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iterative dynamic programming: A new class of query optimization algorithms

ACM TRANSACTIONS ON DATABASE SYSTEMS 2000年第1期25卷 43-82页

作者： Kossmann, D Stocker, K Univ Passau Lehrstuhl Dialogorientierte Syst D-94030 Passau Germany

The query optimizer is one of the most important components of a database system. Most commercial query optimizers today are based on a dynamic-programming algorithm, as proposed in Selinger et al. [1979]. While this algorithm produces good optimization results (i.e., good plans), its high complexity can be prohibitive if complex queries need to be processed, new query execution techniques need to be integrated, or in certain programming environments (e.g., distributed database systems). In this paper, we present and thoroughly evaluate a new class of query optimization algorithms that are based on a principle that we call iterative dynamic programming, or IDP for short. IDP has several important advantages: First, IDP-algorithms produce the best plans of all known algorithms in situations in which dynamic programming is not viable because of its high complexity. Second, some IDP variants are adaptive and produce as good plans as dynamic programming if dynamic programming is viable and as-good-as possible plans if dynamic programming turns out to be not viable. Three, all IDP-algorithms can very easily be integrated into an existing optimizer which is based on dynamic programming.

关键词： algorithms performance query optimization dynamic programming iterative dynamic programming randomized optimization greedy algorithm plan evaluation function

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iterative dynamic-programming FOR MINIMUM ENERGY CONTROL-PROBLEMS WITH TIME-DELAY

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OPTIMAL CONTROL APPLICATIONS & METHODS 1995年第3期16卷 217-227页

作者： DADEBO, SA MCAULEY, KB QUEENS UNIV DEPT CHEM ENGNKINGSTONON K7L 3N6CANADA

This paper presents the use of iterative dynamic programming employing exact penalty functions for minimum energy control problems. We show that exact continuously non-differentiable penalty functions are superior to continuously differentiable penalty functions in terms of satisfying final state constraints. We also demonstrate that the choice of an appropriate penalty function factor depends on the relative size of the time delay with respect to the final time and on the expected value of the energy consumption. A quadratic approximation (QA) of the delayed variables is much better than a linear approximation (LA) of the same for relatively large time delays. The QA improves the rate of convergence and avoids the formation of 'kinks'. A more general way of selecting appropriate penalty function factors is given and the results obtained using four illustrative examples of varying complexity corroborate the efficacy of the method.

关键词： iterative dynamic programming ABSOLUTE ERROR PENALTY FUNCTION QUADRATIC APPROXIMATION TIME DELAY SYSTEMS MINIMUM ENERGY CONTROL

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iterative dynamic programming for solving linear and nonlinear differential equations

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APPLIED MATHEMATICS AND COMPUTATION 2006年第1期175卷 247-257页

作者： Effati, S. Roohparvar, H. Teacher Training Univ Sabzevar Dept Math Sabzevar Iran

In this paper we solve a wide range of nth order ordinary nonlinear differential equations with initial conditions by using iterative dynamic programming. First we transform the problem to a first order system of ordinary nonlinear differential equations, and then transform it to an optimal control problem. By the optimal solution of the problem we obtain a piecewise-constant optimal control function and then by using the approximate control signals, we obtain the approximate optimal trajectory. Finally, we calculate the error functional E. (c) 2005 Elsevier Inc. All rights reserved.

关键词： differential equations optimal control iterative dynamic programming

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Optimal control of polymer flooding based on mixed-integer iterative dynamic programming

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INTERNATIONAL JOURNAL OF CONTROL 2011年第11期84卷 1903-1914页

作者： Lei, Yang Li, Shurong Zhang, Xiaodong Zhang, Qiang Guo, Lanlei China Univ Petr Coll Informat & Control Engn E China Dongying Peoples R China Sinopec Shengli Oilfield Co Res Inst Geol Sci Dongying Peoples R China

Polymer flooding is one of the most important technologies for enhanced oil recovery. In this article, a mixed-integer optimal control model of distributed parameter systems (DPS) for the injection strategies is established, which involves the performance index as maximum of the profit, the governing equations as the fluid flow equations of polymer flooding and some inequalities constraints, such as polymer concentration and injection amount limitation. The control variables are the volume size, the injection concentration of each slug and the terminal flooding time. For the constant injection rate, the slug size is determined by the integer time stage length, and thus the integer variables are introduced in the DPS. To cope with the optimal control problem (OCP) of this DPS, a mixed-integer iterative dynamic programming incorporating a special truncation procedure to handle integer restrictions on stage lengths is proposed. First, the OCP with variable time stage lengths is transformed into a fixed time stage problem by introducing a normalised time variable. Then, the optimisation procedure is carried out at each stage and preceded backwards in a systematic way. Finally, the numerical results of an example illustrate the effectiveness of the proposed method.

关键词： optimal control polymer flooding iterative dynamic programming mixed-integer

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Demand subscription services - An iterative dynamic programming for the substation suffering from capacity shortage

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IEEE TRANSACTIONS ON POWER SYSTEMS 2003年第2期18卷 947-953页

作者： Huang, KY Cheng Shiu Inst Technol Dept Elect Engn Kaohsiung 833 Taiwan

The iterative dynamic programming (IDP) is presented in this paper to carry out the demand subscription services (DSS) for reducing the peak load of the substation, which is suffering from capacity shortage. The optimal or near optimal schedule of the interruptible load can be obtained by the enumerative search structure of the algorithm. Besides,the shortcoming of the enumerative approach, in which the enormous memory is needed, can also be circumvented in light of the iterative procedure. Moreover, for avoiding the influence of the uncertainties of the practical load on the results of the IDP, the heuristic inference rules (HIR) are introduced in the paper. The approach cannot only adjust the schedule of the interruptible load to enhance the robustness for the uncertainties but also possess the ability of real-time operation. Three cases of demand subscription services are implemented through the proposed algorithms. The results show that the interruptible load scheduling can reduce the system load effectively and the load capacity reduced by the DSS follows close on the trajectory of the peak load.

关键词： demand subscription services heuristic inference rules iterative dynamic programming real-time operation

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An iterative dynamic programming optimization based on biorthogonal spatial-temporal Hammerstein modeling for the enhanced oil recovery of ASP flooding

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JOURNAL OF PROCESS CONTROL 2019年 73卷 75-88页

作者： Li, Shurong Ge, Yulei Shi, Yuhuan Beijing Univ Posts & Telecommun Automat Sch Beijing 100876 Peoples R China Qingdao Topscomm Commun Co Ltd Qingdao 266071 Peoples R China CNPC EastChina Design Inst Co Ltd Qingdao 266071 Peoples R China China Univ Petr East China Coll Informat & Control Engn Qingdao 266580 Peoples R China

In this paper, an iterative dynamic programming (IDP) based on biorthogonal spatial-temporal Hammerstein modeling is developed to solve the enhanced oil recovery for alkali-surfactant-polymer (ASP) flooding. At first, the biorthogonal spatial-temporal Hammerstein model is presented to build the relation between the inputs and states, in which the Hammerstein model is expanded on a set of spatial basis functions and temporal basis functions. After inferring the necessary condition of solutions, these basis functions are determined by the snapshots method. Then, the least square estimation and singular value decomposition is used to identify the parameters in Hammerstein model. In addition, Auto-Regressive and Moving Average (ARMA) model is applied to build the model between states and outputs, whose parameters are identified by recursive least squares. At last, IDP algorithm is applied to solve the enhanced oil recovery problem for ASP flooding based on the identification model. Simulation verifies the accuracy and effectiveness of proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Enhanced oil recovery ASP flooding Biorthogonal spatial-temporal decomposition Distributed Hammerstein model Recursive least square iterative dynamic programming

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Numerical solution of time-delayed optimal control problems by iterative dynamic programming

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OPTIMAL CONTROL APPLICATIONS & METHODS 2000年第3期21卷 91-105页

作者： Chen, CL Sun, DY Chang, CY Natl Taiwan Univ Dept Chem Engn Taipei 10617 Taiwan

This work presents a numerical method to solve the optimal control problem with time-delayed arguments and a fixed terminal time. A series Of auxiliary states obtained from the linearly truncated Taylor series expansion are used to represent the status of a time-delayed state at different time intervals. The backward iterative dynamic programming (IDP) technique can thus be directly employed to solve the delay-free optimal control problem with augmented states. Five numerical examples are provided, demonstrating the proposed method's effectiveness in solving the time-delayed optimal control problems. Copyright (C) 2000 John Wiley & Sons, Ltd.

关键词： iterative dynamic programming time-delayed systems Taylor series expansion optimal control

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APPLICATION OF iterative dynamic-programming TO TIME-OPTIMAL CONTROL

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CHEMICAL ENGINEERING RESEARCH & DESIGN 1994年第A1期72卷 72-80页

作者： BOJKOV, B LUUS, R UNIV TORONTO DEPT CHEM ENGNTORONTO M5S 1A4ONCANADA

A procedure for the establishment of time optimal control policies for systems described by ordinary differential equations is presented. The method consists of introducing an augmented performance index and using iterative dynamic programming (IDP), in conjunction with a modified false position method to search for the minimum final time t(f). Numerical investigation of three examples indicate that results close to the time optimal control policy can be obtained without difficulty, even when the systems are highly nonlinear. This procedure is especially attractive if a personal computer is used for the computations.

关键词： TIME OPTIMAL CONTROL iterative dynamic programming

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Numerical solution of dynamic optimization problems with flexible inequality constraints by iterative dynamic programming

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FUZZY SETS AND SYSTEMS 2002年第2期127卷 165-176页

作者： Chen, CL Sun, DY Chang, CY Natl Taiwan Univ Dept Chem Engn Taipei 10617 Taiwan

A solution strategy for optimizing the dynamic systems with flexible inequality constraints is proposed, To apply fuzzy inference in solution, the flexible portion in the problem is treated as fuzzy constraints. After functional values are bounded in a region, the objective function of this problem can also be fuzzified easily. When the problem is formulated as a fuzzy dynamic optimization problem, the iterative dynamic programming integrated with fuzzy inference is adopted to find the solution. Two examples are employed, demonstrating the facility of the proposed algorithm. (C) 2002 Elsevier Science B,V. All fights reserved.

关键词： fuzzy mathematical programming fuzzy constraint satisfaction iterative dynamic programming

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A Real-Time Battery Thermal Management Strategy for Connected and Automated Hybrid Electric Vehicles (CAHEVs) Based on iterative dynamic programming

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第9期67卷 8077-8084页

作者： Zhu, Chong Lu, Fei Zhang, Hua Sun, Jing Mi, Chunting Chris San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

Connected and automated hybrid electric vehicles (CAHEVs) are a potential solution to the future transportation due to their improved fuel economy, reduced emissions, and capability to mitigate congestion and improve safety. The battery thermal management (BTM) in CAHEVs is one of the crucial problems, because the lithium-ion batteries are highly temperature sensitive. Therefore, a practical and energy-efficient BTM strategy is required for both improving the operating temperature of batteries and saving energy. In this study, the dynamic programming (DP) is implemented for a BTM system in CAHEVs for achieving the optimal cooling/heating energy savings for batteries. To enhance the real-time capability, an iterative approach is proposed to approximate the optimum control strategy iteratively in a multidimensional search space. The proposed iterative DP strategy can improve the system performance and energy-efficiency by fully exploiting the future road information in CAHEVs combined with a model predictive control method. The hardware-in-the-loop validation of the proposed strategy is conducted on the UDDS and the WLTC drive cycles based on a Toyota Prius PHEV model. The results demonstrate the feasibility and effectiveness of the proposed BTM strategy that leads to a considerable BTM energy reduction.

关键词： Battery thermal management (BTM) connected and automated hybrid electric vehicle (CAHEV) energy saving iterative dynamic programming

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