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Based on the so‐called lazy gradient information, this note proposes two communication‐reduced distributed optimization algorithms over undirected multi‐agent networks. The lazy gradients refer to some gradients that do not change much in the ...

Federated learning (FL) is a promising technique for resolving the rising privacy and security concerns. Its main ingredient is to cooperatively learn the model among the distributed clients without uploading any sensitive data. In this article, we ...

Output feedback distributed optimization problem is studied for higher‐order multi‐agent systems with uncertain nonlinearities, which are assumed to satisfy linear growth conditions. The agents dynamics are permitted to be heterogenous with ...

Contact tracing can limit the spread of infectious diseases by notifying people of potential exposure to disease. Manual contact tracing is resource-intensive, but much of it can be automated using mobile phones, which are ubiquitous and can detect and ...

We consider the communication complexity of some fundamental convex optimization problems in the point-to-point (coordinator) and blackboard communication models. We strengthen known bounds for approximately solving linear regression, p-norm regression (...

We consider a large-scale parallel-server loss system with an unknown arrival rate, where each server is able to adjust its processing speed. The objective is to minimize the system cost, which consists of a power cost to maintain the servers' processing ...

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk ...

We propose a distributed algorithm with an event‐triggered communication mechanism for the economic dispatch problem (EDP), aimed at minimizing communication resources in growing power systems. This method determines step size from each agent's local ...

The identification of switched systems amounts to a mixed integer nonlinear optimization problem, where the continuous variables are associated to the model parameterizations of the different modes, and the discrete ones are related to the ...

This paper introduces output feedback distributed optimization algorithms designed specifically for second‐order nonlinear multi‐agent systems. The agents are allowed to have heterogeneous dynamics, characterized by distinct nonlinearities, as ...

We present a theoretical study of server-side optimization in federated learning. Our results are the first to show that the widely popular heuristic of scaling the client updates with an extra parameter is very useful in the context of Federated ...

When optimizing electric power system operational decisions, it is of great importance to prevent potential failures in both the system operation and the optimization algorithm. In “An Alternating Direction Method of Multipliers-Based Distributed ...

In this paper, we study efficient and robust computational methods for solving the security-constrained alternating current optimal power flow (SC-ACOPF) problem, a two-stage nonlinear optimization problem with disjunctive constraints, that is central to ...

We consider a large-scale parallel-server system, where each server dynamically chooses its processing speed in a completely distributed fashion. The goal is to minimize the global cost that is the sum of the average cost of maintaining the respective ...

Modern applied optimization problems become more and more complex every day. Due to this fact, distributed algorithms that can speed up the process of solving an optimization problem through parallelization are of great importance. The main ...

This paper studies the distributed optimization problem of second‐order multiagent systems containing external disturbances. To reject the external disturbances and lead agents' states to converge to the optimal consensus point, an adaptive event‐...

We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires O (d) communication complexity, where d is the problem dimension. We also provide theoretical analysis to ...

Evaluating the fitness of individuals in the initial population of an evolutionary algorithm (EA) is usually straightforward and poses few theoretical problems. In asynchronous steady-state EAs (ASSEAs), however, the choice of initialization strategy ...

We introduce moptipy, a toolbox for implementing, experimenting with, and applying optimization algorithms. It features mechanisms for executing fully reproducible experiments. Our seeding procedure for random number generators makes our experiments ...

In decentralized optimization, several nodes connected by a network collaboratively minimize some objective function. For minimization of Lipschitz functions lower bounds are known along with optimal algorithms. We study a specific class of ...

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate ...

We study sparse linear regression over a network of agents, modeled as an undirected graph (with no centralized node). The estimation problem is formulated as the minimization of the sum of the local LASSO loss functions plus a quadratic penalty of the ...