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Data-driven proximal algorithms for the design of structured optimal feedback gains

Data-driven proximal algorithms for the design of structured...

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American Control Conference

作者： Sepideh Hassan-Moghaddam Mihailo R. Jovanovic Sean Meyn University of Southern California Los Angeles CA 90089 University of Florida Gainesville FL 32611

ISBN: (纸本)9781538679012;9781538679265

Distributed feedback design and complexity constrained control are examples of problems posed within the domain of structured optimal feedback synthesis. The optimal feedback gain is typically a non-convex function of system primitives. However, in recent years, algorithms have been proposed to obtain locally optimal solutions. In applications to large-scale distributed control, the major obstacle is computational complexity. This paper addresses complexity through a combination of linear-algebraic techniques and computational methods adapted from both machine learning and reinforcement learning. It is shown that for general classes of optimal control problems, the objective function and its gradient can be computed from data. Transformations borrowed from the theory of reinforcement learning are adapted to obtain simulation-based algorithms for computing the structured optimal H_2 feedback gain. Customized proximal algorithms based on gradient descent and incremental gradient are tested in computational experiments and their relative merits are discussed.

关键词： approximation theory control system synthesis convex programming distributed control feedback gradient methods learning (artificial intelligence) optimal control

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Katz centrality of Markovian temporal networks: Analysis and optimization

Katz centrality of Markovian temporal networks: Analysis and...

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American Control Conference

作者： Masaki Ogura Victor M. Preciado Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia 19014 USA

ISBN: (纸本)9781509045839

Identifying important nodes in complex networks is a fundamental problem in network analysis. Although a plethora of measures has been proposed to identify important nodes in static (i.e., time-invariant) networks, there is a lack of tools in the context of temporal networks (i.e., networks whose connectivity dynamically changes over time). The aim of this paper is to propose a system-theoretic approach for identifying important nodes in temporal networks. In this direction, we first propose a generalization of the popular Katz centrality measure to the family of Markovian temporal networks using tools from the theory of Markov jump linear systems. We then show that Katz centrality in Markovian temporal networks can be efficiently computed using linear programming. Finally, we propose a convex program for optimizing the Katz centrality of a given node by tuning the weights of the temporal network in a cost-efficient manner. Numerical simulations illustrate the effectiveness of the obtained results.

关键词： Network Network analysis Ganglia convex programming time-invariant complex networks Plethora

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Cooperative Energy Trading in CoMP Systems Powered by Smart Grids

Cooperative Energy Trading in CoMP Systems Powered by Smart ...

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IEEE Global Communications Conference

作者： Jie Xu Rui Zhang ECE Department National University of Singapore

ISBN: (纸本)9781479935130

This paper studies the energy management in the coordinated multi-point (CoMP) systems powered by smart grids, where each base station (BS) with local renewable energy generation is allowed to implement the two-way energy trading with the grid. Due to the unevenly generated renewable energy over distributed BSs and the difference in the prices for buying/selling energy from/to the gird, it is in general beneficial for the cooperative BSs to jointly manage their energy trading with the grid and energy consumption in CoMP based communication for reducing the total energy cost. We consider the downlink transmission in one CoMP cluster by jointly optimizing the BSs' purchased/sold energy units from/to the grid and their cooperative transmit beamforming, so as to minimize the total energy cost subject to the given quality of service (QoS) constraints for the users. By applying techniques from convex optimization and uplink-downlink duality, we propose an efficient algorithm to solve this problem optimally. Through simulations, we show the performance gain achieved by our proposed joint energy trading and communication cooperation scheme in terms of cost reduction, as compared to a baseline scheme with the separate designs of communication cooperation and energy trading.

关键词： convex programming cooperative communication energy management systems smart power grids CoMP systems base station convex optimization cooperative energy trading cooperative transmit beamforming coordinated multipoint systems downlink transmission joint energy trading and communication cooperation scheme local renewable energy generation quality of service constraints smart grids two-way energy trading uplink-downlink duality Array signal processing Downlink Interference Quality of service Renewable energy sources Signal to noise ratio Vectors

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Energy Efficient Power Allocation Scheme in Heterogeneous Cellular Networks

Energy Efficient Power Allocation Scheme in Heterogeneous Ce...

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International Conference on Wireless Communications and Signal Processing

作者： Yan Lin Shidang Li Yi Wang Chunguo Li Yongming Huang Luxi Yang Sch. of Inf. Sci. & Eng. Southeast Univ. Nanjing China

ISBN: (纸本)9781467376884

The deployment of small-cell access points (SCAs) is widely acknowledged as a promising network densification way to satisfy the future capacity needs of 5G wireless cellular network. In this paper, we study the power allocation problem in heterogeneous downlink network while satisfying QoS constraints and power constraints simultaneously. The scheme is formulated as maximizing the system energy efficiency and then transformed into a tractable convex optimization problem. Utilizing multiflow RZF beamforming to reduce complexity, an iterative algorithm is proposed with provable convergence. Numerical results compare the proposed algorithm in different simulation parameters and show that increasing the number of SCAs, the antennas per SCA and users could enhance the total system energy efficiency.

关键词： 5G mobile communication array signal processing cellular radio convex programming iterative methods quality of service telecommunication power management 5G wireless cellular network QoS SCA convex optimization problem energy efficient power allocation scheme heterogeneous cellular networks iterative algorithm multiflow RZF beamforming network densification small-cell access points Antennas Array signal processing Interference Optimization Quality of service Resource management Transmitters Quality of service array signal processing Cellular networks Iterative algorithms cellular radio convex programming ANTENNA power splitting Transmitters iterative methods Energy efficiency management of resources Simulation parameter

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Global Optimization for Estimating a BRDF with Multiple Specular Lobes

Global Optimization for Estimating a BRDF with Multiple Spec...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Chanki Yu Yongduek Seo Sang Wook Lee Dept. of Media Technology Sogang University

ISBN: (纸本)9781424469840;9781424469857

This paper presents a global minimization framework for estimating analytical BRDF model parameters using the techniques of convex programming and branch and bound. Traditional local minimization suffers from local minima and requires a large number of initial conditions and supervision for successful results especially when a model is highly complex and nonlinear. We consider the Cook-Torrance model, a parametric model with the Gaussian-like Beckmann distributions for specular reflectances. Instead of optimizing the multiple parameters simultaneously, we search over all possible surface roughness values based on a branch-and-bound algorithm, and reduce the estimation problem to convex minimization with known fixed surface roughness. Our algorithm guarantees globally optimal solutions. Experiments have been carried out for isotropic surfaces to validate the method using the extensive high-precision measurements from the MERL BRDF database.

关键词： Global optimization convex programming branches model parameters Lobe Regulated industries INITIAL CONDITIONS Surface roughness

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Non-Gaussian filter based on the Method of Characteristics for Nonlinear Dynamical Systems

Non-Gaussian filter based on the Method of Characteristics f...

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American Control Conference

作者： Nagavenkat Adurthi Manoranjan Majji Texas A & M University College Station TX

ISBN: (纸本)9781538679012;9781538679265

This paper deals with the development of a non-Gaussian filter for nonlinear systems with discrete time measurements. Specifically, for systems with no process noise, the evolution of the state probability density function is governed by the Liouville equation. In general, solving the Liouville equation is computationally challenging. To this end, we leverage the method of characteristics to propagate probabilities along the characteristics solutions of the Liouville equation. Further, a convex optimization procedure is proposed to reconstruct the state probability density function from these characteristic solutions. Numerical examples of capturing the non-Gaussian nature of the uncertainty in the duffing oscillator and the two body problem are illustrated.

关键词： convex programming discrete time systems filtering theory Gaussian processes Liouville equation nonlinear dynamical systems probability Liouville equation Nonlinear dynamical systems Probability density function discrete time systems method of characteristics filtering theory convex programming Gaussian processes Probability Non-Gaussian Gaussian filter two body problem

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Game-Theoretic Precoding for Cooperative MIMO SWIPT Systems with Secrecy Consideration

Game-Theoretic Precoding for Cooperative MIMO SWIPT Systems ...

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IEEE Global Communications Conference

作者： Bing Fang Zuping Qian Wei Shao Wei Zhong Tinghui Yin College of Communications Engineering PLA University of Science and Technology

ISBN: (纸本)9781479959532

In this paper, we study the secrecy precoding problem for simultaneous wireless information and power transfer (SWIPT) in a multiple-input multiple-output (MIMO) relay network. The problem is formulated as a noncooperative game, where the network utility, defined as the nonnegative sum of the achievable secrecy rate and the harvested energy, is regarded as the common payoff, and the source and the relay are assumed as two rational game players. The formulated game is proven to be a potential game, which always processes at least one pure-strategy Nash equilibrium (NE), and the optimal transmit strategy profile that maximizes the network utility also constitutes a pure-strategy NE of it. Since the best-response problems of the proposed game constitute difference convex (DC)-type programming problems, we solve them by employing a successive convex approximation (SCA) method. With the SCA method, the two best-response problems can be iteratively solved through successive convex programming of their convexified versions. Then, based on the best-response dynamic, a distributed precoding algorithm is developed to obtain a feasible NE solution the proposed game. Numerical simulations are further provided to demonstrate it. Results show that our algorithm can converge fast to a near-optimal solution with guaranteed convergence.

关键词： MIMO precoding Physical layer security Energy harvesting Successive convex approximation Game theory Game theory energy harvesting Hunting Game animals Privacy prediction coding multiple input multiple output systems Physical layer Neon Nuclear Energy convex programming

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Age-Optimal Data Gathering and Energy Recharging of UAV in Wireless Sensor Networks

Age-Optimal Data Gathering and Energy Recharging of UAV in W...

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作者： Chen Zhang Juan Liu Lingfu Xie Xiaofan He School of Electrical Engineering and Computer Science Ningbo University School of Electronic Information Wuhan University

Unmanned aerial vehicle(UAV) can act as an aerial data collector to efficiently gather fresh information in wireless sensor networks(WSNs). To support continuous data collection, the battery-powered UAV should be able to obtain energy supplements in time. In this paper, we propose a joint data gathering and energy recharging framework for age minimization in UAV-enabled WSNs. To discuss the impacts of the deployments of ground terminals and charging stations on the network age, we assume that the UAV's action follows a stochastic policy. It flies from one terminal to another with some probability when its on-board energy is sufficient, otherwise flies to the nearest charging station to recharge its battery. The UAV-aided data collection problem with energy recharging is modeled as a non-linear optimization problem. With the aid of convex programming, a stochastic optimal policy is found to minimize the network peak age. Simulation results show that the network peak age performance is greatly affected by the intensities of the ground terminals and charging stations.

关键词： UAV AoI convex programming

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OPTIMIZING PORTFOLIO TAIL MEASURES: ASYMPTOTICS AND EFFICIENT SIMULATION OPTIMIZATION 08

OPTIMIZING PORTFOLIO TAIL MEASURES: ASYMPTOTICS AND EFFICIEN...

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Winter Simulation Conference (WSC 2008)

作者： Sandeep Juneja Tata Institute of Fundamental Research Mumbai India

ISBN: (纸本)9781424427086

We consider a portfolio allocation problem where the objective function is a tail event such as probability of large portfolio losses. The dependence between assets is captured through multi-factor linear model. We address this optimization problem using two broad approaches. We show that a suitably scaled asymptotic of the probability of large losses can be developed that is a simple convex function of the allocated resources. Thus, asymptotically, portfolio allocation problem is approximated by a convex programming problem whose solution is easily computed and provides significant managerial insight. We then solve the original problem using sample average simulation optimization. Since rare events are involved, naive simulation may perform poorly. To remedy this, we introduce change-of-variable based importance sampling technique and develop a single change of measure that asymptotically optimally estimates tail probabilities across the entire space of feasible allocations.

关键词： asymptotic BRIEFCASE simulating convex function original problem convex programming Objective function

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Switching Control Resource Allocation in Networked Control Systems

Switching Control Resource Allocation in Networked Control S...

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IEEE Annual Conference on Decision and Control

作者： Thais T. de Sousa Jose C. Geromel Grace S. Deaecto School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil School of Mechanical Engineering University of Campinas Campinas SP Brazil

ISBN: (纸本)9781479978878

This paper is devoted to H_2 state feedback sampled-data control design in the context of Networked Control Systems (NCS). Signals are transmitted through a limited bandwidth channel and a cooperative resource sharing dynamic strategy is proposed in order to control simultaneously several different LTI plants. The theory is based on the existence of a new class of switching functions assuring global asymptotic stability and guaranteed performance. Numerical and practical implementation aspects are analyzed and discussed in the framework of convex programming problems expressed by LMIs. The validity of the methodology is demonstrated by means of a simple example which consists on the simultaneous control of two linearized inverted pendulums.

关键词： Sampled-data control linear systems networked control systems Networked control systems Linear system data control INVERTED PENDULUM simultaneous control control systems convex programming Global Asymptotic Stability State feedback Resource utilization

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