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检索条件"主题词=Concave Programming"

共 311 条记录，以下是241-250 订阅

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Towards a dominant-polytope complexity reduction in Explicit MPC

Towards a dominant-polytope complexity reduction in Explicit...

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International Conference on Process Control (PC)

作者： Stevek, Juraj Kvasnica, Michal Fikar, Miroslav Slovak Univ Technol Bratislava Fac Chem & Food Technol Inst Informat Engn Automat & Math Bratislava 81237 Slovakia

ISBN: (纸本)9781479909261;9781479909278

In this paper a complexity reduction method of explicit MPC based on a dominant polytope is proposed. When a partitioning of explicit MPC consists of many small regions and a single big region it is reasonable to expect more frequent call of the control law associated with the big region than another ones during the control performance. Such an organization of the regions favors the online evaluation of explicit MPC. In the paper a problem to find a dominant polytope over the regions that share the same affine control law is studied. The problem of finding maximum volume polytope inscribed in nonconvex area is known as potato peeling problem in the literature. In this paper we focus to find a suboptimal solution by means of stochastic search.

关键词： computational complexity computational geometry concave programming piecewise constant techniques predictive control search problems stochastic processes suboptimal control

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Optimal Balanced Coordinated Network Resource Allocation Using Swarm Optimization

Optimal Balanced Coordinated Network Resource Allocation Usi...

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51st IEEE Annual Conference on Decision and Control (CDC)

作者： Hui, Qing Zhang, Haopeng Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA

ISBN: (纸本)9781467320665

In this research, we present a new balanced coordinated resource allocation algorithm in a network based on semistabilization theory for discrete-time linear systems with stochastic noise and compartmental modeling. Specifically, necessary and sufficient conditions for the convergence of the algorithm and for the existence of solutions based on the new notion of semiobservability to the proposed semistable Lyapunov equations are derived. With this theory, we first present a new framework to design balanced coordinated network resource allocation algorithms by means of a robust and optimal semistable control technique. Next, we convert this optimal control-based design problem into a constrained, nonconvex optimization problem. To solve this optimization problem, we propose a class of swarm optimization-based numerical algorithms called multiagent coordination optimization. Finally, numerical results are provided to validate the proposed design framework.

关键词： Lyapunov methods concave programming convergence networked control systems optimal control resource allocation robust control stochastic systems

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A Hybrid Method for Chance Constrained Control in Uncertain Environments

A Hybrid Method for Chance Constrained Control in Uncertain ...

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51st IEEE Annual Conference on Decision and Control (CDC)

作者： Vitus, Michael P. Tomlin, Claire J. Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781467320665

This paper introduces a novel hybrid method for solving a stochastic control problem for linear, Gaussian systems through uncertain environments. Due to the imperfect knowledge of the system state caused by motion, sensor and environment uncertainty, the system constraints cannot be guaranteed to be satisfied and consequently must be considered probabilistically. Due to the environmental uncertainty, the constraints are sums of products of random variables which do not have a closed-form analytical expression. Previous approaches have either approximated the distribution leading to a nonconvex optimization program, or used sampling alone to represent the uncertainty which requires a large number of samples to accurately represent the distribution. To address these limitations, a novel hybrid method is proposed that uses both analytical functions and sampling to represent the uncertainty. It is shown that under certain conditions, the resulting optimization program is convex. Also, this method drastically reduces the computational complexity over previous methods, which is demonstrated through an example.

关键词： Gaussian processes approximation theory computational complexity concave programming linear systems probability stochastic systems uncertain systems

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Full-Duplex MIMO Relaying: Achievable Rates under Limited Dynamic Range

Full-Duplex MIMO Relaying: Achievable Rates under Limited Dy...

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46th Asilomar Conference on Signals, Systems and Computers

作者： Day, Brian P. Margetts, Adam R. Bliss, Daniel W. Schniter, Philip Ohio State Univ Dept ECE Columbus OH 43210 USA

ISBN: (纸本)9781467350518

In this paper we consider the problem of full-duplex multiple-input multiple-output (MIMO) relaying between multi-antenna source and destination nodes. The principal difficulty in implementing such a system is that, due to the limited attenuation between the relay's transmit and receive antenna arrays, the relay's outgoing signal may overwhelm its limited-dynamic-range input circuitry, making it difficult-if not impossible-to recover the desired incoming signal. While explicitly modeling transmitter/receiver dynamic-range limitations and channel estimation error, we derive tight upper and lower bounds on the end-to-end achievable rate of decode-and-forward-based full-duplex MIMO relay systems, and propose a transmission scheme based on maximization of the lower bound. The maximization requires us to (numerically) solve a nonconvex optimization problem, for which we detail a novel approach based on bisection search and gradient projection. To gain insights into system design tradeoffs, we also derive an analytic approximation to the achievable rate and numerically demonstrate its accuracy.(1)

关键词： MIMO communication antenna arrays channel estimation concave programming decode and forward communication gradient methods receiving antennas relay networks (telecommunication) search problems transmitting antennas

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On Using Multi-State Spectrum Sensing for Joint Detection and Transmission in Opportunistic Spectrum Sharing

On Using Multi-State Spectrum Sensing for Joint Detection an...

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Tsai, Jiun-Shian Hsieh, Hung-Yun Natl Taiwan Univ Grad Inst Commun Engn Taipei 106 Taiwan

ISBN: (纸本)9781424492688

In this paper, we investigate the problem of opportunistic spectrum sharing through joint optimization of spectrum hole detection and transmission power control. Unlike conventional spectrum sensing models that classify the state of the primary user as being idle or active, we consider a multi-state spectrum sensing model where there exist multiple states to be chosen as the detection decision. Different states represent the degrees of certainty at the detector regarding the presence of the primary user, and hence are mapped to different levels of transmission powers to be used by the secondary user for spectrum access. Our goal thus is to jointly decide the optimal detection thresholds and transmission powers such that the achieved capacity at the secondary user is maximized subject to the interference temperature limit constraint at the primary user. We first formulate a non-convex optimization problem based on the system models and then propose an algorithm for solving the problem. By varying the number of states in the multi-state sensing model, we compare the performance of the conventional two-state hard sensing model and the infinite-state soft sensing model and motivate further investigation on multi-state spectrum sensing for dynamic spectrum access.

关键词： Detectors Equations Interference Joints Optimization Transmitters cognitive radio concave programming detection decision dynamic spectrum access infinite-state soft sensing model interference temperature limit constraint joint detection-transmission optimization multistate spectrum sensing model nonconvex optimization problem opportunistic spectrum sharing optimal detection thresholds power control primary user radiofrequency interference secondary user signal detection spectrum hole detection telecommunication control transmission power control two-state hard sensing model

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Rocking in Two by Two: From Collatz-Wielandt to Donsker-Varadhan

Rocking in Two by Two: From Collatz-Wielandt to Donsker-Vara...

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Information Theory and Applications Workshop (ITA)

作者： Anantharam, V. Borkar, V. S. Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Indian Inst Technol Dept Elect Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781479971954

We derive a variational formula for the optimal growth rate of reward in the infinite horizon risk-sensitive control problem for discrete time Markov decision processes with compact state and action spaces, extending a formula of Donsker and Varadhan for the Perron-Frobenius eigenvalue of a positive operator. This can be viewed as an abstract version of the Collatz-Wielandt formula for the Perron-Frobenius eigenvalue of a non-negative matrix. This leads to a concave maximization formulation of the problem of determining the optimal growth rate of risk-sensitive reward.

关键词： Markov processes concave programming eigenvalues and eigenfunctions infinite horizon matrix algebra variational techniques Perron-Frobenius eigenvalue concave maximization discrete time Markov decision process infinite horizon risk-sensitive control problem nonnegative matrix optimal growth rate variational formula Aerospace electronics Eigenvalues and eigenfunctions Extraterrestrial measurements Kernel Zinc Markov chain Eigenfunctions Extraterrestrial measurements avionics infinite horizon Calculus of variation matrix algebra Body Rocking Growth rate zinc Kernel

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Nonlinear Non-convex Optimization of Hydraulic Networks

Nonlinear Non-convex Optimization of Hydraulic Networks

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IEEE International Conference on Control Applications (CCA)

作者： Tahavori, Maryamsadat Kallesoe, Carsten Leth, John Wisniewski, Rafael Aalborg Univ Dept Elect Syst Fredrik Bajers Vej 7 DK-9220 Aalborg Denmark Grundfos Management AS Bjerringbro Denmark

ISBN: (纸本)9781479915590

Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption in pumps and also to regulate the pressure at the end-user valves to a desired value. The optimization problem which is solved is a nonlinear and non-convex optimization. The barrier method is used to solve this problem. The modeling framework and the optimization technique which are used are general. They can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users. The results in this paper show that the power consumption of the pumps is reduced.

关键词： concave programming hydraulic systems nonlinear programming pressure control pumps water supply

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Optimum Multi-user Detection by Nonsmooth Optimization

Optimum Multi-user Detection by Nonsmooth Optimization

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tuan, H. D. Son, T. T. Tuy, H. Nguyen, H. H. Univ New S Wales Sydney NSW 2071 Australia Toyota Technol Inst Nagoya Aichi 468 Japan Inst Matemat Hanoi 10307 Vietnam Univ Saskatchewan Saskatoon SK Canada

ISBN: (纸本)9781457705397

The optimum multiuser detection (OMD) is a discrete (binary) optimization. The previously developed approaches often relax it by a semi-definite program (SDP) and then employ randomization for searching the optimal solution around the solution of this relaxed SDP. In this paper, we show the limited capacity of this SDP program, which at the end cannot give a better solution than the simple linear minimum mean square error detector (LMMSE). Our departure point is to express the problem as quadratic minimization over quadratic equality constraint (QMQE) or concave quadratic minimization over a box of continuous optimization (CQOB). The QMQE allows us to develop a nonsmooth optimization algorithm to locate the global optimal solution of OMD, while CQOB facilities effective confirmation of the solutions found by QMQE. Our intensive simulation clearly shows that the algorithm outperforms all previously developed algorithms while the computational burden is essentially reduced.

关键词： Multiuser detection semi-definite programming concave programming nonsmooth optimization

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A Duality Study for Fading Multiple Access/Broadcast Channels Without Channel State Information at the Transmitter

A Duality Study for Fading Multiple Access/Broadcast Channel...

引用

IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Buehler, Joerg Wunder, Gerhard Tech Univ Berlin Heinrich Hertz Lehrstuhl Informat Theorie & Theor Einsteinufer 25 D-10587 Berlin Germany Fraunhofer German Sino Lab Mobile Commun D-10587 Berlin Germany

ISBN: (纸本)9781424456383

In this paper, we study duality relations for the fading broadcast channel (BC) under additive white Gaussian noise and a strict ordering of the fading distributions without channel state information at the transmitter and the natural corresponding dual fading multiple-access channel (MAC). We show that if the fading distribution for the weaker user is non-deterministic, the achievable rate region using superposition coding and successive decoding and Gaussian signaling, which is conjectured to be the capacity region, is different from the dual MAC region. Duality holds only in the case of one-sided fading, where the fading distribution for the weaker user is deterministic. Despite this lack of duality, we propose to use the dual MAC in order to approximatively solve a non-convex problem for the BC. Specifically, we consider the problem of weighted sum-rate optimization for the BC and give, under some assumptions, upper bounds on the error incurred using this procedure.

关键词： AWGN Gaussian signaling additive white Gaussian noise broadcast channels concave programming decoding duality study fading channels fading multiple access-broadcast channels nonconvex problem one-sided fading successive decoding superposition coding weigh

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Decentralized Multi-Cell Beamforming with Base Station Cooperation

Decentralized Multi-Cell Beamforming with Base Station Coope...

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10th IEEE Consumer Communications and Networking Conference (CCNC)

作者： Bose, Sayak Deshmukh, Siddharth Natarajan, Balasubramaniam Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA

ISBN: (纸本)9781467331333;9781467347358

This paper presents a design strategy for base station (BS) coordinated multi cell joint beamforming. Our objective is to maximize the average cell throughput while maintaining quality of service (QoS) for individual users. The QoS in our analysis relates to minimum signal to interference plus noise ratio (SINR) achieved by every user. Since, throughput maximization is a non convex problem, an optimal solution can not be obtained using polynomial order algorithms. However, it can be observed that existence of inter cell interference (ICI) terms are the cause of non convexity. With this knowledge, we propose two decentralized approaches based on primal and dual decomposition of the throughput maximization problem. As ICI terms are the shared variables between the base stations (BSs), these terms are exchanged between the neighboring BSs and kept fixed during an iteration. This fixes the non convex (ICI) terms, resulting in convex subproblems. We demonstrate the quality of our sub optimal decentralized solution by simulations and comparing it with optimal exhaustive search algorithms (Genie-aided algorithm). A comparison analysis is also presented for both primal and dual solutions.

关键词： array signal processing concave programming polynomials quality of service search problems

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