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Found 6 papers, 0 papers with code
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
These new algorithms can handle statistical and sys- tem heterogeneity, which are the two main challenges in federated learning, while achieving the best known communication complexity.
Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function
We develop a novel and single-loop variance-reduced algorithm to solve a class of stochastic nonconvex-convex minimax problems involving a nonconvex-linear objective function, which has various applications in different fields such as ma- chine learning and robust optimization.
Transferring Optimality Across Data Distributions via Homotopy Methods
Homotopy methods, also known as continuation methods, are a powerful mathematical tool to efficiently solve various problems in numerical analysis, including complex non-convex optimization problems where no or only little prior knowledge regarding the localization of the solutions is available.
Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates
Our algorithms have several new features compared to existing methods.
A Universal Primal-Dual Convex Optimization Framework
We propose a new primal-dual algorithmic framework for a prototypical constrained convex optimization template.
An Inexact Proximal Path-Following Algorithm for Constrained Convex Minimization
Many scientific and engineering applications feature nonsmooth convex minimization problems over convex sets.
