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A Linearly Convergent Doubly Stochastic Gauss-Seidel Algorithm for Solving Linear Equations and A Certain Class of Over-Parameterized Optimization Problems
Meisam Razaviyayn, Mingyi Hong, Navid Reyhanian +1
Consider the classical problem of solving a general linear system of equations . It is well known that the (successively over relaxed) Gauss-Seidel scheme and many of its var…
Distributed Non-Convex First-Order Optimization and Information Processing: Lower Complexity Bounds and Rate Optimal Algorithms
Haoran Sun, Mingyi Hong
We consider a class of popular distributed non-convex optimization problems, in which agents connected by a network collectively optimize a sum of smooth (possibly no…
On the Sublinear Convergence of Randomly Perturbed Alternating Gradient Descent to Second Order Stationary Solutions
Songtao Lu, Mingyi Hong, Zhengdao Wang
The alternating gradient descent (AGD) is a simple but popular algorithm which has been applied to problems in optimization, machine learning, data ming, and signal processing, etc…
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization
Mingyi Hong, Jason D. Lee, Meisam Razaviyayn
In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM),…