The Experts below are selected from a list of 7614 Experts worldwide ranked by ideXlab platform
Agami T. Reddy - One of the best experts on this subject based on the ideXlab platform.
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optimal control of building hvac r systems using complete simulation based Sequential Quadratic Programming csb sqp
Building and Environment, 2005Co-Authors: Jian Sun, Agami T. ReddyAbstract:This paper presents a general and systematic methodology, termed complete simulation-based Sequential Quadratic Programming (CSB-SQP), for determining the optimal control of building HVAC&R systems. This approach allows the coupling of a detailed simulation program with an efficient optimization method, namely the Sequential Quadratic Programming (SQP) algorithm. This approach allows the use of accurate component models of the system as against empirical models as currently used, while providing efficient optimal solutions to be determined. We develop the mathematical basis of the methodology and apply it to a simple cooling plant system to illustrate the accuracy, efficiency and robustness of this method. The issue of implementing such an optimization under real-time control is also discussed.
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Optimal control of building HVAC&R systems using complete simulation-based Sequential Quadratic Programming (CSB-SQP)
Building and Environment, 2005Co-Authors: Jian Sun, Agami T. ReddyAbstract:This paper presents a general and systematic methodology, termed complete simulation-based Sequential Quadratic Programming (CSB-SQP), for determining the optimal control of building HVAC&R systems. This approach allows the coupling of a detailed simulation program with an efficient optimization method, namely the Sequential Quadratic Programming (SQP) algorithm. This approach allows the use of accurate component models of the system as against empirical models as currently used, while providing efficient optimal solutions to be determined. We develop the mathematical basis of the methodology and apply it to a simple cooling plant system to illustrate the accuracy, efficiency and robustness of this method. The issue of implementing such an optimization under real-time control is also discussed.
Jian Sun - One of the best experts on this subject based on the ideXlab platform.
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optimal control of building hvac r systems using complete simulation based Sequential Quadratic Programming csb sqp
Building and Environment, 2005Co-Authors: Jian Sun, Agami T. ReddyAbstract:This paper presents a general and systematic methodology, termed complete simulation-based Sequential Quadratic Programming (CSB-SQP), for determining the optimal control of building HVAC&R systems. This approach allows the coupling of a detailed simulation program with an efficient optimization method, namely the Sequential Quadratic Programming (SQP) algorithm. This approach allows the use of accurate component models of the system as against empirical models as currently used, while providing efficient optimal solutions to be determined. We develop the mathematical basis of the methodology and apply it to a simple cooling plant system to illustrate the accuracy, efficiency and robustness of this method. The issue of implementing such an optimization under real-time control is also discussed.
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Optimal control of building HVAC&R systems using complete simulation-based Sequential Quadratic Programming (CSB-SQP)
Building and Environment, 2005Co-Authors: Jian Sun, Agami T. ReddyAbstract:This paper presents a general and systematic methodology, termed complete simulation-based Sequential Quadratic Programming (CSB-SQP), for determining the optimal control of building HVAC&R systems. This approach allows the coupling of a detailed simulation program with an efficient optimization method, namely the Sequential Quadratic Programming (SQP) algorithm. This approach allows the use of accurate component models of the system as against empirical models as currently used, while providing efficient optimal solutions to be determined. We develop the mathematical basis of the methodology and apply it to a simple cooling plant system to illustrate the accuracy, efficiency and robustness of this method. The issue of implementing such an optimization under real-time control is also discussed.
Zhongbo Sun - One of the best experts on this subject based on the ideXlab platform.
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a new trust region Sequential Quadratic Programming approach for nonlinear systems based on nonlinear model predictive control
Engineering Optimization, 2019Co-Authors: Zhongbo Sun, Y Y Sun, Keping LiuAbstract:ABSTRACTIn this article, a superlinearly convergent trust region–Sequential Quadratic Programming approach is first proposed, developed and investigated for nonlinear systems based on nonlinear mod...
Matthew J Tenny - One of the best experts on this subject based on the ideXlab platform.
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nonlinear model predictive control via feasibility perturbed Sequential Quadratic Programming
Computational Optimization and Applications, 2004Co-Authors: Matthew J Tenny, Stephen J Wright, James B RawlingsAbstract:Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed Sequential Quadratic Programming algorithm (developed in a companion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computational experience with several representative sample problems is described, demonstrating the effectiveness of the proposed approach.
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a feasible trust region Sequential Quadratic Programming algorithm
Siam Journal on Optimization, 2004Co-Authors: Stephen J Wright, Matthew J TennyAbstract:An algorithm for smooth nonlinear constrained optimization problems is described, in which a sequence of feasible iterates is generated by solving a trust-region Sequential Quadratic Programming (SQP) subproblem at each iteration and by perturbing the resulting step to retain feasibility of each iterate. By retaining feasibility, the algorithm avoids several complications of other trust-region SQP approaches: the objective function can be used as a merit function, and the SQP subproblems are feasible for all choices of the trust-region radius. Global convergence properties are analyzed under various assumptions on the approximate Hessian. Under additional assumptions, superlinear convergence to points satisfying second-order sufficient conditions is proved.
E I Uskov - One of the best experts on this subject based on the ideXlab platform.
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subspace stabilized Sequential Quadratic Programming
Computational Optimization and Applications, 2017Co-Authors: A F Izmailov, E I UskovAbstract:The stabilized Sequential Quadratic Programming (SQP) method has nice local convergence properties: it possesses local superlinear convergence under very mild assumptions not including any constraint qualifications. However, any attempts to globalize convergence of this method indispensably face some principal difficulties concerned with intrinsic deficiencies of the steps produced by it when relatively far from solutions; specifically, it has a tendency to produce long sequences of short steps before entering the region where its superlinear convergence shows up. In this paper, we propose a modification of the stabilized SQP method, possessing better "semi-local" behavior, and hence, more suitable for the development of practical realizations. The key features of the new method are identification of the so-called degeneracy subspace and dual stabilization along this subspace only; thus the name "subspace-stabilized SQP". We consider two versions of this method, their local convergence properties, as well as a practical procedure for approximation of the degeneracy subspace. Even though we do not consider here any specific algorithms with theoretically justified global convergence properties, subspace-stabilized SQP can be a relevant substitute for the stabilized SQP in such algorithms using the latter at the "local phase". Some numerical results demonstrate that stabilization along the degeneracy subspace is indeed crucially important for success of dual stabilization methods.
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globalizing stabilized Sequential Quadratic Programming method by smooth primal dual exact penalty function
Journal of Optimization Theory and Applications, 2016Co-Authors: A F Izmailov, M V Solodov, E I UskovAbstract:An iteration of the stabilized Sequential Quadratic Programming method consists in solving a certain Quadratic program in the primal-dual space, regularized in the dual variables. The advantage with respect to the classical Sequential Quadratic Programming is that no constraint qualifications are required for fast local convergence (i.e., the problem can be degenerate). In particular, for equality-constrained problems, the superlinear rate of convergence is guaranteed under the only assumption that the primal-dual starting point is close enough to a stationary point and a noncritical Lagrange multiplier (the latter being weaker than the second-order sufficient optimality condition). However, unlike for the usual Sequential Quadratic Programming method, designing natural globally convergent algorithms based on the stabilized version proved quite a challenge and, currently, there are very few proposals in this direction. For equality-constrained problems, we suggest to use for the task linesearch for the smooth two-parameter exact penalty function, which is the sum of the Lagrangian with squared penalizations of the violation of the constraints and of the violation of the Lagrangian stationarity with respect to primal variables. Reasonable global convergence properties are established. Moreover, we show that the globalized algorithm preserves the superlinear rate of the stabilized Sequential Quadratic Programming method under the weak conditions mentioned above. We also present some numerical experiments on a set of degenerate test problems.
