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International Conference on Electronics Technology (ICET)

作者： Lintao Ding Chenguang Shi Kang Chen Jianjiang Zhou Key Laboratory of Radar Imaging and Microwave Photonics Nanjing Univ. Aeronaut. Astronaut Nanjing China Marine Design & Research Institute of China Shanghai China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

In this paper, a joint transmit and receive beampattern design (JTRBD) strategy is developed for multi-target tracking (MT) in colocated multiple-input multiple-output (CMIMO) radar system under hostile environment. The key mechanism of the JTRBD strategy can be divided into two successive iteration optimization stages. First, the C-MIMO radar optimizes the transmit waveform correlation matrix (WCM) to form a low peak sidelobe level (PSL) and resource-awarebased beampattern, thereby minimizing the MT error and the probability of interception. Second, the radar system designs the weights of the spatial filters and employs digital beamforming techniques to generate corresponding receive beampatterns, so as to reduce the gain in the direction of oppressive interference. Due to the coupling of the adaptable parameters in both the objective function and constraints, the resultant problem is nonconvex and NP-hard, and therefore, an iterative solution scheme based on semidefinite programming (SDP) algorithm and the nonmonotone spectral projected gradient (NSPG) method is devised. Simulation results illustrate the effectiveness of the JTRBD strategy.

关键词： Measurement semidefinite programming Simulation Radar Radar tracking Spatial filters Iterative algorithms

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Joint 2-D DOA Estimation Using Gridless Sparse Method

Joint 2-D DOA Estimation Using Gridless Sparse Method

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Longfei Xiang Qinghua Huang Lin Zhang Kai Liu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University

A novel gridless sparse method(GSM) is proposed to estimate two-dimensional(2-D) direction-of-arrival(DOA)using L-shaped *** angular space is transformed to a frequency one and a new model is constructed in the frequency *** on the new model,the covariance matrix is reparameterized by a positive semidefinite Toeplitz *** fitting criterion and semidefinite programming are used to estimate DOAs in the continuous *** with traditional 2-D DOA estimation methods,there is no need to discretize the whole angular space which can cause modeling error and increase computation ***,the proposed method can get pair-matching automatically.

关键词： Gridless sparse method 2-D DOA estimation Lshaped arrays semidefinite programming pair-matching

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Optimal algorithms and inapproximability results for every CSP? 08

Optimal algorithms and inapproximability results for every C...

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Proceedings of the fortieth annual ACM symposium on Theory of computing

作者： Prasad Raghavendra University of Washington Seattle WA USA

ISBN: (纸本)9781605580470

semidefinite programming(SDP) is one of the strongest algorithmic techniques used in the design of approximation algorithms. In recent years, Unique Games Conjecture(UGC) has proved to be intimately connected to the limitations of semidefinite *** this connection precise, we show the following result : If UGC is true, then for every constraint satisfaction problem(CSP) the best approximation ratio is given by a certain simple SDP. Specifically, we show a generic conversion from SDP integrality gaps to UGC hardness results for every CSP. This result holds both for maximization and minimization problems over arbitrary finite *** this connection between integrality gaps and hardness results we obtain a generic polynomial-time algorithm for all CSPs. Assuming the Unique Games Conjecture, this algorithm achieves the optimal approximation ratio for every ***, for all 2-CSPs the algorithm achieves an approximation ratio equal to the integrality gap of a natural SDP used in literature. Further the algorithm achieves at least as good an approximation ratio as the best known algorithms for several problems like MaxCut, Max2Sat, MaxDiCut and Unique Games.

关键词： unique games conjecture rounding schemes constraint satisfaction problem semidefinite programming dictatorship tests

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Reducing the Computational Cost of the Sum-of-Squares Stability Test for Time-Delayed Systems

Reducing the Computational Cost of the Sum-of-Squares Stabil...

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American Control Conference

作者： Yashun Zhang Matthew Peet Keqin Gu School of Automation Nanjing University of Science and Technology China Department of Mechanical Materials and Aerospace Engineering Illinois Institute of Technology USA Department of Mechanical and Industrial Engineering Southern Illinois University Edwardsville USA

ISBN: (纸本)9781424474264

This paper considers the problem of reducing the computational complexity associated with the Sum-of-Squares approach to stability analysis of time-delay systems. Specifically, this paper considers systems with a large state-space but with relatively few delays-- the most common situation in practice. The paper uses the general framework of coupled differential-difference equations with delays in low-dimensional feedback channels. This framework includes both the standard delayed and neutral-type systems. The approach is based on recent results which introduced a new type of Lyapunov-Krasovskii form which was shown to be necessary and sufficient for stability of this class of systems. This paper shows how exploiting the structure of the new functional can yield dramatic improvements in computational complexity. Numerical examples are given to illustrate this improvement.

关键词： Lyapunov-Krasovskii Time delay semidefinite programming Sum-of-Squares Complexity

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Position estimation of underwater nodes based on relative error

Position estimation of underwater nodes based on relative er...

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第33届中国控制与决策会议

作者： RuomaoYan YajieMa ShaowuLu YiHu FengxingZhou Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Faculty of Intelligent Manufacturing Wuyi University

In recent years,the scheme based on Received Signal Strength(RSS) has attracted wide attention in sensor nodes positioning due to its advantage of low cost and lack of *** this paper,a positioning model is built based on RSS for underwater wireless sensor networks,and the Least Square Relative Error(LSRE) estimation method is adopted to solve the problem of semidefinite programming of prior constraints when the transmission power is ***,the formula of underwater acoustic path loss is approximate to pseudo linear multiplication model by mathematical *** a nonconvex LSRE problem with the node position and transmission power as variables is established with this ***,a matrix containing compound variables is constructed by using the ascending dimension relaxation technique of semidefinite *** on the external characteristics of compound variables,prior constraints are added to solve the convex optimization *** results show that this algorithm has higher estimation accuracy than that based on absolute error estimation.

关键词： Underwater Sensor Networks Positioning Received Signal Strength Relative Error semidefinite programming

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Docking with PIPER and refinement with SDU in rounds 6-11 of CAPRI

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2007年第4期69卷 734-742页

作者： Shen, Yang Brenke, Ryan Kozakov, Dima Comeau, Stephen R. Beglov, Dmitri Vajda, Sandor Boston Univ Dept Biomed Engn Boston MA 02215 USA Boston Univ BioMol Engn Res Ctr Boston MA 02215 USA Boston Univ Program Syst Engn Boston MA 02215 USA Boston Univ Program Bioinformat Boston MA 02215 USA Dyax Corp Boston MA USA

Our approach to protein-protein docking includes three main steps. First we run PIPER, a new rigid body docking program. PIPER is based on the Fast Fourier Transform (FFT) correlation approach that has been extended to use pairwise interactions potentials, thereby substantially increasing the number of near-native structures generated. The interaction potential is also new, based on the DARS (Decoys As the Reference State) principle. In the second step, the 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. Third, the conformations are refined by a new medium-range optimization method SDU (Semi-Definite programming based Underestimation). SDU has been developed to locate global minima within regions of the conformational space in which the energy function is funnel-like. The method constructs a convex quadratic underestimator function based on a set of local energy minima, and uses this function to guide future sampling. The combined method performed reliably without the direct use of biological information in most CAPRI problems that did not require homology modeling, providing acceptable predictions for targets 21, and medium quality predictions for targets 25 and 26.

关键词： fast Fourier transform clustering energy funnel protein docking by PIPER refinement by SDU global optimization semidefinite programming DARS pairwise potential

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Convexity Results on Derivative of Negative Fisher Information Along Heat Flow

Convexity Results on Derivative of Negative Fisher Informati...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Ken Chen Yanlin Geng School of Telecommunications Engineering Xidian University Xi'an China State Key Laboratory of ISN Xidian University Xi'an China

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

Recently, it was shown that the Fisher information is log-convex along the heat flow. The main tools involved were the Cauchy-Schwarz inequality and clever observations on the sum of squares. In this work, we reformulate their method as a rank-one constrained semidefinite programming problem. Then we show that the rank-one matrix can be determined through investigating the first diagonal entry. We apply this new approach to recover existing results, as well as to obtain new results on the derivative of the negative Fisher information along the heat flow: the derivative is convex if it is raised to the power of three-eighths, and is log-convex if the input distribution is log-concave.

关键词： semidefinite programming Conferences Linear matrix inequalities Heat recovery Information theory

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Multibeam Beamforming Design in RIS-aided SISO Communications Subject to Reradiation Masks – Optimization and Machine Learning Methods

Multibeam Beamforming Design in RIS-aided SISO Communication...

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International Symposium on Wireless Communication Systems (ISWCS)

作者： Shumin Wang Marco Di Renzo Marios Poulakis CentraléSupelec Laboratoire des Signaux et Systèmes Université Paris-Saclay CNRS Gif-sur-Yvette France Huawei Technologies Sweden AB Kista Sweden

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

Reconfigurable intelligent surfaces (RISs) are considered as one of the most promising technologies to reduce the power consumption and improve the spectral efficiency in future wireless systems. In this paper, we investigate the design of a two-user RIS-aided single-input single-output communication system and propose a beamforming design for RIS optimization, which accounts for specified reradiation masks at the design stage. First, we formulate a max-min optimization problem that aims to maximize the minimum of the normalized powers received at the two users, and we solve it by using semidefinite programming. Then, we devise an efficient implementation of the proposed algorithm based on neural networks. The obtained simulation results show that the proposed schemes are capable of shaping the desired dual-beam radiation pattern towards the directions of interest, while fulfilling the imposed reradiation masks towards other directions. The implementation based on neural networks is shown to reduce the execution time.

关键词： Wireless communication semidefinite programming Power demand Array signal processing Spectral efficiency Simulation Neural networks

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Sparsity-promoting optimal control of consensus and synchronization networks

Sparsity-promoting optimal control of consensus and synchron...

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American Control Conference

作者： Xiaofan Wu Mihailo R. Jovanovic Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN 55455

ISBN: (纸本)9781479932757

We study a class of optimal control problems that are encountered in consensus and synchronization networks. These are characterized by structural constraints that arise from the lack of the absolute measurements for a part of the state vector. In order to deal with these constraints, we introduce a coordinate transformation to eliminate the average mode and assure stabilizability and detectability of the remaining modes. To design networks with low communication requirements, we seek solutions to the l_1 regularized version of the standard H_2 optimal control problem. Such solutions trade off network performance and sparsity of the controller. We also identify a class of optimal control problems that can be cast as semidefinite programs. Examples from power systems and consensus networks are provided to illustrate our developments.

关键词： Alternating direction method of multipliers consensus power systems semidefinite programming sparsity-promoting optimal control synchronization wide-area control Optimal control power systems Coordinate transformations Network absolute measurement terminal area control

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Performance of noisy Nesterov's accelerated method for strongly convex optimization problems

Performance of noisy Nesterov's accelerated method for stron...

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American Control Conference

作者： Hesameddin Mohammadi Meisam Razaviyayn Mihailo R. Jovanovic Department of Electrical and Computer Engineering University of Southern California Los Angeles CA 90089

ISBN: (纸本)9781538679012;9781538679265

We study the performance of noisy gradient descent and Nesterov's accelerated methods for strongly convex objective functions with Lipschitz continuous gradients. The steady-state second-order moment of the error in the iterates is analyzed when the gradient is perturbed by an additive white noise with zero mean and identity covariance. For any given condition number κ, we derive explicit upper bounds on noise amplification that only depend on κ and the problem size. We use quadratic objective functions to derive lower bounds and to demonstrate that the upper bounds are tight up to a constant factor. The established upper bound for Nesterov's accelerated method is larger than the upper bound for gradient descent by a factor of √κ. This gap identifies a fundamental tradeoff that comes with acceleration in the presence of stochastic uncertainties in the gradient evaluation.

关键词： Accelerated first-order algorithms control for optimization convex optimization integral quadratic constraints linear matrix inequalities Nesterov's method noise amplification second-order moments semidefinite programming Upper bound linear matrix inequalities second moment Convex function control optimization Objective function Additive white noise zero mean descent Gradient

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