen A. Vavasis
Primal-dual hybrid gradient (PDHG) is a first-order method for saddle-point problems and convex programming introduced by Chambolle and Pock. Recently, Applegate et al.\ analyzed t…
MGProx: A nonsmooth multigrid proximal gradient method with adaptive restriction for strongly convex optimization
Andersen Ang, Hans De Sterck, Stephen Vavasis
We study the combination of proximal gradient descent with multigrid for solving a class of possibly nonsmooth strongly convex optimization problems. We propose a multigrid proxima…
Nonlinear conjugate gradient for smooth convex functions
Sahar Karimi, Stephen Vavasis
The method of nonlinear conjugate gradients (NCG) is widely used in practice for unconstrained optimization, but it satisfies weak complexity bounds at best when applied to smooth…