This paper is concerned with a general class of distributed constrained optimization problems over multiagent network, where the global objective function represented by sum all local functions. Each agent in network only knows its own function, and restricted to nonempty closed convex set. We discuss scenario communication whole expressed as sequence time-varying unbalanced directed graphs. The graphs are required be uniformly jointly strongly connected weight matrices row-stochastic. To...

In this paper, the economic dispatch problem (EDP) in smart grids is investigated over a directed network, which concentrates on allocating generation power among generators to satisfy load demands with minimal total cost while complying all constraints of local capacity. Each generator possesses its own cost, and sum costs. To deal EDP, most existing methods, such as strategy based push-sum, surmount unbalancedness induced by network via employing column-stochastic weights, might be...

This paper investigates distributed online optimization for a group of agents communicating on undirected networks. The objective is to collaboratively minimize the sum locally known convex cost functions while overcoming communication bandwidth limitations. To tackle this challenge, we propose Q-DADAM algorithm, quantized adaptive momentum method that ensures interact with neighbors optimize global function collectively. Unlike many existing algorithms overlook constraints, algorithm...

This paper studies a class of distributed convex optimization problems by set agents in which each agent only has access to its own local objective function and the estimate is restricted both coupling linear constraint individual box constraints. Our focus devise primal-dual gradient algorithm for working out problem over sequence time-varying general directed graphs. The communications among are assumed be uniformly strongly connected. A column-stochastic mixing matrix fixed step-size...

In this paper, we discuss a class of distributed constrained optimization problems in power systems where the target is to optimize sum all agents' local convex objective functions over general unbalanced directed communication network. Each function known exclusively single agent, and variables are global coupling linear constraint individual box constraints. To collaboratively solve problems, existing methods mostly require network be balanced or have knowledge in-neighbors' out-degree for...

This article investigates the problem of distributed online optimization for a group units communicating on time-varying unbalanced directed networks. The main target set is to cooperatively minimize sum all locally known convex cost functions (global function) while pursuing privacy their local being well masked. To address such problems in collaborative and fashion, differentially private-distributed stochastic subgradient-push algorithm, called DP-DSSP, proposed, which ensures that...

In this article, we concentrate on dealing with the distributed optimization problem over a directed network, where each unit possesses its own convex cost function and principal target is to minimize global (formulated by average of all local functions) while obeying network connectivity structure. Most existing methods, such as push-sum strategy, have eliminated unbalancedness induced via utilizing column-stochastic weights, which may be infeasible if implementation requires gain access...

This article focuses on distributed convex optimization problems over an unbalanced directed multiagent (no central coordinator) network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known cost functions. Every single agent in only knows its local objective function and constraint, constrained a privately set. Furthermore, we particularly discuss scenario which interactions among agents whole are subjected possible link failures. To collaboratively...

This paper focuses on a class of event-triggered discrete-time distributed consensus optimization algorithms, with set agents whose communication topology is depicted by sequence time-varying networks. The process steered independent trigger conditions observed and decentralized just rests each agent’s own state. At time, agent only has access to its privately local Lipschitz convex objective function. the next time step, every updates state applying function information sent from...

In this article, we study a decentralized online constrained optimization problem with common constraint set, in which the main purpose is to optimize over certain time interval. Due continuously broadcast privacy-related information between nodes, most existing algorithms that settle issue may be at risk of privacy leakage. response such dilemma, propose an effective differentially private dual averaging algorithm, takes into account usages perturbation Laplace noise and gradient rescaling...

The problem of distributed constrained optimisation over a network agents, where the goal is to cooperatively minimise sum all local convex objective functions studied. Each agent in possesses only its private function and coupling equality constraint inequality constraint. Moreover, authors particularly focus on scenario each allowed interact with their in‐neighbours series time‐varying directed unbalanced networks. To collectively address problem, novel primal‐dual push‐DIGing (integrated...

Decentralized dual methods play significant roles in large-scale optimization, which effectively resolve many constrained optimization problems machine learning and power systems. In this article, we focus on studying a class of totally non-smooth composite over multi-agent systems, where the mutual goal agents system is to optimize sum two separable functions consisting strongly-convex function another convex (not necessarily strongly-convex) function. Agents conduct parallel local...

The increasing privacy concerns associated with cloud-assisted image retrieval have captured the attention of researchers. However, a significant number current research endeavors encounter limitations, including suboptimal accuracy, inefficient retrieval, and lack effective result verification mechanisms. To address these we propose an adaptive verifiable privacy-preserving medical (AVPMIR) scheme in outsourced cloud. Specifically, utilize convolutional neural network (CNN) ResNet50 model...

In this article, we consider a decentralized constrained optimization problem over time-varying directed networks, where nodes in the networks collaboratively minimize sum of local cost functions subject to common constraint set. Due continued broadcasting information that involves privacy during process, most existing algorithms for settling may suffer from risk leakage normal nodes. The becomes even more challenging when are and directed. To overcome these difficulties, propose an...

In this article, the problem of distributed convex optimization is investigated, where target to collectively minimize a sum local functions over an unbalanced directed multiagent network. Each agent in network possesses only its private objective function, and all constitutes global function. We particularly consider scenario, underlying interaction strongly connected relevant weight matrix row stochastic. To figure out problem, accelerated convergence algorithm agents utilize uncoordinated...

This paper focuses on solving the problem of composite constrained convex optimization with a sum smooth functions and non-smooth regularization terms (ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm) subject to locally general constraints. Motivated by modern large-scale information processing problems in machine learning (the samples training dataset are randomly decentralized across multiple computing nodes), each objective is...

This article considers distributed optimization by a group of agents over an undirected network. The objective is to minimize the sum twice differentiable convex function and two possibly nonsmooth functions, one which composed bounded linear operator. A novel primal-dual fixed point algorithm proposed based on adapted metric method, exploits second-order information function. Furthermore, incorporating randomized coordinate activation mechanism, we propose asynchronous iterative that allows...