63 citations · 323 across the 46 of their papers we have counts for
41 papers · 1 filter
On the Performance of Gradient Tracking with Local Updates
Edward Duc Hien Nguyen, Sulaiman A. Alghunaim, Kun Yuan +1
We study the decentralized optimization problem where a network of agents seeks to minimize the average of a set of heterogeneous non-convex cost functions distributedly. State…
Distributed Generalized Wirtinger Flow for Interferometric Imaging on Networks
Sean M. Farrell, Ashok Veeraraghavan, Ashutosh Sabharwal +1
We study the problem of decentralized interferometric imaging over networks, where agents have access to a subset of local radar measurements and can compute pair-wise correlations…
On Arbitrary Compression for Decentralized Consensus and Stochastic Optimization over Directed Networks
Mohammad Taha Toghani, César A. Uribe
We study the decentralized consensus and stochastic optimization problems with compressed communications over static directed graphs. We propose an iterative gradient-based algorit…
On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression
Ekaterina Trimbach, Edward Duc Hien Nguyen, César A. Uribe
We study the acceleration of the Local Polynomial Interpolation-based Gradient Descent method (LPI-GD) recently proposed for the approximate solution of empirical risk minimization…
On Distributed Exact Sparse Linear Regression over Networks
Tu Anh-Nguyen, César A. Uribe
In this work, we propose an algorithm for solving exact sparse linear regression problems over a network in a distributed manner. Particularly, we consider the problem where data i…
Scalable Average Consensus with Compressed Communications
Mohammad Taha Toghani, César A. Uribe
We propose a new decentralized average consensus algorithm with compressed communication that scales linearly with the network size n. We prove that the proposed method converges t…
Approximate Wasserstein Attraction Flows for Dynamic Mass Transport over Networks
Ferran Arqué, César A. Uribe, Carlos Ocampo-Martinez
This paper presents a Wasserstein attraction approach for solving dynamic mass transport problems over networks. In the transport problem over networks, we start with a distributio…
Robust Distributed Optimization With Randomly Corrupted Gradients
Berkay Turan, Cesar A. Uribe, Hoi-To Wai +1
In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all t…
Hyperfast Second-Order Local Solvers for Efficient Statistically Preconditioned Distributed Optimization
Pavel Dvurechensky, Dmitry Kamzolov, Aleksandr Lukashevich +4
Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems. In this approach, multiple worker nodes compute gradients in para…
Distributed Optimization with Quantization for Computing Wasserstein Barycenters
Roman Krawtschenko, César A. Uribe, Alexander Gasnikov +1
We study the problem of the decentralized computation of entropy-regularized semi-discrete Wasserstein barycenters over a network. Building upon recent primal-dual approaches, we p…
Robust Asynchronous and Network-Independent Cooperative Learning
Eduardo Mojica-Nava, David Yanguas-Rojas, César A. Uribe
We consider the model of cooperative learning via distributed non-Bayesian learning, where a network of agents tries to jointly agree on a hypothesis that best described a sequence…
On Robustness of the Normalized Subgradient Method with Randomly Corrupted Subgradients
Berkay Turan, Cesar A. Uribe, Hoi-To Wai +1
Numerous modern optimization and machine learning algorithms rely on subgradient information being trustworthy and hence, they may fail to converge when such information is corrupt…
A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks
César A. Uribe, Ali Jadbabaie
We propose a distributed, cubic-regularized Newton method for large-scale convex optimization over networks. The proposed method requires only local computations and communications…
Multimarginal Optimal Transport by Accelerated Alternating Minimization
Nazarii Tupitsa, Pavel Dvurechensky, Alexander Gasnikov +1
We consider a multimarginal optimal transport, which includes as a particular case the Wasserstein barycenter problem. In this problem one has to find an optimal coupling between $…
Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation
Berkay Turan, Cesar A. Uribe, Hoi-To Wai +1
Distributed algorithms for multi-agent resource allocation can provide privacy and scalability over centralized algorithms in many cyber-physical systems. However, the distributed…
Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal-dual tensor methods
Pavel Dvurechensky, Petr Ostroukhov, Alexander Gasnikov +2
Motivated, in particular, by the entropy-regularized optimal transport problem, we consider convex optimization problems with linear equality constraints, where the dual objective…
Generalized Self-concordant Hessian-barrier algorithms
Pavel Dvurechensky, Mathias Staudigl, César A. Uribe
Many problems in statistical learning, imaging, and computer vision involve the optimization of a non-convex objective function with singularities at the boundary of the feasible s…
Non-Bayesian Social Learning with Uncertain Models over Time-Varying Directed Graphs
César A. Uribe, James Z. Hare, Lance Kaplan +1
We study the problem of non-Bayesian social learning with uncertain models, in which a network of agents seek to cooperatively identify the state of the world based on a sequence o…
Resilient Distributed Optimization Algorithms for Resource Allocation
Cesar A. Uribe, Hoi-To Wai, Mahnoosh Alizadeh
Distributed algorithms provide flexibility over centralized algorithms for resource allocation problems, e.g., cyber-physical systems. However, the distributed nature of these algo…
Gradient Methods for Problems with Inexact Model of the Objective
Fedor Stonyakin, Darina Dvinskikh, Pavel Dvurechensky +8
We consider optimization methods for convex minimization problems under inexact information on the objective function. We introduce inexact model of the objective, which as a parti…
On Primal-Dual Approach for Distributed Stochastic Convex Optimization over Networks
Darina Dvinskikh, Eduard Gorbunov, Alexander Gasnikov +2
We introduce a primal-dual stochastic gradient oracle method for distributed convex optimization problems over networks. We show that the proposed method is optimal in terms of com…
On the Complexity of Approximating Wasserstein Barycenter
Alexey Kroshnin, Darina Dvinskikh, Pavel Dvurechensky +3
We study the complexity of approximating Wassertein barycenter of discrete measures, or histograms of size by contrasting two alternative approaches, both using entropic re…
On Increasing Self-Confidence in Non-Bayesian Social Learning over Time-Varying Directed Graphs
César A. Uribe, Ali Jadbabaie
We study the convergence of the log-linear non-Bayesian social learning update rule, for a group of agents that collectively seek to identify a parameter that best describes a join…
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…