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检索条件"主题词=Concave Programming"

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A Distributed Power Allocation Scheme for Sum-Rate Maximization on Cognitive GMACs

A Distributed Power Allocation Scheme for Sum-Rate Maximizat...

引用

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Han, Sang-wook Kim, Hoon Han, Youngnam Cioffi, John M. Leung, Victor C. M. Korea Adv Inst Sci & Technol Dept Elect Engn Taejon South Korea Incheon Univ Dept Elect Engn Inchon South Korea Stanford Univ Dept Elect Engn Stanford CA 94305 USA King Abdulaziz Univ Dept Comp Sci Jeddah Saudi Arabia Univ British Columbia Dept Elect Engn & Comp Engn Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9781424480166

This paper considers a distributed power allocation scheme for sum-rate-maximization under cognitive Gaussian multiple access channels (GMACs), where primary users and secondary users may communicate under mutual interference with the Gaussian noise. Formulating the problem as a standard nonconvex quadratically constrained quadratic problem (QCQP) provides a simple distributed method to find a solution using iterative Jacobian method instead of using centralized schemes. Numerical results show that this distributed algorithm for power allocation is converged to a fixed point and the solution achieves almost the same performance of the exhaustive search.

关键词： Gaussian channels Gaussian noise Gaussian noise Jacobian matrices centralized schemes cognitive GMAC cognitive Gaussian multiple access channels cognitive radio concave programming distributed algorithm distributed algorithms distributed power allocation scheme iterative Jacobian method iterative methods multi-access systems mutual interference nonconvex quadratically constrained quadratic problem quadratic programming sum-rate maximization

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Polytope of Correct (Linear programming) Decoding and Low-Weight Pseudo-Codewords

Polytope of Correct (Linear Programming) Decoding and Low-We...

引用

IEEE International Symposium on Information Theory (ISIT)

作者： Chertkov, Michael Stepanov, Mikhail LANL CNLS Los Alamos NM 87545 USA Univ Kaiserslautern Dept Math D-67663 Kaiserslautern Germany

ISBN: (纸本)9781457705953

We analyze Linear programming (LP) decoding of graphical binary codes operating over soft-output, symmetric and log-concave channels. We show that the error-surface, separating domain of the correct decoding from domain of the erroneous decoding, is a polytope. We formulate the problem of finding the lowest-weight pseudo-codeword as a non-convex optimization (maximization of a convex function) over a polytope, with the cost function defined by the channel and the polytope defined by the structure of the code. This formulation suggests new provably convergent heuristics for finding the lowest weight pseudo-codewords improving in quality upon previously discussed. The algorithm performance is tested on the example of the Tanner [155,64,20] code over the Additive White Gaussian Noise (AWGN) channel.

关键词： AWGN channel AWGN channels AWGN channels Convex functions Decoding LP decoding Minimization Noise Optimization Parity check codes additive white gaussian noise channel binary codes channel coding concave programming convergent heuristic correct decoding cost function decoding erroneous decoding error correction codes error-surface separating domain graphical binary code linear programming linear programming decoding log-concave channel lowest-weight pseudo-codeword nonconvex optimization polytope soft-output channel symmetric channel

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Dominance-constrained Markov decision processes

Dominance-constrained Markov decision processes

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51st IEEE Annual Conference on Decision and Control (CDC)

作者： Haskell, William B. Jain, Rahul Univ So Calif ISE Dept Los Angeles CA 90089 USA Univ So Calif EE & ISE Dept Los Angeles CA 90089 USA

ISBN: (纸本)9781467320665

We are interested in risk constraints for discrete time Markov decision processes (MDPs). Starting with the average reward case, we argue that stochastic dominance constraints are natural risk constraints for MDPs. Specifically, we constrain the empirical distribution of reward to dominate a benchmark distribution in the increasing concave stochastic order. We argue that the optimal policy for the dominance-constrained MDP is a stationary randomized policy. Further, the optimal policy can be computed from a linear program in the space of occupation measures, where the dominance constraint is represented by linear inequalities. The dual of this linear program is computed and is shown to be close to the usual linear programming form of the average dynamic programming optimality equations. In our case, a pricing term appears in the dual corresponding to the dominance constraints. We carry out a parallel development for the discounted reward case with stochastic dominance constraints. We also extend this development to cover multivariate stochastic dominance.

关键词： Markov processes concave programming discrete time systems dynamic programming linear programming linear systems stochastic systems

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Distributed Algorithm for SDP State Estimation

Distributed Algorithm for SDP State Estimation

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Conference of the IEEE PES on Innovative Smart Grid Technologies (ISGT)

作者： Weng, Yang Li, Qiao Negi, Rohit Ilic, Marija Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781467348966;9781467348942

Smart Grid State Estimation (SE) aims at providing robust and accurate system state estimate for subsequent control operations to accommodate the disturbance of highly intermittent components. Conventional SE for AC Power Grid formulates the estimation process as a non-convex optimization problem, which may reach a local optimal and stop. To compensate the drawback, Semidefinite programming (SDP) was recently applied to convexify the non-convex problem with rank one relaxation technique, which shows perspectives with approximately globally optimal estimate. However, as the SDP approach is essentially a centralized algorithm, it is computationally expensive and non-robust to partial network failure, bad data. etc. To ameliorate the current SDP SE approach, this paper presents a distributed algorithm by employing Lagrangian method and graph theory results and particularly by dividing power networks in accordance with network 'cliques'. Correspondingly, significant improvements are illustrated in simulations on IEEE test beds.

关键词： concave programming graph theory power system control power system state estimation robust control smart power grids

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A Low-Complexity Cache-Aided Multi-Antenna Content Delivery Scheme 20

A Low-Complexity Cache-Aided Multi-Antenna Content Delivery ...

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20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Zhao, Junlin Amiri, Mohammad Mohammadi Gunduz, Deniz Imperial Coll London Dept Elect & Elect Engn London SW7 2BT England

ISBN: (纸本)9781538665282

We study downlink beamforming in a single-cell network with a multi-antenna base station (BS) serving cache-enabled users. For a given common rate of the files in the system, we first formulate the minimum transmit power with beamforming at the BS as a non-convex optimization problem. This corresponds to a multiple multicast problem, to which a stationary solution can be efficiently obtained through successive convex approximation (SCA). It is observed that the complexity of the problem grows exponentially with the number of subfiles delivered to each user in each time slot, which itself grows exponentially with the number of users in the system. Therefore, we introduce a low-complexity alternative through time-sharing that limits the number of subfiles that can be received by a user in each time slot. It is shown through numerical simulations that, the reduced-complexity beamforming scheme has minimal performance gap compared to transmitting all the subfiles jointly, and outperforms the state-of-the-art low-complexity scheme at all SNR and rate values with sufficient spatial degrees of freedom, and in the high SNR/high rate regime when the number of spatial degrees of freedom is limited.

关键词： antenna arrays approximation theory array signal processing cellular radio computational complexity concave programming cellular radio array signal processing Approximation Theory Antenna arrays Degree of freedom complexity classes Beamforming multiple antenna Transmitter power Consumers Content distribution Bahamas

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Advanced optimization methods for power systems

Advanced optimization methods for power systems

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2014 Power Systems Computation Conference (PSCC)

作者： Panciatici, P. Campi, M. C. Garatti, S. Low, S. H. Molzahn, D. K. Sun, A. X. Wehenkel, L. RTE R&D Dept Versailles France Univ Brescia Dept Informat Engn Brescia Italy Politecn Milan Dept Elect Comp Sci & Bioengn Milan Italy CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA Univ Michigan Dept EE&CS Ann Arbor MI 48109 USA Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Univ Liege Dept EE&CS Inst Montefiore B-4000 Liege Belgium

ISBN: (纸本)9788393580132

Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous and discrete variables. In the recent years, a number of complementary theoretical advances in addressing such problems have been obtained in the field of applied mathematics. The paper introduces a selection of these advances in the fields of non-convex optimization, in mixed-integer programming, and in optimization under uncertainty. The practical relevance of these developments for power systems planning and operation are discussed, and the opportunities for combining them, together with high-performance computing and big data infrastructures, as well as novel machine learning and randomized algorithms, are highlighted.

关键词： Big Data concave programming integer programming learning (artificial intelligence) nonlinear programming parallel processing power engineering computing power system planning randomised algorithms Big Data infrastructure advanced optimization method high-performance computing machine learning mixed integer programming nonconvex optimization power system operation randomized algorithm Electronic mail Europe Linear programming Optimization Planning Power systems Uncertainty Power system planning randomized algorithms Integer programming power systems High Performance Computing big data Power engineering computing electronic mail Nonlinear programming PARALLEL PROCESSING (COMPUTERS) Optimization methods linear programming Uncertainty

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Low Complexity Semi-Blind Channel Estimation Algorithm in Two-Way Relay Networks

Low Complexity Semi-Blind Channel Estimation Algorithm in Tw...

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22nd IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zhao, Qiong Zhou, Zhendong Vucetic, Branka Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

ISBN: (纸本)9781457713484

In this work, we propose a low complexity semi-blind channel estimation algorithm, referred to as the variance squared maximum likelihood (VSML) estimator, which employs only one training symbol in each channel estimation, to estimate general non-reciprocal flat-fading channels in amplify-and forward (AF) two-way relay networks (TWRNs). We formulate a non-convex objective function and obtain closed-form channel estimates by minimizing its approximate expression. Theoretical analysis proves that the derived channel estimation is asymptotically optimal in large sample size scenarios. Monte-Carlo simulation results show that the VSML estimator outperforms the existing relaxed maximum likelihood (RML) estimator in terms of mean squared error (MSE) performance and remarkably reduces the computational complexity by completely avoiding the grid-search algorithm under M-ary phase-shift-keying (MPSK) modulation.

关键词： Channel estimation M-ary phase-shift-keying MPSK modulation MSE performance Maximum likelihood estimation Monte Carlo methods Monte-Carlo simulation Phase shift keying Relays Signal to noise ratio Training VSML estimator amplify and forward communication amplify-and-forward two-way relay networks channel estimation closed-form channel estimation computational complexity computational complexity concave programming fading channels grid-search algorithm low complexity semiblind channel estimation algorithm maximum likelihood estimation mean square error methods mean squared error performance nonconvex objective function nonreciprocal flat-fading channel estimation phase shift keying training symbol variance squared maximum likelihood estimator

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The profit maximization problem in economies of scale

The profit maximization problem in economies of scale

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10th International Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE)

作者： Bayon, L. Otero, J. A. Ruiz, M. M. Suarez, P. M. Tasis, C. Univ Oviedo Dept Math Gijon Spain

In this paper we present a generalization of the classic Firm's Profit Maximization Problem, using the linear model for the production function, considering a decreasing price w(i)(x(i)) = b(i) - c(i)x(i) and maximum constraints for the inputs or, equivalently, considering inputs that are in turn outputs in economies of scale with quadratic concave cost functions. We formulate the problem by previously calculating the analytical minimum cost function in the quadratic concave case. This minimum cost function will be calculated for each production level via the infimal convolution of quadratic concave functions whose result is a piecewise quadratic concave function. (C) 2012 Elsevier B.V. All rights reserved.

关键词： concave programming Economies of scale Infimal convolution Piecewise concave functions Algorithm complexity

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A New Linear Precoding Strategy for MIMO BC

A New Linear Precoding Strategy for MIMO BC

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Nam, Junyoung Ha, Jeongseok Ahn, Jae Young ETRI Mobile Telecommun Div Daejeon South Korea Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Engn Daejeon South Korea

ISBN: (纸本)9781424456383

The problem of maximizing sum rate of a multiple-antenna Gaussian broadcast channel (BC) with dirty-paper coding (DPC) is well known to be efficiently solved via duality, which transforms this nonconvex problem into a well-structured convex multiple-access channel (MAC) problem. To date, however, finding an optimal transmission strategy when employing linear precoding is still open, since it could not be transformed to a convex problem. In this paper, we introduce some restrictive condition and establish a duality between sum rates of MIMO BC and MIMO MAC with linear precoding under that condition, which converts the nonconvex and untractable problem at hand to a well-structured one like the DPC strategy. By doing so, we can anticipate a computationally efficient optimization algorithm which converges to the global optimum.

关键词： DPC strategy Gaussian channels MIMO BC MIMO MAC MIMO systems broadcast channels computationally efficient optimization algorithm concave programming convex multiple-access channel convex programming dirty-paper coding duality duality (mathematics) linear

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Grouped Sparse Signal Reconstruction Using Non-convex Regularizers

Grouped Sparse Signal Reconstruction Using Non-convex Regula...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Samarasinghe, Kasun M. Fan, H. Howard Univ Cincinnati Dept Elect Engn & Comp Syst Cincinnati OH 45220 USA

ISBN: (纸本)9781479970889

This paper introduces the usage of non-convex based regularizers to solve the underdetermined MEG inverse problem. The signal to be reconstructed is considered to have a structure which entails group-wise sparsity and within group sparsity among its covariates. We discuss the usage of l(2) norm regularization and smoothed l(0) ( SL0) norm regularization to impose group-wise and within group sparsity respectively. In addition, we introduce a novel criterion which if satisfied, guarantees global optimality while solving this non-convex optimization problem. We use proximal gradient descent as the method of optimization as it promises faster convergence rates. We show that our algorithm can successfully recover a sparse signal with a smaller number of measurements than the conventional l(1) regularization framework.

关键词： concave programming gradient methods magnetoencephalography medical signal processing signal reconstruction ?1 regularization framework ?2 norm regularization MEG inverse problem global optimality group-wise sparsity grouped sparse signal reconstruction magneto-encephalography nonconvex optimization problem nonconvex regularizers proximal gradient descent smoothed ?0 norm regularization sparse signal recovery within group sparsity Cost function Image reconstruction Linear approximation Sensors Vectors

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