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In this paper, we present a perspective that a solution process for a class of distributed optimization is passive. After formulating the intended distributed optimization problem, we design a distributed optimization dynamics based on so-called PI consensus algorithm. The dynamical system is then shown to be regarded as a feedback interconnection of passive systems and hence to preserve passivity...
In this paper, we address energy management for heating, ventilation, and air-conditioning (HVAC) systems in buildings, and present a novel combined optimization and control approach. We first formulate a thermal dynamics and an associated optimization problem. An optimization dynamics is then designed based on a standard primal-dual algorithm, and its strict passivity is proved. We then design a...
This paper investigates resource allocation algorithms that use limited communication - where the supplier of a resource broadcasts a coordinating signal using one bit of information to users per iteration. Rather than relay anticipated consumption to the supplier, the users locally compute their allocation, while the supplier measures the total resource consumption. Since the users do not compare...
This paper presents a real-time decentralized temperature control scheme via Heating Ventilation and Air Conditioning (HVAC) systems for energy efficient buildings, which balances user comfort and energy saving. Firstly, we introduce a thermal dynamic model of building systems and its approximation. Then a steady-state optimization problem is formulated, which aims to minimize the aggregate deviation...
We study a deterministic primal-dual subgradient method for distributed optimization of a separable objective function with global inequality constraints. To control the norm of dual variables, we augment the Lagrangian function with a regularizer on dual variables. Specifically, we show that under a certain restriction on the step size of the underlying algorithm, the norm of dual variables is inversely...
There has been a growing effort in studying the distributed optimization problem over a network. The objective is to optimize a global function formed by a sum of local functions, using only local computation and communication. Literature has developed consensus-based distributed (sub)gradient descent (DGD) methods and has shown that they have the same convergence rate O(log t/√t) as the centralized...
A multi-carrier one-way relay network is considered, in which a source wishes to send information to a destination via a full-duplex amplify-and-forward relay. We perform power allocation across different subcarriers for the source and the relay, as well as subcarrier pairing at the relay, so as to maximize the achievable sum rate subject to individual power budget constraint at each transmit node,...
In this paper, we consider the problem of distributed control for linear network systems to achieve optimal steady-state performance. Motivated by recent research on re-engineering cyber-physical systems, we propose a reverse- and forward-engineering framework which consists of two steps. Firstly, we reverse-engineer a dynamic system as a gradient algorithm to solve an optimization problem. Secondly,...
Increasing distributed energy resources introduce rapid and fast fluctuations in power supply and demand. As a result, the control and economic optimization for power networks need to operate at faster time-scales for reliability and economic efficiency. In this paper, we consider the problem of achieving real-time economic dispatch for power networks with controllable loads. We first present system...
In this paper, we propose a robust proximal classifier via absolute value inequalities (AVIPC) for pattern classification. AVIPC determines K proximal planes by solving K optimization problems with absolute value inequalities. In AVIPC, each proximal plane is closer to one class and far away from the others. By using the absolute value inequalities, AVIPC is more robust and sparse than traditional...
Cuckoo search is a swarm-intelligence-based algorithm that is very effective for solving highly nonlinear optimization problems. In this paper, the multiobjective cuckoo search is extended so as to obtain high-quality Pareto fronts more accurately for multiobjective optimization problems with complex constraints. The proposed approach uses a combination of the cuckoo search with non-dominated sorting...
Automatic generation control (AGC) regulates mechanical power generation in response to load changes through local measurements. Its main objective is to maintain system frequency and keep energy balanced within each control area so as to maintain the scheduled net interchanges between control areas. The scheduled interchanges as well as some other factors of AGC are determined at a slower time scale...
In this paper, the linear minimum mean square error beamformers are designed for relay-assisted cloud radio access network (C-RAN). In C-RAN, the remote radio units are separated from the baseband units to save energy cost. To further enhance network coverage, it is a good choice to duly arrange relay nodes. Regrading the per-antenna power constraints at both the relay nodes and the remote radio heads...
In this paper, the low information-exchange and robust precoding design for distributed antenna systems is investigated, which is of great importance for the new mobile network architecture namely cloud radio access networks (C-RAN). Relying on an interesting low complexity decomposition algorithm, a distributed robust linear minimum mean-square-error (LMMSE) precoding algorithm is proposed. Exploiting...
Cloud radio access network (C-RAN) is a new concept of network architecture, which brings a technical revolution into the wireless communication market and leads to some kind of all new mode of the future wireless communications. In this paper the clustering algorithm based on multi-objective optimization is investigated. The proposed algorithm aims at maximizing the throughput contribution of the...
The central goal in multi-agent systems is to engineer a decision making architecture where agents make independent decisions in response to local information while ensuring that the emergent global behavior is desirable with respect to a given system level objective. Our previous work identified a systematic methodology for such a task using the framework of state based games. One core advantage...
In this paper, we focus on resource allocation for distributed broadband heterogeneous networks. Considering the fairness among different users, using the game-theoretic diagram, a fair resource allocation scheme is proposed first under each mobile user's minimal rate constraint. Then, an optimal power allocation algorithm is derived based on the Lagrange optimization method. To further improve the...
Demand response has recently become a topic of active research. Most of work however considers only the balance between aggregate load and supply, and abstracts away the underlying power network and the associated power flow constraints and operating constraints. In this paper, we study demand response in a radial distribution network, by formulating it as an optimal power flow problem that maximizes...
In cognitive radio (CR) networks, the secondary users are allowed to use the licensed frequency bands of the primary users (PUs) when these bands are vacant. However,once the PUs come back, they may collide with the transmitting secondary users. Therefore, the secondary users are generally required to sense the channel periodically to ensure that the primary users are absent during their data transmission...
In order to escape from premature convergence and lack good direction in particles the evolutionary process, quantum technology and immunologic mechanism were employed, and an adaptive immune quantum-behaved particle swarm optimization algorithm was provided. Meanwhile, according to larger calculation and longer consumed time, parallel computation technology was introduced into the provided algorithm...
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