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A decomposition algorithm for a class of constrained nonsmooth convex optimization problems

A decomposition algorithm for a class of constrained nonsmoo...

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2016 IEEE Information Technology,Networking,Electronic and Automation Control Conference(ITNEC 2016)

作者： Dan Li Shuang Chen Fan-Yun Meng Information and Engineering College Dalian University School of Mathematical Sciences Dalian University of Technology

ISBN: (纸本)9781467391955

In this paper we propose a decomposition algorithm for solving a class of constrained nonsmooth convex optimization problems which constraints are another nonsmooth minimization *** original problem is approximately replaced by minimizing a parameterized family of functions instead of penalty *** family of these functions is the sum of two convex functions and we decompose the parameterized problem to two approximate subproblems into exploit the substructures of each *** is shown that the accumulation points of iterative sequence belong to the solution set of the original *** experiments validate the theoretical convergence analysis and illustrate the implementation of the decomposition algorithm.

关键词： nonlinear programming convex programming nonsmooth optimization proximal point methods alternating linearization algorithms

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A Parametric Multi-convex Splitting Technique with Application to Real-Time NMPC

A Parametric Multi-Convex Splitting Technique with Applicati...

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IEEE Annual Conference on Decision and Control

作者： Jean-Hubert Hours Colin N. Jones Laboratoire d'Automatique Ecole Polytechnique Federale de Lausanne (EPFL) CH-1015 Lausanne

ISBN: (纸本)9781467360890

A novel splitting scheme to solve parametric multi-convex programs is presented. It consists of a fixed number of proximal alternating minimisations and a dual update per time step, which makes it attractive in a real-time Nonlinear Model Predictive Control (NMPC) framework and for distributed computing environments. Assuming that the parametric program is semi-algebraic and that its critical points are strongly regular, a contraction estimate is derived and it is proven that the sub-optimality error remains stable under some mild assumptions. Efficacy of the method is demonstrated by solving a bilinear NMPC problem to control a DC motor. In particular, the effect of the sampling period on the optimality tracking error is analysed for a fixed computational power.

关键词： DC motors Parametric sampling interval nonlinear model predictive control convex programming splitting scheme computational power

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Linear three-phase power flow model for autonomous microgrid systems

Linear three-phase power flow model for autonomous microgrid...

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CIRED 2020 Berlin Workshop (CIRED 2020)

作者： Youthanalack Vilaisarn Morad Abdelaziz Jerome Cros Laval University Quebec Canada University of British Columbia Kelowna Canada

In this work, the authors propose a linear three-phase power flow model for droop-controlled autonomous microgrids. The proposed linear power flow model adopts a set of linear algebraic equations and can, with small errors, be compared to the detailed nonlinear model, account for the lack of a slack bus, the variability of autonomous microgrid frequency and the operation of distributed generation units in droop control mode. The proposed linear model can as such provide a fast and accurate power flow solution and has great potential in the stochastic planning and online operational applications of autonomous microgrids, as well as in linear or convex optimal power flow and probabilistic load flow analysis. The accuracy and effectiveness of the proposed linear power flow model are validated through different case studies considering balanced and unbalanced test systems.

关键词： load flow control distributed power generation stochastic processes convex programming

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Wireless Powered Communication Networks Aided by an Unmanned Aerial Vehicle

Wireless Powered Communication Networks Aided by an Unmanned...

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IEEE Vehicular Technology Conference

作者： Junhee Park Hoon Lee Subin Eom Inkyu Lee Sch. of Electr. Eng. Korea Univ. Seoul South Korea Inf. Syst. Technol. & Design Pillar Singapore Univ. of Technol. & Design Singapore Singapore

ISBN: (纸本)9781538663592;9781538663585

This paper investigates an unmanned aerial vehicle (UAV)-aided wireless powered communication network where a mobile hybrid access point serves multiple energy-constrained ground terminals (GTs) in terms of wireless energy transfer and data collection. Specifically, to support information transmission of the GTs, the mobile UAV first transfers wireless energy in the downlink. Then, by harvesting this wireless energy, the GTs transmit their uplink information signals to the UAV in a time division multiple access manner. In this system, we jointly optimize the trajectory of the UAV and the uplink power control policy in order to maximize the minimum throughput of the GTs. By applying the concave-convex procedure, we propose an iterative algorithm which efficiently identifies a locally optimal solution. Simulation results verify the efficiency of the proposed algorithm compared to conventional schemes.

关键词： autonomous aerial vehicles concave programming convex programming energy harvesting iterative methods mobile radio power control radiofrequency power transmission telecommunication control telecommunication power management telecommunication power supplie Communication and control networks(communications) Mobile radios convex programming drone honey bees Unmanned Aerial Vehicle iterative methods energy harvesting Power control wireless vehicle time division multiple access data transmission

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Power supply capability evaluation of AC/DC hybrid distribution network considering charging-swapping-storage integrated station

Power supply capability evaluation of AC/DC hybrid distribut...

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2021 Annual Meeting of CSEE Study Committee of HVDC and Power Electronics (HVDC 2021)

作者： H. Yuan East China Electric Power Design Institute Huangpu District Shanghai China

With the development of DC distribution technology, electric vehicles and renewable energy will be more efficient access to the grid. In this paper, a model of power supply capability evaluation is proposed, which takes the electric vehicle chargingswapping-storage integrated station and AC/DC hybrid distribution network into account. Firstly, the battery charging station model is built based on the driving behavior characteristics of electric vehicle fast charging users, and then the model of battery swapping station is established combined with urban road traffic model. Finally, the model of electric vehicle charging-swapping-storage integrated station is completed by combining the model of energy storage station. In the AC/DC hybrid distribution network with wind turbine, photovoltaic and integrated station, a power supply capability evaluation model is established. The model is a nonlinear nonconvex mixed integer programming problem, which could be transformed into a mixed integer second-order cone convex optimization problem. The model could be efficiently solved by commercial solver by using linearization and second-order cone relaxation techniques. Taking the modified IEEE33 test system as an example, the effectiveness and rationality of the proposed model are verified by combining with the actual road traffic situation in a certain area.

关键词： convex programming integer programming battery storage plants battery powered vehicles distribution networks road traffic electric vehicle charging

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Low-rank Tensor Completion with a New Tensor Nuclear Norm Induced by Invertible Linear Transforms

Low-rank Tensor Completion with a New Tensor Nuclear Norm In...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Canyi Lu Xi Peng Yunchao Wei Carnegie Mellon University Sichuan University University of Illinois at Urbana-Champaign

ISBN: (纸本)9781728132945

This work studies the low-rank tensor completion problem, which aims to exactly recover a low-rank tensor from partially observed entries. Our model is inspired by the recently proposed tensor-tensor product (t-product) based on any invertible linear transforms. When the linear transforms satisfy certain conditions, we deduce the new tensor tubal rank, tensor spectral norm, and tensor nuclear nor-m. Equipped with the tensor nuclear norm, we then solve the tensor completion problem by solving a convex program and provide the theoretical bound for the exact recovery under certain tensor incoherence conditions. The achieved sampling complexity is order-wise optimal. Our model and result greatly extend existing results in the low-rank matrix and tensor completion. Numerical experiments verify our results and the application on image recovery demonstrates the superiority of our method.

关键词： Tensor rank of matrix convex programming Completion Transform Reversibility

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Stabilization of sampled-data T-S fuzzy systems

Stabilization of sampled-data T-S fuzzy systems

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Systems and Control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on

作者： Huijun Gao Tongwen Chen Space Control and Inertial Technology Research Center Harbin Institute of Technology China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (纸本)0780393953

This paper investigates the problem of stabilization for a Takagi-Sugeno (T-S) fuzzy system with nonuniform uncertain sampling. The sampling is not required to be periodic, and the only assumption is that the distance between any two consecutive sampling instants is less than a given bound. By using the input delay approach, the T-S fuzzy system with variable uncertain sampling is transformed into a continuous-time T-S fuzzy system with a delay in the state. A new condition guaranteeing asymptotic stability of the closed-loop sampled-data system is derived by a Lyapunov approach plus the free weighting matrix technique. Based on this stability condition, two procedures for designing state-feedback control laws are given: one casts the controller design into a convex optimization by introducing some over design, and the other utilizes the cone complementarity linearization (CCL) idea to cast the controller design into a sequential minimization problem subject to linear matrix inequality (LMI) constraints, which can be readily solved using standard numerical software.

关键词： Lyapunov matrix equations asymptotic stability closed loop systems controllers convex programming fuzzy systems linear matrix inequalities minimisation state feedback Lyapunov approach Takagi-Sugeno fuzzy system asymptotic stability closed-loop sampled-d

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ROBUST ADAPTIVE BEAMFORMING BASED ON MULTI-DIMENSIONAL COVARIANCE FITTING

ROBUST ADAPTIVE BEAMFORMING BASED ON MULTI-DIMENSIONAL COVAR...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Michael Rubsamen Alex B. Gershman Communication Systems Group Technische Universitat Darmstadt Merckstr. 25 64283 Darmstadt Germany

ISBN: (纸本)9781424442959

Robust adaptive beamforming based on worst-case performance optimization is known to provide a substantially improved robustness against signal self-nulling as compared to the traditional adaptive beamforming techniques. The worst-case performance optimization based beamformers of [1] and [2] can be alternatively obtained by solving the one-dimensional (1D) covariance fitting problem [3]. In this paper, we show that the robustness of this approach can be significantly improved by extending it to multi-dimensional (MD) covariance fitting.

关键词： Robust adaptive beamforming multi-dimensional covariance fitting difference of convex programming Adaptive Beam forming Covariance convex programming MULTIDIMENSIONAL MACHINE SHOP PRACTICE beamformers Robustness

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convex Relaxations for Nonconvex Optimal Control Problems

Convex Relaxations for Nonconvex Optimal Control Problems

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IEEE Conference on Decision and Control

作者： Joseph K. Scott Paul I. Barton Process Systems Engineering Laboratory Department of Chemical Engineering MIT Cambridge MA 02139 USA

ISBN: (纸本)9781612848006

An optimal control problem with a nonlinear control system embedded is considered. Using the endpoint map of the control system, such problems can be written as nonlinear programs on the set of admissible controls. Though necessary optimality conditions exist for such problems, they are often nonconvex and such conditions are not sufficient. A relaxation procedure is outlined which generates a convex program whose solution value is a guaranteed lower bound on the solution value of the original problem. This result is a crucial step towards developing deterministic global optimization techniques for optimal control problems using a branch-and-bound framework. The major contribution is that, unlike other developments along these lines, the convex underestimating program derived here is valid on the original function space;i.e. there is no need to discretize the control.

关键词： Optimal control control systems convexity Nonlinear programming Nonlinear control systems Function spaces convex programming

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Multiple illumination phaseless super-resolution (MIPS) with applications to phaseless DoA estimation and diffraction imaging

Multiple illumination phaseless super-resolution (MIPS) with...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Fariborz Salehi Kishore Jaganathan Babak Hassibi Department of Electrical Engineering California Institute of Technology Pasadena United States of America

ISBN: (纸本)9781509041183

Phaseless super-resolution is the problem of recovering an unknown signal from measurements of the "magnitudes" of the "low frequency" Fourier transform of the signal. This problem arises in applications where measuring the phase, and making high-frequency measurements, are either too costly or altogether infeasible. The problem is especially challenging because it combines the difficult problems of phase retrieval and classical super-resolution. Recently, the authors in [1] demonstrated that by making three phaseless low-frequency measurements, obtained by appropriately "masking" the signal, one can uniquely and robustly identify the phase using convex programming and obtain the same super-resolution performance reported in [2]. However, the masks proposed in [1] are very specific and in many applications cannot be directly implemented. In this paper, we broadly extend the class of masks that can be used to recover the phase and show how their effect can be emulated in coherent diffraction imaging using multiple illuminations, as well as in direction-of-arrival (DoA) estimation using multiple sources to excite the environment. We provide numerical simulations to demonstrate the efficacy of the method and approach.

关键词： Super-resolution phase-retrieval direction-of-arrival diffraction imaging semidefinite relaxation super-resolution Fourier Transform Phase illumination Masks diffraction convex programming

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