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Gauss Pseudospectral Method for Solving InfiniteHorizon Optimal Control Problems
"... The previously developed Gauss pseudospectral method is extended to the case of nonlinear infinitehorizon optimal control problems. First, the semiinfinite domain t ∈ [0,+∞) is transformed to the domain τ = [−1,+1). The firstorder optimality conditions of NLP obtained from the pseudospectral di ..."
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The previously developed Gauss pseudospectral method is extended to the case of nonlinear infinitehorizon optimal control problems. First, the semiinfinite domain t ∈ [0,+∞) is transformed to the domain τ = [−1,+1). The firstorder optimality conditions of NLP obtained from the pseudospectral
Pseudospectral Methods for InfiniteHorizon Nonlinear Optimal
 Control Problems,” AIAA Guidance, Navigation, and Control Conference, AIAA
, 2005
"... A central computational issue in solving infinitehorizon nonlinear optimal control problems is the treatment of the horizon. In this paper, we directly address this issue by a domain transformation technique that maps the infinite horizon to a finite horizon. The transformed finite horizon serves a ..."
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Cited by 19 (6 self)
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A central computational issue in solving infinitehorizon nonlinear optimal control problems is the treatment of the horizon. In this paper, we directly address this issue by a domain transformation technique that maps the infinite horizon to a finite horizon. The transformed finite horizon serves
Infinitehorizon policygradient estimation
 Journal of Artificial Intelligence Research
, 2001
"... Gradientbased approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in valuefunction methods. In this paper we introduce � � , a si ..."
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Cited by 205 (5 self)
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Gradientbased approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in valuefunction methods. In this paper we introduce � � , a
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 696 (15 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
Direct Trajectory Optimization and Costate Estimation of FiniteHorizon and InfiniteHorizon Optimal Control Problems Using a Radau pseudospectral Method
 Computational Optimization and Applications
, 2011
"... A method is presented for direct trajectory optimization and costate estimation using global collocation at LegendreGaussRadau (LGR) points. The method is formulated first by casting the dynamics in integral form and computing the integral from the initial point to the interior LGR points and the ..."
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Cited by 37 (24 self)
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is derived. As a result, the method presented in this paper can be thought of as either a global implicit integration method or a pseudospectral method. Moreover, the formulation derived in this paper enables solving general finitehorizon problems using global collocation at the LGR points. A key feature
Optimization Flow Control, I: Basic Algorithm and Convergence
 IEEE/ACM TRANSACTIONS ON NETWORKING
, 1999
"... We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm. In thi ..."
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Cited by 690 (64 self)
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We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm
A Limited Memory Algorithm for Bound Constrained Optimization
 SIAM Journal on Scientific Computing
, 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. ..."
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Cited by 557 (9 self)
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An algorithm for solving large nonlinear optimization problems with simple bounds is described.
On optimistic methods for concurrency control
 ACM Transactions on Database Systems
, 1981
"... Most current approaches to concurrency control in database systems rely on locking of data objects as a control mechanism. In this paper, two families of nonlocking concurrency controls are presented. The methods used are “optimistic ” in the sense that they rely mainly on transaction backup as a co ..."
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Cited by 547 (1 self)
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Most current approaches to concurrency control in database systems rely on locking of data objects as a control mechanism. In this paper, two families of nonlocking concurrency controls are presented. The methods used are “optimistic ” in the sense that they rely mainly on transaction backup as a
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 582 (23 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
Results 1  10
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