Results 51 to 60 of about 3,744,840 (204)
Continuous State Dynamic Programming via Nonexpansive Approximation [PDF]
This paper studies fitted value iteration for continuous state dynamic programming using nonexpansive function approximators. A number of nonexpansive approximation schemes are discussed.
John Stachurski
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Differential Dynamic Programming: An Optimization Technique for Nonlinear Systems [PDF]
, 1969 Differential Dynamic. Programming is a new method, based on Bellman's principle of optimality, for determining optimal control strategies for nonlinear systems. It has originally been developed by D.H.Jacobson. In this thesis a result is presented for
Sato, Nobuyuki
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Fuzzy dynamic programming problem for single additive constraint with multiplicatively separable return in terms of trapezoidal membership functions [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2019 Dynamic programming problems (DP) are multivariable optimization problems that can be decomposed into a series of stages, and optimization is done at each stage with respect to one variable only.
Kaliyaperumal Palanivel +1 more
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Algebraic Dynamic Programming [PDF]
, 2002 Dynamic programming is a classic programming technique, applicable in a wide variety of domains, like stochastic systems analysis, operations research, combinatorics of discrete structures, flow problems, parsing with ambiguous grammars, or biosequence analysis. Yet, no methodology is available for designing such algorithms. The matrix recurrences that
Giegerich, Robert, Meyer, Carsten
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CoRR, 2017 We propose a general method for combinatorial online learning problems whose offline optimization problem can be solved efficiently via a dynamic programming algorithm defined by an arbitrary min-sum recurrence. Examples include online learning of Binary Search Trees, Matrix-Chain Multiplications, $k$-sets, Knapsacks, Rod Cuttings, and Weighted ...
Holakou Rahmanian +2 more
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Regularization by dynamic programming [PDF]
Journal of Inverse and Ill-posed Problems, 2007 We investigate continuous regularization methods for linear inverse problems of static and dynamic type. These methods are based on dynamic programming approaches for linear quadratic optimal control problems. We prove regularization properties and also obtain rates of convergence for our methods.
Stefan Kindermann 0001, A. Leitao
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Compiling a domain specific language for dynamic programming [PDF]
, 2006 Steffen P. Compiling a domain specific language for dynamic programming.
Steffen, Peter
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Spatial cluster detection using dynamic programming
BMC Medical Informatics and Decision Making, 2012 Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value.
Sverchkov Yuriy +2 more
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Energies, 2018 This research, a new thrust-allocation algorithm based on penalty programming is developed to minimize the fuel consumption of offshore vessels/platforms with dynamic positioning system.
Se Won Kim, Moo Hyun Kim
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Output‐feedback stochastic model predictive control of chance‐constrained nonlinear systems
IET Control Theory & Applications, 2023 This study covers the output‐feedback model predictive control (MPC) of nonlinear systems subjected to stochastic disturbances and state chance constraints. The stochastic optimal control problem is solved in a stochastic dynamic programming fashion, and
Jingyu Zhang, Toshiyuki Ohtsuka
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