A consensus problem has been studied in many fundamental and application fields to analyze coordinated behavior in multi-agent systems. In a consensus problem, it is usually assumed that a state of each agent is scalar and all agents have an identical linear consensus protocol. We present a consensus problem of multi-agent systems where each agent has multiple state variables and a performance value evaluated by a nonlinear performance function according to its current state. We derive sufficient conditions for agents to achieve consensus on the performance value using an algebraic graph theory and the mean value theorem. We also consider an application of a performance consensus problem to resource allocation in soft real-time systems so as to achieve a fair QoS (Quality of Service) level.

This paper proposes a consensus-based distributed Particle Swarm Optimization (PSO) algorithm with event-triggered communications for a non-convex and non-differentiable optimization problem. We consider a multi-agent system whose local communications among agents are represented by a fixed and connected graph. Each agent has multiple particles as estimated solutions of global optima and updates positions of particles by an average consensus dynamics on an auxiliary variable that accumulates the past information of the own objective function. In contrast to the existing time-triggered approach, the local communications are carried out only when the difference between the current auxiliary variable and the variable at the last communication exceeds a threshold. We show that the global best can be estimated in a distributed way by the proposed event-triggered PSO algorithm under a diminishing condition of the threshold for the trigger condition.

This paper presents a quantized gradient descent algorithm for distributed nonconvex optimization in multiagent systems that takes into account the bandwidth limitation of communication channels. Each agent encodes its estimation variable using a zoom-in parameter and sends the quantized intermediate variable to the neighboring agents. Then, each agent updates the estimation by decoding the received information. In this paper, we show that all agents achieve consensus and their estimated variables converge to a critical point in the optimization problem. A numerical example of a nonconvex logistic regression shows that there is a trade-off between the convergence rate of the estimation and the communication bandwidth.

This paper considers a distributed subgradient method for online optimization with event-triggered communication over multi-agent networks. At each step, each agent obtains a time-varying private convex cost function. To cooperatively minimize the global cost function, these agents need to communicate each other. The communication with neighbor agents is conducted by the event-triggered method that can reduce the number of communications. We demonstrate that the proposed online algorithm achieves a sublinear regret bound in a dynamic environment with slow dynamics.

In this paper, we consider a distributed stochastic nonconvex optimization problem for multiagent systems. We propose a distributed stochastic gradient-tracking method with event-triggered communication. A group of agents cooperatively finds a critical point of the sum of local cost functions, which are smooth but not necessarily convex. We show that the proposed algorithm achieves a sublinear convergence rate by appropriately tuning the step size and the trigger threshold. Moreover, we show that agents can effectively solve a nonconvex optimization problem by the proposed event-triggered algorithm with less communication than by the existing time-triggered gradient-tracking algorithm. We confirm the validity of the proposed method by numerical experiments.

In this paper, we consider distributed decision-making over directed time-varying multi-agent systems. We consider an adversarial bandit problem in which a group of agents chooses an option from among multiple arms to maximize the total reward. In the proposed method, each agent cooperatively searches for the optimal arm with the highest reward by a consensus-based distributed Exp3 policy. To this end, each agent exchanges the estimation of the reward of each arm and the weight for exploitation with the nearby agents on the network. To unify the explored information of arms, each agent mixes the estimation and the weight of the nearby agents with their own values by a consensus dynamics. Then, each agent updates the probability distribution of arms by combining the Hedge algorithm and the uniform search. We show that the sublinearity of a pseudo-regret can be achieved by appropriately setting the parameters of the distributed Exp3 policy.

In this paper, we propose a distributed subgradient-based method over quantized and event-triggered communication networks for constrained convex optimization. In the proposed method, each agent sends the quantized state to the neighbor agents only at its trigger times through the dynamic encoding and decoding scheme. After the quantized and event-triggered information exchanges, each agent locally updates its state by a consensus-based subgradient algorithm. We show a sufficient condition for convergence under summability conditions of a diminishing step-size.

This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.

This paper proposes a novel distributed proximal minimization algorithm for constrained optimization problems over fixed strongly connected networks. At each iteration, each agent updates its own state by evaluating a proximal operator of its objective function under a constraint set and compensating the unbalancing due to unidirectional communications. We show that the states of all agents asymptotically converge to one of the optimal solutions. Numerical results are shown to confirm the validity of the proposed method.

This paper proposes a distributed algorithm over quantized communication networks for unconstrained optimization with smooth cost functions. We consider a multi-agent system whose local communication is represented by a fixed and connected graph. Each agent updates a state and an auxiliary variable for the estimates of the optimal solution and the average gradient of the entire cost function by a consensus-based optimization algorithm. The state and the auxiliary variable are sent to neighbor agents through a uniform quantizer. We show a convergence rate of the proposed algorithm with respect to the errors between the cost at the time-averaged state and the optimal cost. Numerical examples show that the estimated solution by the proposed quantized algorithm converges to the optimal solution.

This paper addresses an application of the potential game theory to a power-aware mobile sensor coverage problem where each sensor tries to maximize a probability of target detection in a convex mission space. The probability of target detection depends on a sensing voltage of each mobile sensor as well as its current position. While a higher sensing voltage improves the target detection probability, this requires more power consumption. In this paper, we assume that mobile sensors have different sensing capabilities of detecting a target and they can adaptively change sensing areas by adjusting their sensing voltages. We consider an objective function to evaluate a trade-off between improving the target detection probability and reducing total power consumption of all sensors. We represent a sensing voltage and a position of each mobile sensor using a barycentric coordinate over an extended strategy space. Then, the sensor coverage problem can be formulated as a potential game where the power-aware objective function and the barycentric coordinates correspond to a potential function and players' mixed strategies, respectively. It is known that all local maximizers of a potential function in a potential game are equilibria of replicator dynamics. Based on this property of potential games, we propose decentralized control for the power-aware sensor coverage problem such that each mobile sensor finds a locally optimal position and sensing voltage by updating its barycentric coordinate using replicator dynamics.

This paper proposes an adaptive resource allocation for multi-tier computing systems to guarantee a fair QoS level under resource constraints of tiers. We introduce a multi-tier computing architecture which consists of a group of resource managers and an arbiter. Resource allocation of each client is managed by a dedicated resource manager. Each resource manager updates resources allocated to subtasks of its client by locally exchanging QoS levels with other resource managers. An arbiter compensates the updated resources to avoid overload conditions in tiers. Based on the compensation by the arbiter, the subtasks of each client are executed in corresponding tiers. We derive sufficient conditions for the proposed resource allocation to achieve a fair QoS level avoiding overload conditions in all tiers with some assumptions on a QoS function and a resource consumption function of each client. We conduct a simulation to demonstrate that the proposed resource allocation can adaptively achieve a fair QoS level without causing any overload condition.

In order to examine the validity of Mott-Schottky model at organic/metal interfaces, the position of the vacuum level of N,N'-bis(3-methylphenyl)-N,N'-diphenyl -[1,1'-biphenyl]-4,4'-diamine (TPD) film formed on various metal substrates (Au, Cu, Ag, Mg and Ca) was measured as a function of the film-thickness by Kelvin probe method in ultrahigh vacuum (UHV). TPD is a typical hole-injecting material for organic electroluminescent devices. At all the interfaces, sharp shifts of the vacuum level were observed within 1 nm thickness. Further deposition of TPD up to 100 nm did not change the position of the vacuum level indicating no band bending at these interfaces. These findings clearly demonstrate the Fermi level alignment between metal and bulk TPD solid is not established within typical thickness of real devices.

This paper addresses a discrete-time consensus problem with non-linear performance functions over dynamically changing communication topologies. Each agent has a performance value based on its internal information state and exchanges the performance value with other agents to achieve consensus. We derive sufficient conditions for a global consensus using algebraic graph theory.

This paper investigates a constrained distributed online optimization problem over strongly connected communication networks, where a local cost function of each agent varies in time due to environmental factors. We propose a distributed online projected subgradient method over unbalanced directed networks. The performance of the proposed method is evaluated by a regret which is defined by the error between the cumulative cost over time and the cost of the optimal strategy in hindsight. We show that a logarithmic regret bound can be achieved for strongly convex cost functions. We also demonstrate the validity of the proposed method through a numerical example on distributed estimation over a diffusion field.