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IEEE International Conference on Image Processing

作者： Xianghua Ying Xiang Mei Sen Yang Ganwen Wang Hongbin Zha Center for Inf. Sci. Peking Univ. Beijing China

ISBN: (纸本)9781479957521

In Hartley-Kang's paper [7], they directly treated a planar calibration pattern as an image to construct an image pair together with a radial distorted image of the planar calibration pattern, and then proposed a very efficient method to determine the center of radial distortion by estimating the epipole in the radial distorted image. After determined the center of radial distortion, a least square method was utilized to recover the radial distortion function using the monotonicity constraints. In this paper, we present a convex optimization method to recover the radial distortion function using the same constraints as those required by Hartley-Kang's method, whereas our method can obtain better results of radial distortion correction. The experiments validate our approach.

关键词： Hartley transforms convex programming image denoising least squares approximations Hartley-Kang method convex optimization epipole estimation least square method monotonicity constraint planar calibration pattern image radial distortion correction radial distortion function recovery Calibration Cameras Computational modeling Computer vision convex functions Least squares methods Lenses Radial distortion camera calibration distortion correction distortion correction convex function Least squares method images Hartley transform least squares approximations convex programming camera calibration image denoising Computational modeling Cameras computer vision Radial Lenses Imagery (Psychotherapy)

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Energy Balance Based Power Generation Scheduling of Microgrid with Nonanticipativity

Energy Balance Based Power Generation Scheduling of Microgri...

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第三十九届中国控制会议

作者： Zhao JieXing Zhai QiaoZhu Miao HongYi Systems Engineering Institute MOE KLINNS Lab and SKLMS Lab Xi’an Jiaotong University

Optimal scheduling of the power generation system is of great importance to the security and reliability of the power grid. Microgrid can reduce operating costs through reasonable economic dispatch. At present, related research mainly uses the power balance constraint based on the discrete-time model, which couldn’t exactly model the integral relationship between electrical energy and power output rate of generators. A scheduling formulation is established with consideration of integral constraints and nonanticipativity in this paper. With detailed analyzing the original model, a three-stage numerical solution method is proposed. By solving convex programming, in most instances, the global optimal solution can be obtained efficiently. The optimal schedule in the sense of expectation can be obtained with the real-time load demand satisfied and nonanticipativity guaranteed. Numerical testing for autonomous power plants of a big steel enterprise is performed and the effectiveness of the method is verified.

关键词： optimal scheduling microgrid integral constraints nonanticipativity convex programming

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Observer design for systems with an energy preserving nonlinearity, with application to fluid flow

Observer design for systems with an energy preserving nonlin...

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IEEE Conference on Decision and Control

作者： Andrew Wynn Paul Goulart Department of Aeronautics Imperial College London Prince Consort Road London SW7 2AZ United Kingdom

ISBN: (纸本)9781612848006

In this paper observer design is considered for a class of non-linear systems whose non-linear part is energy preserving. Examples of such systems arise from considering finite dimensional approximations of fluid flows. A strategy to construct convergent observers for this class of non-linear system is presented. The approach has the advantage that it is possible, via convex programming, to prove whether the constructed observer converges, in contrast to several existing approaches to observer design for non-linear systems. Finally the method is used to produce a globally convergent observer for the Lorenz attractor.

关键词： Lorenz attractor fluid flow convex programming nonlinearity Energy sources Fluid

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Interior point method for sparse recovery space time adaptive processing

Interior point method for sparse recovery space time adaptiv...

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IET International Radar Conference (IET IRC 2020)

作者： N. Cui Z. Yu K. Xing Aerospace Information Research Institute Beijing China

convex optimization is one of the most common method for solving the problem of sparse recovery space-time adaptive processing (SR-STAP), however, most SR-STAP methods are focusing on the construction of problem model and they usually directly search the optimal solution by off-the-shelf software such as CVX. This way ignores the realization of convex optimization algorithms and makes an obstacle to the practical application. This paper proposed a general process of convex optimization algorithm for SR-STAP based on the interior-point method (IPM). It can be separated into two steps, first is to transform the SR-STAP problem to the second order cone programming (SOCP) problem, second is to solve above problem by IPM. The numerical simulation experiments illustrate that the proposed method can effectively recover clutter covariance matrix (CCM), moreover this structure can be easily extended to other convex optimization methods.

关键词： convex programming radar clutter covariance matrices space-time adaptive processing numerical analysis

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From weak to strong LP gaps for all CSPs 17

From weak to strong LP gaps for all CSPs

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Proceedings of the 32nd Computational Complexity Conference

作者： Mrinalkanti Ghosh Madhur Tulsiani Toyota Technological Institute at Chicago

ISBN: (纸本)9783959770408

We study the approximability of constraint satisfaction problems (CSPs) by linear programming (LP) relaxations. We show that for every CSP, the approximation obtained by a basic LP relaxation, is no weaker than the approximation obtained using relaxations given by [EQUATION] levels of the Sherali-Adams hierarchy on instances of size *** was proved by Chan et al. [FOCS 2013] (and recently strengthened by Kothari et al. [STOC 2017]) that for CSPs, any polynomial size LP extended formulation is no stronger than relaxations obtained by a super-constant levels of the Sherali-Adams hierarchy. Combining this with our result also implies that any polynomial size LP extended formulation is no stronger than simply the basic LP, which can be thought of as the base level of the Sherali-Adams hierarchy. This essentially gives a dichotomy result for approximation of CSPs by polynomial size LP extended *** our techniques, we also simplify and strengthen the result by Khot et al. [STOC 2014] on (strong) approximation resistance for LPs. They provided a necessary and sufficient condition under which Ω(log log n) levels of the Sherali-Adams hierarchy cannot achieve an approximation better than a random assignment. We simplify their proof and strengthen the bound to [EQUATION] levels.

关键词： linear programming hierarchy integrality gap convex programming constraint satisfaction problem

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The Sum-Rate Maximization Precoding for Multiuser MIMO SWIPT Systems

The Sum-Rate Maximization Precoding for Multiuser MIMO SWIPT...

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IEEE Vehicular Technology Conference

作者： Zhaohui Yue Hui Gao Ruohan Cao Tiejun Lv Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781479980895

This paper proposes a linear precoding that aims at maximizing the sum-rate in a multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. In this scenario some receivers harvest energy while the others decode information at the same time. The sum-rate for information decoding users is considered as the optimization policy where the transmit power constraint and power harvesting per user constraints are taken into account. Since the objective function of the sum-rate maximization problem is non-convex, it is difficult to find the optimal solution. Thus, we propose an iterative algorithm to find a locally optimal design based on a sequential convex approximation (SCA) method. In this way, the non-convex optimization problem is approximated by a convex program at each iteration. Finally, with the aid of the concept of the Rate-Energy (R-E) region simulation results show that the proposed scheme achieves significant improvement compared to existing works.

关键词： Non-convex prediction coding multiple input multiple output systems Transmitter power convex programming Optimal design DECODING Iterative algorithms Objective function convexity

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一种基于Peaceman-Rachford分裂算法的前景提取方法

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高等学校计算数学学报 2019年第3期41卷 277-288页

作者：怀开展倪明放高洁陆军工程大学研究生院吉林大学珠海学院

1引言快速准确地从视频流中提取背景和运动前景是计算机视觉领域一个重要的热门研究课题,特别是在视频监视、交通监控系统等实际应用中.然而,运动前景的不断变化使得求解这个问题变得很困难.许多学者对此问题进行了深入的研究,并提出了... 详细信息

1引言快速准确地从视频流中提取背景和运动前景是计算机视觉领域一个重要的热门研究课题,特别是在视频监视、交通监控系统等实际应用中.然而,运动前景的不断变化使得求解这个问题变得很困难.许多学者对此问题进行了深入的研究,并提出了一些行之有效的方法,其中Candes等人[1]指出可以将前景提取问题刻画为主成分追踪（Principal Component Pursuit）模型·主成分追踪模型可以简单地描述为:已知观测矩阵M∈Rm×n(由

关键词： Foreground extraction Peaceman-Rachford splitting method convex programming

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Auction Algorithm for Nonlinear Resource Allocation Problems

Auction Algorithm for Nonlinear Resource Allocation Problems

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2010 49th IEEE Conference on Decision and Control

作者： Ajay Kumar Bangla David A. Castanon Dept of Electrical & Computer Eng. Boston University

ISBN: (纸本)9781424477456

We study the problem of optimally assigning N divisible resources to M competing missions/tasks. We call this the Nonlinear Resource Allocation Problem (RAP). This simple yet powerful framework has found applications in diverse fields such as search theory, statistics, finance, economics, logistics, sensor and wireless networks. RAP is an instance of convex program but it has a bipartite structure like the assignment/transportation problem. In this paper, we propose a new algorithm, RAP Auction, for this problem which finds a near optimal solution in finite time. Unlike most existing methods in literature, we do not presuppose a particular cost function or assume differentiability or strict convexity. RAP Auction works for any monotonic convex cost function. It has a very simple computation structure amenable to distributed implementation.

关键词： Cost functions Resource utilization Differentiable optimal solution management of resources convexity convex programming

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FREQUENCY-DOMAIN BLIND SPEECH SEPARATION USING INCOMPLETE DE-MIXING TRANSFORM

FREQUENCY-DOMAIN BLIND SPEECH SEPARATION USING INCOMPLETE DE...

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European Signal Processing Conference

作者： Zbynek Koldovsky Francesco Nesta Petr Tichavsky Nobutaka Ono Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Conexant System Institute of Information Theory and Automation National Institute of Informatics (NII) SOKENDAI (The Graduate University for Advanced Studies)

ISBN: (纸本)9781509018918

We propose a novel solution to the blind speech separation problem where the de-mixing transform is estimated only within selected frequency bins. This solution is based on Independent Vector Analysis applied to a subset of instantaneous mixtures, one per selected frequency bin. Next, two approaches are proposed to complete the transform: one based on null beamforming, and the other based on convex programming. In subsequent experiments, we compare combinations of both methods and evaluate their ability to retrieve the whole de-mixing transform. Depending on the number of selected frequencies and the sparsity of room impulse responses, the methods show improvements in terms of computational complexity as well as in terms of separation accuracy.

关键词： Blind Source Separation Independent Vector Analysis Relative Transfer Function Sparse Reconstruction convex Optimization Blind source separation convex function Speech Frequency-domain analysis Vector Analysis Transform convex programming separation complexity classes

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A QCQP and SDP Formulation of the Optimal Power Flow Including Renewable Energy Resources

A QCQP and SDP Formulation of the Optimal Power Flow Includi...

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IEEE International Symposium on Systems Engineering (ISSE)

作者： Juan Carlos Bedoya Ahmed Abdelhadi Chen-Ching Liu Anamika Dubey Power and Energy Center (PEC) Virginia Polytechnic Institute of Technology VA USA Engineering Technology Department University of Houston Houston TX USA Energy Systems Innovation Center from the School of Electrical Engineering & Computer Science Washington State University Pullman WA USA

The optimal power flow (OPF) problem is a relevant subject for the secure and economic power systems operation. For instance, OPF can be used to reduce the power system technical losses. A reduction in 0.1% in the losses accounts for near 50 billion USD in cost savings. Likewise, OPF can be used along with power production forecast tools, for renewable energy sources, to evaluate their impact in the grid security operation planning. In an OPF problem, an objective function related with demand supply generation cost, power line losses, or violation limits is optimized subject to several system security constraints. These constraints are related with branch power flow limits, voltages limits, power injection limits, among others. The exact model of the objective function and constraints of the OPF problem is neither linear nor convex. In this paper it is presented the OPF problem in a Quadratically Constrained Quadratic Program (QCQP) approach. Objective function is based on a quadratic function of the bus voltages and constraints are formulated as Quadratic forms. A two-bus system is used to demonstrates the non-convexity of the OPF problem. Also, it is presented the Rank- 1 convex relaxation of the OPF problem which relaxes the QCQP model into a positive Semidefinite programming (SDP) model. Once the OPF problem is relaxed into a SDP convex form, a global optimal solution can be obtained. An application example for the OPF problem is presented for the IEEE 14 system. The QCQP and the SDP results are compared and discussed.

关键词： convex programming load flow quadratic programming renewable energy sources

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