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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...

引用

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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Trustworthy and Scalable Nonconvex Statistical Estimation for Sample-Starved Multi-Modal Data Models.

Trustworthy and Scalable Nonconvex Statistical Estimation fo...

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作者： Chen, Y.

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H/sub /spl infin// synthesis for satellite formation keeping: an LMI approach

H/sub /spl infin// synthesis for satellite formation keeping...

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

作者： Baolin Wu Xibin Cao Fuchun Liu Research Center of Satellite Technology Harbin Institute of Technology Harbin China Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)0780393953

Numerous controllers have been proposed based on linear Hill's equations for satellite formation control. To date, most of these controllers assumed that the system model is well defined, and the disturbances are small. Previous studies have failed to consider the problem of disturbance rejection. In this paper, H/sub /spl infin// controllers are designed for disturbance attenuation based on linear Hill's equations. The goal is to design a state-feedback control law which minimizes the H/sub /spl infin// norm of transfer function from disturbance to the regulated outputs, which guarantee stable for the closed-loop system. The problem is reduced to a convex optimization involving linear matrix inequalities (LMIs). The proposed robust controllers are evaluated in simulation. The simulation results demonstrate that the proposed controllers are capable of controlling the satellite formation system more precisely than LQR under various perturbations.

关键词： H/sup /spl infin// control aerospace control artificial satellites closed loop systems convex programming linear matrix inequalities robust control state feedback transfer functions H/sub /spl infin// controllers H/sub /spl infin// synthesis closed-loop

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Scheduling Distributed Locomotives for Optimal Train Handling

Scheduling Distributed Locomotives for Optimal Train Handlin...

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American Control Conference

作者： Anup Menon H. Kirk Mathews James D. Brooks Dan Dai Brian N. Meyer GE Global Research Niskayuna NY USA GE Transportation Erie PA USA

ISBN: (纸本)9781509045839

This paper deals with an open loop control problem of planning the split of tractive and braking efforts amongst locomotives distributed along a heavy-haul freight train with the objective of managing the forces experienced by couplers between railcars (i.e. in-train forces). An optimization problem is formulated to solve this train handling problem with the magnitude of the largest forces experienced by the couplers as the cost function, which is to be minimized subject to real-world operational constraints on what tractive effort splits are permissible and temporal restrictions thereof. While the formulated problem contains continuous and discrete-valued variables, the structure of the problem is used to decompose it into a convex program with continuous variables and a purely discrete variable decision making problem. The latter is solved using dynamic programming by mapping it to a variant of the shortest path on a graph problem. Simulation results are presented to verify the efficacy of the approach.

关键词： Optimal Train Handling TRAIN locomotives Handling (Psychology) couplers Optimal Open loop systems rail vehicle Cost functions convex programming shortest path Force dynamic programming

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Stochastic Filtering in Jump Systems With State Dependent Mode Transitions

Stochastic Filtering in Jump Systems With State Dependent Mo...

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2009 American Control Conference (ACC 2009)

作者： Agostino Capponi Concetta Pilotto Division of Engineering and Applied Sciences California Institute of Technology

ISBN: (纸本)9781424445233

We introduce a new methodology to construct a Gaussian mixture approximation to the true filter density in hybrid Markovian switching systems. We relax the assumption that the mode transition process is a Markov chain and allow it to depend on the actual and unobservable state of the system. The main feature of the method is that the Gaussian densities used in the approximation are selected as the solution of a convex programming problem which trades off sparsity of the solution with goodness of fit. A meaningful example shows that the proposed method can outperform the widely used interacting multiple model (IMM) filter in terms of accuracy at the expenses of an increase in computational time.

关键词： Tracking Kalman filtering estimation transition process Markov chain convex programming approximation computational time jump Filtration Solutions Gauss Filters Switched systems Stochastic IMM

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Low probability of intercept-based collaborative transmit resource and bandwidth allocation strategy for target tracking in distributed radar network system

Low probability of intercept-based collaborative transmit re...

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IET International Radar Conference (IET IRC 2020)

作者： L. Ding C. Shi F. Wang J. Zhou Nanjing Univ. Aeronaut. Astronaut. Nanjing China

In this study, a collaborative transmit resource and bandwidth allocation (CTRBA) strategy based on the low probability of intercept (LPI) is presented for target tracking in the distributed radar network system. The primary goal of this strategy is to minimize the total resource consumption while satisfying a predetermined target tracking performance requirement. The closed-form expression for the predicted Bayesian Cramer-Rao lower bound (BCRLB) is derived and computed to gauge the target tracking accuracy. It is shown that the CTRBA is a mixed-Boolean and non-convex optimization problem. We propose an efficient two-step solution methodology to solve the resulting problem. Simulation results demonstrate that the CTRBA strategy can achieve the desired tracking performance with less resource consumption.

关键词： optimisation bandwidth allocation concave programming target tracking convex programming Bayes methods radar tracking probability

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Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via convex Optimization

Tensor Robust Principal Component Analysis: Exact Recovery o...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Canyi Lu Jiashi Feng Yudong Chen Wei Liu Zhouchen Lin Shuicheng Yan Dept. of Electr. & Comput. Eng. Nat. Univ. of Singapore Singapore Singapore Sch. of Oper. Res. & Inf. Eng. Cornell Univ. Ithaca NY USA Sch. of EECS Peking Univ. Beijing China

ISBN: (纸本)9781467388528

This paper studies the Tensor Robust Principal Component (TRPCA) problem which extends the known Robust P-CA [4] to the tensor case. Our model is based on a new tensor Singular Value Decomposition (t-SVD) [14] and its induced tensor tubal rank and tensor nuclear norm. Consider that we have a 3-way tensor X ∈ R~(n_1×n_2×n_3) such that X = L_0 + S_0, where L_0 has low tubal rank and S_0 is sparse. Is that possible to recover both components? In this work, we prove that under certain suitable assumptions, we can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the l_1-norm, i.e., min from L,ε ‖L‖_*+λ‖ε‖_1, s.t. x = L + ε, where λ = 1{the square root of}(max(n_1, n_2)n_3). Interestingly, TRPCA involves RPCA as a special case when n_3 = 1 and thus it is a simple and elegant tensor extension of RPCA. Also numerical experiments verify our theory and the application for the image denoising demonstrates the effectiveness of our method.

关键词： Tensile stress Principal component analysis Robustness Matrix decomposition Sparse matrices convex functions Image denoising image denoising convex function Tensor matrix decomposition Sparse matrix principal components analysis Tensile stress Robustness convex programming

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Offloading Dependent Tasks in Mobile Edge Computing with Service Caching

Offloading Dependent Tasks in Mobile Edge Computing with Ser...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Gongming Zhao Hongli Xu Yangming Zhao Chunming Qiao Liusheng Huang School of Computer Science and Technology University of Science and Technology of China Department of Computer Science and Engineering University at Buffalo The State University of New York

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

In Mobile Edge Computing (MEC), many tasks require specific service support for execution and in addition, have a dependent order of execution among the tasks. However, previous works often ignore the impact of having limited services cached at the edge nodes on (dependent) task offloading, thus may lead to an infeasible offloading decision or a longer completion time. To bridge the gap, this paper studies how to efficiently offload dependent tasks to edge nodes with limited (and predetermined) service caching. We formally define the problem of offloading dependent tasks with service caching (ODT-SC), and prove that there exists no algorithm with constant approximation for this hard problem. Then, we design an efficient convex programming based algorithm (CP) to solve this problem. Moreover, we study a special case with a homogeneous MEC and propose a favorite successor based algorithm (FS) to solve this special case with a competitive ratio of O(1). Extensive simulation results using Google data traces show that our proposed algorithms can significantly reduce applications' completion time by about 27-51% compared with other alternatives.

关键词： Mobile Edge Computing Task Offloading Service Caching Dependency Approximation UNLOADING approximation Religious Missions DEPENDENCE Cache memory Completion time Legal Executions dependent variables convex programming Dependent algorithms

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Policy Gradient for Coherent Risk Measures 15

Policy Gradient for Coherent Risk Measures

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Annual Conference on Neural Information Processing Systems

作者： Aviv Tamar Yinlam Chow Mohammad Ghavamzadeh Shie Mannor UC Berkeley Stanford University Adobe Research & INRIA Technion

ISBN: (纸本)9781510825024

Several authors have recently developed risk-sensitive policy gradient methods that augment the standard expected cost minimization problem with a measure of variability in cost. These studies have focused on specific risk-measures, such as the variance or conditional value at risk (CVaR). In this work, we extend the policy gradient method to the whole class of coherent risk measures, which is widely accepted in finance and operations research, among other fields. We consider both static and time-consistent dynamic risk measures. For static risk measures, our approach is in the spirit of policy gradient algorithms and combines a standard sampling approach with convex programming. For dynamic risk measures, our approach is actor-critic style and involves explicit approximation of value function. Most importantly, our contribution presents a unified approach to risk-sensitive reinforcement learning that generalizes and extends previous results.

关键词： Policies convex programming gradient methods

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Practical Large-Scale Optimization for Max-Norm Regularization 10

Practical Large-Scale Optimization for Max-Norm Regularizati...

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Annual Conference on Neural Information Processing Systems

作者： Jason Lee Benjamin Recht Ruslan Salakhutdinov Nathan Srebro Joel A. Tropp Institute of Computational and Mathematical Engineering Stanford University Department of Computer Sciences University of Wisconsin-Madison Brain and Cognitive Sciences and CSAIL Massachusetts Institute of Technology Toyota Technological Institute at Chicago Computing and Mathematical Sciences California Institute of Technology

ISBN: (纸本)9781617823800

The max-norm was proposed as a convex matrix regularizer in [1] and was shown to be empirically superior to the trace-norm for collaborative filtering problems. Although the max-norm can be computed in polynomial time, there are currently no practical algorithms for solving large-scale optimization problems that incorporate the max-norm. The present work uses a factorization technique of Burer and Monteiro [2] to devise scalable first-order algorithms for convex programs involving the max-norm. These algorithms are applied to solve huge collaborative filtering, graph cut, and clustering problems. Empirically, the new methods outperform mature techniques from all three areas.

关键词： collaborative filtering Polynomial time algorithms convexity new methods convex programming

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