The Experts below are selected from a list of 124200 Experts worldwide ranked by ideXlab platform
Dragan Nesic - One of the best experts on this subject based on the ideXlab platform.
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a Unified Framework for design and analysis of networked and quantized control systems
IEEE Transactions on Automatic Control, 2009Co-Authors: Dragan Nesic, Daniel LiberzonAbstract:We generalize and unify a range of recent results in quantized control systems (QCS) and networked control systems (NCS) literature and provide a Unified Framework for controller design for control systems with quantization and time scheduling via an emulation-like approach. A crucial step in our proofs is finding an appropriate Lyapunov function for the quantization/time-scheduling protocol which verifies its uniform global exponential stability (UGES). We construct Lyapunov functions for several representative protocols that are commonly found in the literature, as well as some new protocols not considered previously. Our approach is flexible and amenable to further extensions which are briefly discussed.
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a Unified Framework for design and analysis of networked and quantized control systems
IEEE Transactions on Automatic Control, 2009Co-Authors: Dragan Nesic, Daniel LiberzonAbstract:We generalize and unify a range of recent results in quantized control systems (QCS) and networked control systems (NCS) literature and provide a Unified Framework for controller design for control systems with quantization and time scheduling via an emulation-like approach. A crucial step in our proofs is finding an appropriate Lyapunov function for the quantization/time-scheduling protocol which verifies its uniform global exponential stability (UGES). We construct Lyapunov functions for several representative protocols that are commonly found in the literature, as well as some new protocols not considered previously. Our approach is flexible and amenable to further extensions which are briefly discussed.
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a Unified Framework for input to state stability in systems with two time scales
IEEE Transactions on Automatic Control, 2003Co-Authors: Andrew R Teel, L Moreau, Dragan NesicAbstract:This paper develops a Unified Framework for studying robustness of the input-to-state stability (ISS) property and presents new results on robustness of ISS to slowly varying parameters, to rapidly varying signals, and to generalized singular perturbations. The common feature in these problems is a time-scale separation between slow and fast variables which permits the definition of a boundary layer system like in classical singular perturbation theory. To address various robustness problems simultaneously, the asymptotic behavior of the boundary layer is allowed to be complex and it generates an average for the derivative of the slow state variables. The main results establish that if the boundary layer and averaged systems are ISS then the ISS bounds also hold for the actual system with an offset that converges to zero with the parameter that characterizes the separation of time-scales. The generality of the Framework is illustrated by making connection to various classical two time-scale problems and suggesting extensions.
Daniel Liberzon - One of the best experts on this subject based on the ideXlab platform.
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a Unified Framework for design and analysis of networked and quantized control systems
IEEE Transactions on Automatic Control, 2009Co-Authors: Dragan Nesic, Daniel LiberzonAbstract:We generalize and unify a range of recent results in quantized control systems (QCS) and networked control systems (NCS) literature and provide a Unified Framework for controller design for control systems with quantization and time scheduling via an emulation-like approach. A crucial step in our proofs is finding an appropriate Lyapunov function for the quantization/time-scheduling protocol which verifies its uniform global exponential stability (UGES). We construct Lyapunov functions for several representative protocols that are commonly found in the literature, as well as some new protocols not considered previously. Our approach is flexible and amenable to further extensions which are briefly discussed.
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a Unified Framework for design and analysis of networked and quantized control systems
IEEE Transactions on Automatic Control, 2009Co-Authors: Dragan Nesic, Daniel LiberzonAbstract:We generalize and unify a range of recent results in quantized control systems (QCS) and networked control systems (NCS) literature and provide a Unified Framework for controller design for control systems with quantization and time scheduling via an emulation-like approach. A crucial step in our proofs is finding an appropriate Lyapunov function for the quantization/time-scheduling protocol which verifies its uniform global exponential stability (UGES). We construct Lyapunov functions for several representative protocols that are commonly found in the literature, as well as some new protocols not considered previously. Our approach is flexible and amenable to further extensions which are briefly discussed.
Marc Vanderhaeghen - One of the best experts on this subject based on the ideXlab platform.
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Unified Framework for generalized and transverse momentum dependent parton distributions within a 3q light cone picture of the nucleon
Journal of High Energy Physics, 2011Co-Authors: Cedric Lorce, Barbara Pasquini, Marc VanderhaeghenAbstract:We present a systematic study of generalized transverse-momentum dependent parton distributions (GTMDs). By taking specific limits or projections, these GTMDs yield various transverse-momentum dependent and generalized parton distributions, thus providing a Unified Framework to simultaneously model different observables. We present such simultaneous modeling by considering a light-cone wave function overlap representation of the GTMDs. We construct the different quark-quark correlation functions from the 3-quark Fock components within both the light-front constituent quark model and the chiral quark-soliton model. We provide a comparison with available data and make predictions for different observables.
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Unified Framework for generalized and transverse momentum dependent parton distributions within a 3q light cone picture of the nucleon
arXiv: High Energy Physics - Phenomenology, 2011Co-Authors: Cedric Lorce, Barbara Pasquini, Marc VanderhaeghenAbstract:We present a systematic study of generalized transverse-momentum dependent parton distributions (GTMDs). By taking specific limits or projections, these GTMDs yield various transverse-momentum dependent and generalized parton distributions, thus providing a Unified Framework to simultaneously model different observables. We present such simultaneous modeling by considering a light-cone wave function overlap representation of the GTMDs. We construct the different quark-quark correlation functions from the 3-quark Fock components within both the light-front constituent quark model as well as within the chiral quark-soliton model. We provide a comparison with available data and make predictions for different observables.
Yinyu Ye - One of the best experts on this subject based on the ideXlab platform.
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a Unified Framework for dynamic pari mutuel information market design
Electronic Commerce, 2009Co-Authors: Shipra Agrawal, Erick Delage, Mark Peters, Zizhuo Wang, Yinyu YeAbstract:Recently, coinciding with and perhaps driving the increased popularity of prediction markets, several novel pari-mutuel mechanisms have been developed such as the logarithmic market scoring rule (LMSR), the cost-function formulation of market makers, and the sequential convex parimutuel mechanism (SCPM). In this work, we present a Unified convex optimization Framework which connects these seemingly unrelated models for centrally organizing contingent claims markets. The existing mechanisms can be expressed in our Unified Framework using classic utility functions. We also show that this Framework is equivalent to a convex risk minimization model for the market maker. This facilitates a better understanding of the risk attitudes adopted by various mechanisms. The utility Framework also leads to easy implementation since we can now find the useful cost function of a market maker in polynomial time through the solution of a simple convex optimization problem. In addition to unifying and explaining the existing mechanisms, we use the generalized Framework to derive necessary and sufficient conditions for many desirable properties of a prediction market mechanism such as proper scoring, truthful bidding (in a myopic sense), efficient computation, controllable risk-measure, and guarantees on the worst-case loss. As a result, we develop the first proper, truthful, risk controlled, loss-bounded (in number of states) mechanism; none of the previously proposed mechanisms possessed all these properties simultaneously. Thus, our work could provide an effective tool for designing new market mechanisms.
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a Unified Framework for dynamic pari mutuel information market design
arXiv: Trading and Market Microstructure, 2009Co-Authors: Shipra Agrawal, Erick Delage, Mark Peters, Zizhuo Wang, Yinyu YeAbstract:Recently, several new pari-mutuel mechanisms have been introduced to organize markets for contingent claims. Hanson introduced a market maker derived from the logarithmic scoring rule, and later Chen and Pennock developed a cost function formulation for the market maker. On the other hand, the SCPM model of Peters et al. is based on ideas from a call auction setting using a convex optimization model. In this work, we develop a Unified Framework that bridges these seemingly unrelated models for centrally organizing contingent claim markets. The Framework, developed as a generalization of the SCPM, will support many desirable properties such as proper scoring, truthful bidding (in a myopic sense), efficient computation, and guarantees on worst case loss. In fact, our Unified Framework will allow us to express various proper scoring rules, existing or new, from classical utility functions in a convex optimization problem representing the market organizer. Additionally, we utilize concepts from duality to show that the market model is equivalent to a risk minimization problem where a convex risk measure is employed. This will allow us to more clearly understand the differences in the risk attitudes adopted by various mechanisms, and particularly deepen our intuition about popular mechanisms like Hanson's market-maker. In aggregate, we believe this work advances our understanding of the objectives that the market organizer is optimizing in popular pari-mutuel mechanisms by recasting them into one Unified Framework.
Hu Ding - One of the best experts on this subject based on the ideXlab platform.
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a Unified Framework for clustering constrained data without locality property
Algorithmica, 2020Co-Authors: Hu DingAbstract:In this paper, we consider a class of constrained clustering problems of points in $$\mathbb {R}^{d}$$, where d could be rather high. A common feature of these problems is that their optimal clusterings no longer have the locality property (due to the additional constraints), which is a key property required by many algorithms for their unconstrained counterparts. To overcome the difficulty caused by the loss of locality, we present in this paper a Unified Framework, called Peeling-and-Enclosing, to iteratively solve two variants of the constrained clustering problems, constrained k-means clustering (k-CMeans) and constrained k-median clustering (k-CMedian). Our Framework generalizes Kumar et al.’s (J ACM 57(2):5, 2010) elegant k-means clustering approach from unconstrained data to constrained data, and is based on two standalone geometric techniques, called Simplex Lemma and Weaker Simplex Lemma, for k-CMeans and k-CMedian, respectively. The simplex lemma (or weaker simplex lemma) enables us to efficiently approximate the mean (or median) point of an unknown set of points by searching a small-size grid, independent of the dimensionality of the space, in a simplex (or the surrounding region of a simplex), and thus can be used to handle high dimensional data. If k and $$\frac{1}{\epsilon }$$ are fixed numbers, our Framework generates, in nearly linear time (i.e., $$O(n(\log n)^{k+1}d)$$), $$O((\log n)^{k})$$k-tuple candidates for the k mean or median points, and one of them induces a $$(1+\epsilon )$$-approximation for k-CMeans or k-CMedian, where n is the number of points. Combining this Unified Framework with a problem-specific selection algorithm (which determines the best k-tuple candidate), we obtain a $$(1+\epsilon )$$-approximation for each of the constrained clustering problems. Our Framework improves considerably the best known results for these problems. We expect that our technique will be applicable to other variants of k-means and k-median clustering problems without locality.
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a Unified Framework for clustering constrained data without locality property
arXiv: Computational Geometry, 2018Co-Authors: Hu DingAbstract:In this paper, we consider a class of constrained clustering problems of points in $\mathbb{R}^{d}$, where $d$ could be rather high. A common feature of these problems is that their optimal clusterings no longer have the locality property (due to the additional constraints), which is a key property required by many algorithms for their unconstrained counterparts. To overcome the difficulty caused by the loss of locality, we present in this paper a Unified Framework, called {\em Peeling-and-Enclosing (PnE)}, to iteratively solve two variants of the constrained clustering problems, {\em constrained $k$-means clustering} ($k$-CMeans) and {\em constrained $k$-median clustering} ($k$-CMedian). Our Framework is based on two standalone geometric techniques, called {\em Simplex Lemma} and {\em Weaker Simplex Lemma}, for $k$-CMeans and $k$-CMedian, respectively. The simplex lemma (or weaker simplex lemma) enables us to efficiently approximate the mean (or median) point of an unknown set of points by searching a small-size grid, independent of the dimensionality of the space, in a simplex (or the surrounding region of a simplex), and thus can be used to handle high dimensional data. If $k$ and $\frac{1}{\epsilon}$ are fixed numbers, our Framework generates, in nearly linear time ({\em i.e.,} $O(n(\log n)^{k+1}d)$), $O((\log n)^{k})$ $k$-tuple candidates for the $k$ mean or median points, and one of them induces a $(1+\epsilon)$-approximation for $k$-CMeans or $k$-CMedian, where $n$ is the number of points. Combining this Unified Framework with a problem-specific selection algorithm (which determines the best $k$-tuple candidate), we obtain a $(1+\epsilon)$-approximation for each of the constrained clustering problems. We expect that our technique will be applicable to other constrained clustering problems without locality.
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a Unified Framework for clustering constrained data without locality property
Symposium on Discrete Algorithms, 2015Co-Authors: Hu DingAbstract:In this paper, we consider a class of constrained clustering problems of points in Rd space, where d could be rather high. A common feature of these problems is that their optimal clusterings no longer have the locality property (due to the additional constraints), which is a key property required by many algorithms for their unconstrained counterparts. To overcome the difficulty caused by the loss of locality, we present in this paper a Unified Framework, called Peeling-and-Enclosing, to iteratively solve two variants of the constrained clustering problems, constrained k-means clustering (k-CMeans) and constrained k-median clustering (k-CMedian). Our Framework generalizes Kumar et al.'s elegant k-means clustering approach [35] from unconstrained data to constrained data, and is based on two standalone geometric techniques, called Simplex Lemma and Weaker Simplex Lemma, for k-CMeans and k-CMedian, respectively. Simplex lemma (or weaker simplex lemma) enables us to efficiently approximate the mean (or median) point of an unknown set of points by searching a small-size grid, independent of the dimensionality of the space, in a simplex (or the surrounding region of a simplex), and thus can be used to handle high dimensional data. With these techniques, our Framework generates, in nearly linear time (i.e., O(n(log n)k+1d)), O((log n)k) k-tuple candidates for the k mean or median points, and one of them induces a (1 + e)-approximation for k-CMeans or k-CMedian, where n is the number of points. Combining this Unified Framework with a problem-specific selection algorithm (which determines the best k-tuple candidate), we obtain a (1 + e)-approximation for each of the constrained clustering problems. Our Framework improves considerably the best known results for these problems. We expect that our technique will be applicable to other constrained clustering problems without locality.
