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Design of Perfect-Reconstruction Hybrid Filter Banks: A second-order cone programming Approach

Design of Perfect-Reconstruction Hybrid Filter Banks: A Seco...

2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： ZhenHao Cai HongYi Yu Institute of Zhengzhou Information Science and Technology Zhengzhou 450002 P.R.China

ISBN: (纸本)9781424465828

In this paper, we present a new convex programming design methodology for hybrid filter banks (HFB) by solving a linear equation system derived from the perfect reconstruction (PR) conditions. The relationship between the PR conditions and each different solution situations are discussed regarding the consistent and inconsistent equation case, the general solution and the lower bound to the error of reconstruction are given. A second order cone programming (SOCP) based algorithms for designing PR HFB is proposed to get the exact solution with the constrained of stopband energy minimum which can also be used to calculate the minimizing norm solution of the equation. Finally, we consider some design examples and evaluate their performance.

关键词： convex programming hybrid filter banks second order cone programming

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Maximum Mutual Information Estimation via second order cone programming for Large Vocabulary Continuous Speech Recognition

Maximum Mutual Information Estimation via Second Order Cone ...

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10th INTERSPEECH 2009 Conference

作者： Wu, Dalei Li, Baojie Jiang, Hui York Univ Dept Comp Sci & Engn Toronto ON M3J 1P3 Canada

ISBN: (纸本)9781615676927

In this paper, we have successfully extended our previous work of convex optimization methods to MMIE-based discriminative training for large vocabulary continuous speech recognition. Specifically, we have re-formulated the MMIE training into a second order cone programming (SOCP) program using some convex relaxation techniques that we have previously proposed. Moreover, the entire SOCP formulation has been developed for word graphs instead of N-best lists to handle large vocabulary tasks. The proposed method has been evaluated in the standard WSJ-5k task and experimental results show that the proposed SOCP method significantly outperforms the conventional EBW method in terms of recognition accuracy as well as convergence behavior. Our experiments also show that the proposed SOCP method is efficient enough to handle some relatively large HMM sets normally used in large vocabulary tasks.

关键词： speech recognition discriminative training maximum mutual information estimation convex optimization second order cone programming

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Locally Centralized SOCP-based Localization Technique for Wireless Sensor Network

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Procedia Computer Science 2015年 73卷 76-85页

作者： Randa M. Abdelmoneem Eman Shaaban Department of Computer Systems Faculty of Computer and Information Sciences Ain-Shams UniversityCairo11566 Egypt

Node localization is one of the essential requirements to most applications of wireless sensor networks. This paper proposes a clustered localization approach for WSNs based on second order cone programming (SOCP). The cluster solves the SOCP problem as a global minimization to get positions of the cluster sensor nodes. To enhance localization accuracy, a cluster level refinement step is implemented using Gauss-Newton optimization. The initial position for the Gauss-Newton optimization is the position drawn from the preprocessor SOCP. The proposed approach scales well for large networks and provides a considerable reduction in computation time while yielding good localization accuracy.

关键词： Wireless sensor network Localization second order cone programming

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Limit analysis of 3D masonry block structures with non-associative frictional joints using cone programming

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COMPUTERS & STRUCTURES 2014年第Sep.期143卷 108-121页

作者： Portioli, F. Casapulla, C. Gilbert, M. Cascini, L. Univ Naples Federico II Dept Struct Engn & Architecture I-80134 Naples Italy Univ Sheffield Dept Civil & Struct Engn Sheffield S1 3JD S Yorkshire England

A three-dimensional limit analysis model for masonry structures is presented. In the model the masonry is discretized as an assemblage of rigid blocks, which interact via no-tension contact surfaces with Coulomb friction. A concave contact formulation is adopted and an iterative solution procedure is used to allow the underlying non-associative friction problem to be solved. second order cone programming (SOCP) is used to allow direct modelling of the conic failure surface. The formulation is validated against various numerical benchmark problems and then successfully applied to masonry walls and a small-scale masonry building tested experimentally. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Masonry structures Rigid block Limit analysis Non-associative friction second order cone programming Three-dimensional modelling

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Symmetry-exploiting cuts for a class of mixed-0/1 second-order cone programs

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DISCRETE OPTIMIZATION 2014年 13卷 23-35页

作者： Drewes, Sarah Pokutta, Sebastian Georgia Inst Technol ISyE Atlanta GA 30332 USA

We will analyze mixed-0/1 second-order cone programs where the continuous and binary variables are solely coupled via the conic constraints. We devise a cutting-plane framework based on an implicit Sherali-Adams reformulation. The resulting cuts are very effective as symmetric solutions are automatically cut off and each equivalence class of 0/1 solutions is visited at most once. Further, we present computational results showing the effectiveness of our method and briefly sketch an application in optimal pooling of securities. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Mixed-integer nonlinear programming Cutting planes second order cone programming Outer approximation

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Component selection in additive quantile regression models

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2014年第3期43卷 439-452页

作者： Noh, Hohsuk Lee, Eun Ryung Catholic Univ Louvain Inst Stat Biostat & Sci Actuarielles ISBA Louvain Belgium Univ Mannheim Mannheim Germany

Nonparametric additive models are powerful techniques for multivariate data analysis. Although many procedures have been developed for estimating additive components both in mean regression and quantile regression, the problem of selecting relevant components has not been addressed much especially in quantile regression. We present a doubly-penalized estimation procedure for component selection in additive quantile regression models that combines basis function approximation with a ridge-type penalty and a variant of the smoothly clipped absolute deviation penalty. We show that the proposed estimator identifies relevant and irrelevant components consistently and achieves the nonparametric optimal rate of convergence for the relevant components. We also provide an accurate and efficient computation algorithm to implement the estimator and demonstrate its performance through simulation studies. Finally, we illustrate our method via a real data example to identify important body measurements to predict percentage of body fat of an individual. (C) 2014 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.

关键词： Additive model Covariate selection Nonparametric quantile regression second order cone programming Penalized estimation

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second order cone programming relaxation for quadratic assignment problems

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OPTIMIZATION METHODS & SOFTWARE 2008年第3期23卷 441-449页

作者： Xia, Yong Beihang Univ Dept Appl Math Beijing Peoples R China

We present a second order cone programming relaxation with O(n(2)) variables for quadratic assignment problems, which provides a lower bound not less than the well-known quadratic programming bound. It is further stre... 详细信息

关键词： second order cone programming quadratic assignment problem quadratic programming bound

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ROBUST EXTREME LEARNING MACHINE FOR REGRESSION PROBLEMS WITH ITS APPLICATION TO WIFI BASED INDOOR POSITIONING SYSTEM 24

ROBUST EXTREME LEARNING MACHINE FOR REGRESSION PROBLEMS WITH...

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IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Lu, Xiaoxuan Long, Yushen Zou, Han Yu, Chengpu Xie, Lihua Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781479936946

We propose two kinds of robust extreme learning machines (RELMs) based on the close-to-mean constraint and the small-residual constraint respectively to solve the problem of noisy measurements in indoor positioning systems (IPSs). We formulate both RELMs as second order cone programming problems. The fact that feature mapping in ELM is known to users is exploited to give the needed information for robust constraints. Real-world indoor localization experimental results show that, the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviations and worst case errors of IPSs compared with basic ELM and OPT-ELM based IPSs.

关键词： Robust extreme learning machine Indoor positioning system second order cone programming

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A Chance Constrained Approach for Uplink Wireless OFDMA Networks 9

A Chance Constrained Approach for Uplink Wireless OFDMA Netw...

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9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP)

作者： Nunez, Belarmino Adasme, Pablo Soto, Ismael Cheng, Jianqiang Letournel, Marc Lisser, Abdel Univ Santiago Chile Dept Elect Engn Santiago Chile Univ Paris 11 Lab Rech Informat Orsay France

ISBN: (纸本)9781479925810

In this paper, we compare individual and joint probabilistic constraints for a resource allocation problem in an uplink (UL) wireless OFDMA network. For this purpose, we formulate the problem as a stochastic linear programming (SLP) problem. Then, we transform this model into equivalent deterministic second-order cone programming (SOCP) problems. All models are intended to maximize the bit rates throughput of the network subject to subcarrier and power user constraints. Our preliminary numerical results show that the joint chance constraint formulation is slightly conservative than the individual probabilistic one. Finally, we show that our approximation of the deterministic joint probabilistic model is very tight.

关键词： second order cone programming stochastic programming probabilistic constraints linear programming resource allocation in orthogonal frequency division multiple access

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The Q method for second order cone programming

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COMPUTERS & OPERATIONS RESEARCH 2008年第5期35卷 1510-1538页

作者： Xia, Yu Alizadeh, Farid Inst Stat Math Minato Ku Tokyo 1068569 Japan Rutgers State Univ Dept Management Sci & Informat Syst Piscataway NJ 08855 USA Rutgers State Univ Rutgers Ctr Operat Res Piscataway NJ 08855 USA

We develop the Q method for the second order cone programming problem. The algorithm is the adaptation of the Q method for semidefinite programming originally developed by Alizadeh, Haeberly and Overton [A new primal-dual interior point method for semidefinite programming. In: Proceedings of the fifth SIAM conference on applications of linear algebra, Snowbird, Utah, 1994.] and [Primal-dual interior-point methods for semidefinite programming: convergence rates, stability and numerical results. SIAM Journal on Optimization 1998;8(3):746-68 [electronic].]. We take advantage of the special algebraic structure associated with second order cone programs to formulate the Q method. Furthermore we discuss the convergence properties of the algorithm. Finally, some numerical results are presented. (c) 2006 Elsevier Ltd. All fights reserved.

关键词： second order cone programming infeasible interior point method the Q method

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