The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
John B. Moore - One of the best experts on this subject based on the ideXlab platform.
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A quasi-Separation Theorem for LQG optimal control with IQ constraints
Systems & Control Letters, 1997Co-Authors: Andrew Lim, John B. MooreAbstract:We consider the deterministic, the full observation and the partial observation LQG optimal control problems with finitely many IQ (integral quadratic!) constraints, and show that Wohnam's famous Separation Theorem for stochastic control has a generalization to this case. Although the problems of filtering and control are not independent, we show that the interdependence of these two problems is so superficial that in effect, they are problems which can be treated separately. It is in this context that the label Quasi-Separation Theorem is to be understood. We conclude with a discussion of computation issues and show how gradient-type optimization algorithms can be used to solve these problems. In this way, a systematic computation algorithm is derived.
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Separation Theorem for linearly constrained LQG optimal control
Systems & Control Letters, 1996Co-Authors: Andrew Lim, John B. Moore, Leonid FaybusovichAbstract:We solve the linearly constrained linear-quadratic (LQ) and linear-quadratic-Gaussian (LQG) optimal control problems. Closed-form expressions of the optimal controls are derived, and the Separation Theorem is generalized.
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A quasi-Separation Theorem for LQG optimal control with IQ constraints
Proceedings of the 36th IEEE Conference on Decision and Control, 1Co-Authors: Andrew Lim, John B. MooreAbstract:We consider the deterministic, the full observation and the partial observation LQG optimal control problems with finitely many IQ (integral quadratic) constraints. We show that the Separation Theorem does not hold. However, a generalization which we call a quasi-Separation Theorem holds instead. We show how gradient-type optimization algorithms can be used to calculate the optimal control.
Andrew Lim - One of the best experts on this subject based on the ideXlab platform.
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A quasi-Separation Theorem for LQG optimal control with IQ constraints
Systems & Control Letters, 1997Co-Authors: Andrew Lim, John B. MooreAbstract:We consider the deterministic, the full observation and the partial observation LQG optimal control problems with finitely many IQ (integral quadratic!) constraints, and show that Wohnam's famous Separation Theorem for stochastic control has a generalization to this case. Although the problems of filtering and control are not independent, we show that the interdependence of these two problems is so superficial that in effect, they are problems which can be treated separately. It is in this context that the label Quasi-Separation Theorem is to be understood. We conclude with a discussion of computation issues and show how gradient-type optimization algorithms can be used to solve these problems. In this way, a systematic computation algorithm is derived.
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Separation Theorem for linearly constrained LQG optimal control
Systems & Control Letters, 1996Co-Authors: Andrew Lim, John B. Moore, Leonid FaybusovichAbstract:We solve the linearly constrained linear-quadratic (LQ) and linear-quadratic-Gaussian (LQG) optimal control problems. Closed-form expressions of the optimal controls are derived, and the Separation Theorem is generalized.
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A quasi-Separation Theorem for LQG optimal control with IQ constraints
Proceedings of the 36th IEEE Conference on Decision and Control, 1Co-Authors: Andrew Lim, John B. MooreAbstract:We consider the deterministic, the full observation and the partial observation LQG optimal control problems with finitely many IQ (integral quadratic) constraints. We show that the Separation Theorem does not hold. However, a generalization which we call a quasi-Separation Theorem holds instead. We show how gradient-type optimization algorithms can be used to calculate the optimal control.
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Separation Theorem for linearly constrained LQG optimal control - a continuous time case
Proceedings of 35th IEEE Conference on Decision and Control, 1Co-Authors: Andrew Lim, J.b. Moore, Leonid FaybusovichAbstract:We solve the linearly constrained linear-quadratic (LQ) and linear-quadratic-Gaussian (LQG) optimal control problems. Closed form expressions of the optimal controls are derived, and the Separation Theorem is generalized.
Y Steinberg - One of the best experts on this subject based on the ideXlab platform.
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The source-channel Separation Theorem revisited
IEEE Transactions on Information Theory, 1995Co-Authors: Sandhirakasu Vembu, Sergio Verdu, Y SteinbergAbstract:The single-user Separation Theorem of joint source-channel coding has been proved previously for wide classes of sources and channels. We find an information-stable source/channel pair which does not satisfy the Separation Theorem. New necessary and sufficient conditions for the transmissibility of a source through a channel are found, and we characterize the class of channels for which the Separation Theorem holds regardless of the source statistics. >
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When does the source-channel Separation Theorem hold?
Proceedings of 1994 IEEE International Symposium on Information Theory, 1994Co-Authors: Sandhirakasu Vembu, Sergio Verdu, Y SteinbergAbstract:The meeting point of the two main branches of the Shannon (1948) theory is the joint source-channel coding Theorem. This Theorem has two parts: a direct part and a converse part. It follows that either reliable transmission is possible by separate source-channel coding or it is not possible at all. This is the reason why the joint source-channel coding Theorem is commonly referred to as the Separation Theorem. We characterize those channels for which the classical statement of the Separation Theorem holds for every source. We also characterize those sources for which the Separation Theorem holds for every channel. A conclusion to be drawn from our results is that when dealing with nonstationary probabilistic models, care should be exercised before applying the Separation Theorem.
Meir Feder - One of the best experts on this subject based on the ideXlab platform.
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information spectrum approach to the source channel Separation Theorem
International Symposium on Information Theory, 2014Co-Authors: Nir Elkayam, Meir FederAbstract:A source-channel Separation Theorem for a general channel has recently been shown by Aggrawal et al.[1]. This Theorem states that if there exists a coding scheme that achieves a maximum distortion level dmax over a general channel W, then reliable communication can be accomplished over this channel at rates less than R(dmax), where R(·) is the rate distortion function of the source. The source, however, is essentially constrained to be discrete and memoryless (DMS). In this work we prove a stronger claim where the source is general, satisfying only a “sphere packing optimality” feature, and the channel is completely general. Furthermore, we show that if the channel satisfies the strong converse property as defined by Han & Verdu [2], then the same statement can be made with davg, the average distortion level, replacing dmax. Unlike the proofs in [1], we use information spectrum methods to prove the statements and the results can be quite easily extended to other situations.
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information spectrum approach to the source channel Separation Theorem
arXiv: Information Theory, 2013Co-Authors: Nir Elkayam, Meir FederAbstract:A source-channel Separation Theorem for a general channel has recently been shown by Aggrawal et. al. This Theorem states that if there exist a coding scheme that achieves a maximum distortion level d_{max} over a general channel W, then reliable communication can be accomplished over this channel at rates less then R(d_{max}), where R(.) is the rate distortion function of the source. The source, however, is essentially constrained to be discrete and memoryless (DMS). In this work we prove a stronger claim where the source is general, satisfying only a "sphere packing optimality" feature, and the channel is completely general. Furthermore, we show that if the channel satisfies the strong converse property as define by Han & verdu, then the same statement can be made with d_{avg}, the average distortion level, replacing d_{max}. Unlike the proofs there, we use information spectrum methods to prove the statements and the results can be quite easily extended to other situations.
Jun Chen - One of the best experts on this subject based on the ideXlab platform.
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a source channel Separation Theorem with application to the source broadcast problem
IEEE Transactions on Information Theory, 2016Co-Authors: Kia Khezeli, Jun ChenAbstract:A converse method is developed for the source broadcast problem. Specifically, it is shown that the Separation architecture is optimal for a variant of the source broadcast problem, and the associated source-channel Separation Theorem can be leveraged, via a reduction argument, to establish a necessary condition for the original problem, which unifies several existing results in the literature. Somewhat surprisingly, this method, albeit based on the source-channel Separation Theorem, can be used to prove the optimality of non-Separation-based schemes and determine the performance limits in certain scenarios where the Separation architecture is suboptimal.
