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Constrained model predictive control: Stability and optimality
 AUTOMATICA
, 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
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Cited by 738 (16 self)
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Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence
The Cache Performance and Optimizations of Blocked Algorithms
 In Proceedings of the Fourth International Conference on Architectural Support for Programming Languages and Operating Systems
, 1991
"... Blocking is a wellknown optimization technique for improving the effectiveness of memory hierarchies. Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks, so that data loaded into the faster levels of the memory hierarchy are reused. This ..."
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Cited by 574 (5 self)
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. This paper presents cache performance data for blocked programs and evaluates several optimizations to improve this performance. The data is obtained by a theoretical model of data conflicts in the cache, which has been validated by large amounts of simulation. We show that the degree of cache interference
Optimal contracts and competitive markets with costly state verification
 Journal of Economic Theory
, 1979
"... The insight of Arrow [4] and Debreu [7] that uncertainty is easily incorporated into general equilibrium models is doubleedged. It is true that one need only index commodities by the state of nature, and classical results on the existence and optimality of competitive equilibria can be made to ..."
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Cited by 879 (8 self)
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The insight of Arrow [4] and Debreu [7] that uncertainty is easily incorporated into general equilibrium models is doubleedged. It is true that one need only index commodities by the state of nature, and classical results on the existence and optimality of competitive equilibria can be made to
The Ant System: Optimization by a colony of cooperating agents
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSPART B
, 1996
"... An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System. We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation ..."
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Cited by 1300 (46 self)
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An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System. We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed
Active Learning with Statistical Models
, 1995
"... For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative, statist ..."
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Cited by 679 (10 self)
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For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative
Evolutionary Computing
, 2005
"... Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main compone ..."
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Cited by 624 (35 self)
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components all evolutionary algorithms (EA), sketch the differences between different types of EAs and survey application areas ranging from optimization, modeling and simulation to entertainment.
The Coordination of Arm Movements: An Experimentally Confirmed Mathematical Model
 Journal of neuroscience
, 1985
"... This paper presents studies of the coordination of voluntary human arm movements. A mathematical model is formulated which is shown to predict both the qualitative features and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled mathematic ..."
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Cited by 688 (18 self)
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This paper presents studies of the coordination of voluntary human arm movements. A mathematical model is formulated which is shown to predict both the qualitative features and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled
Object Detection with Discriminatively Trained Part Based Models
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves stateoftheart results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
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Cited by 1422 (49 self)
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We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves stateoftheart results in the PASCAL object detection challenges. While deformable part models have become quite popular
Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 516 (18 self)
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. A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 819 (28 self)
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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical
Results 1  10
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