362 citations · 2.5k across the 76 of their papers we have counts for
9 papers · 1 filter
Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE
Jingzhao Zhang, César A. Uribe, Aryan Mokhtari +1
We develop a distributed algorithm for convex Empirical Risk Minimization, the problem of minimizing large but finite sum of convex functions over networks. The proposed algorithm…
Escaping Saddle Points in Constrained Optimization
Aryan Mokhtari, Asuman Ozdaglar, Ali Jadbabaie
In this paper, we study the problem of escaping from saddle points in smooth nonconvex optimization problems subject to a convex set . We propose a generic framework t…
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
Majid Jahani, Xi He, Chenxin Ma +4
In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE) method in which sample size is gradually increasing to quickly obtain a solution whose empiric…
A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
We introduce the primal-dual quasi-Newton (PD-QN) method as an approximated second order method for solving decentralized optimization problems. The PD-QN method performs quasi-New…
An Exact Quantized Decentralized Gradient Descent Algorithm
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani +1
We consider the problem of decentralized consensus optimization, where the sum of smooth and strongly convex functions are minimized over distributed agents that form a con…
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou +2
Recently, the decentralized optimization problem is attracting growing attention. Most existing methods are deterministic with high per-iteration cost and have a convergence rate q…
Direct Runge-Kutta Discretization Achieves Acceleration
Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra +1
We study gradient-based optimization methods obtained by directly discretizing a second-order ordinary differential equation (ODE) related to the continuous limit of Nesterov's acc…
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
This paper considers stochastic optimization problems for a large class of objective functions, including convex and continuous submodular. Stochastic proximal gradient methods hav…
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
In this paper, we showcase the interplay between discrete and continuous optimization in network-structured settings. We propose the first fully decentralized optimization method f…