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Dynamic Programming (dynamic + programming)
Kinds of Dynamic Programming Terms modified by Dynamic Programming Selected AbstractsOut-of-Core and Dynamic Programming for Data Distribution on a Volume Visualization ClusterCOMPUTER GRAPHICS FORUM, Issue 1 2009S. Frank I.3.2 [Computer Graphics]: Distributed/network graphics; C.2.4 [Distributed Systems]: Distributed applications Abstract Ray directed volume-rendering algorithms are well suited for parallel implementation in a distributed cluster environment. For distributed ray casting, the scene must be partitioned between nodes for good load balancing, and a strict view-dependent priority order is required for image composition. In this paper, we define the load balanced network distribution (LBND) problem and map it to the NP-complete precedence constrained job-shop scheduling problem. We introduce a kd-tree solution and a dynamic programming solution. To process a massive data set, either a parallel or an out-of-core approach is required. Parallel preprocessing is performed by render nodes on data, which are allocated using a static data structure. Volumetric data sets often contain a large portion of voxels that will never be rendered, or empty space. Parallel preprocessing fails to take advantage of this. Our slab-projection slice, introduced in this paper, tracks empty space across consecutive slices of data to reduce the amount of data distributed and rendered. It is used to facilitate out-of-core bricking and kd-tree partitioning. Load balancing using each of our approaches is compared with traditional methods using several segmented regions of the Visible Korean data set. [source] Incorporating Penalty Function to Reduce Spill in Stochastic Dynamic Programming Based Reservoir Operation of Hydropower PlantsIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 5 2010Deependra Kumar Jha Non-member Abstract This paper proposes a framework that includes a penalty function incorporated stochastic dynamic programming (SDP) model in order to derive the operation policy of the reservoir of a hydropower plant, with an aim to reduce the amount of spill during operation of the reservoir. SDP models with various inflow process assumptions (independent and Markov-I) are developed and executed in order to derive the reservoir operation policies for the case study of a storage type hydropower plant located in Japan. The policy thus determined consists of target storage levels (end-of-period storage levels) for each combination of the beginning-of-period storage levels and the inflow states of the current period. A penalty function is incorporated in the classical SDP model with objective function that maximizes annual energy generation through operation of the reservoir. Due to the inclusion of the penalty function, operation policy of the reservoir changes in a way that ensures reduced spill. Simulations are carried out to identify reservoir storage guide curves based on the derived operation policies. Reservoir storage guide curves for different values of the coefficient of penalty function , are plotted for a study horizon of 64 years, and the corresponding average annual spill values are compared. It is observed that, with increasing values of ,, the average annual spill decreases; however, the simulated average annual energy value is marginally reduced. The average annual energy generation can be checked vis-à-vis the average annual spill reduction, and the optimal value of , can be identified based on the cost functions associated with energy and spill. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] RESERVOIR OPERATION ANI EVALUATION OF DOWNSTREAM FLOW AUGMENTATION,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2001Mahesh Kumar Sahu ABSTRACT: Operation of a storage-based reservoir modifies the downstream flow usually to a value higher than that of natural flow in dry season. This could be important for irrigation, water supply, or power production as it is like an additional downstream benefit without any additional investment. This study addresses the operation of two proposed reservoirs and the downstream flow augmentation at an irrigation project located at the outlet of the Gandaki River basin in Nepal. The optimal operating policies of the reservoirs were determined using a Stochastic Dynamic Programming (SDP) model considering the maximization of power production. The modified flows downstream of the reservoirs were simulated by a simulation model using the optimal operating policy (for power maximization) and a synthetic long-term inflow series. Comparing the existing flow (flow in river without reservoir operation) and the modified flow (flow after reservoir operation) at the irrigation project, the additional amount of flow was calculated. The reliability analysis indicated that the supply of irrigation could be increased by 25 to 100 percent of the existing supply over the dry season (January to April) with a reliability of more than 80 percent. [source] Three-Dimensional Optimization of Urban Drainage SystemsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2000A. Freire Diogo A global mathematical model for simultaneously obtaining the optimal layout and design of urban drainage systems for foul sewage and stormwater is presented. The model can handle every kind of network, including parallel storm and foul sewers. It selects the optimal location for pumping systems and outfalls or wastewater treatment plants (defining the natural and artificial drainage basins), and it allows the presence of special structures and existing subsystems for optimal remodeling or expansion. It is possible to identify two basic optimization levels: in the first level, the generation and transformation of general layouts (consisting of forests of trees) until a convergence criterion is reached, and in the second level, the design and evaluation of each forest. The global strategy adopted combines and develops a sequence of optimal design and plan layout subproblems. Dynamic programming is used as a very powerful technique, alongside simulated annealing and genetic algorithms, in this discrete combinatorial optimization problem of huge dimension. [source] Exploring the performance of massively multithreaded architecturesCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 5 2010Shahid Bokhari Abstract We present a new scheme for evaluating the performance of multithreaded computers and demonstrate its application to the Cray MTA-2 and XMT supercomputers. Our scheme is based on the concept of clock cycles per element, , plotted against both problem size and the number of processors. This scheme clearly shows if an implementation has achieved its asymptotic efficiency and is more general than (but includes) the commonly used speedup metric. It permits the discovery of any imperfections in both the software as well as the hardware, and is expected to permit a unified comparison of many different parallel architectures. Measurements on a number of well-known parallel algorithms, ranging from matrix multiply to quicksort, are presented for the MTA-2 and XMT and highlight some interesting differences between these machines. The performance of sequence alignment using dynamic programming is evaluated on the MTA-2, XMT, IBM x3755 and SGI Altix 350 and provides a useful comparison of the capabilities of the Cray machines with more conventional shared memory architectures. Copyright © 2009 John Wiley & Sons, Ltd. [source] Optimal eradication: when to stop looking for an invasive plantECOLOGY LETTERS, Issue 7 2006Tracey J. Regan Abstract The notion of being sure that you have completely eradicated an invasive species is fanciful because of imperfect detection and persistent seed banks. Eradication is commonly declared either on an ad hoc basis, on notions of seed bank longevity, or on setting arbitrary thresholds of 1% or 5% confidence that the species is not present. Rather than declaring eradication at some arbitrary level of confidence, we take an economic approach in which we stop looking when the expected costs outweigh the expected benefits. We develop theory that determines the number of years of absent surveys required to minimize the net expected cost. Given detection of a species is imperfect, the optimal stopping time is a trade-off between the cost of continued surveying and the cost of escape and damage if eradication is declared too soon. A simple rule of thumb compares well to the exact optimal solution using stochastic dynamic programming. Application of the approach to the eradication programme of Helenium amarum reveals that the actual stopping time was a precautionary one given the ranges for each parameter. [source] An Equilibrium Theory of Learning, Search, and WagesECONOMETRICA, Issue 2 2010Francisco M. Gonzalez We examine the labor market effects of incomplete information about the workers' own job-finding process. Search outcomes convey valuable information, and learning from search generates endogenous heterogeneity in workers' beliefs about their job-finding probability. We characterize this process and analyze its interactions with job creation and wage determination. Our theory sheds new light on how unemployment can affect workers' labor market outcomes and wage determination, providing a rational explanation for discouragement as the consequence of negative search outcomes. In particular, longer unemployment durations are likely to be followed by lower reemployment wages because a worker's beliefs about his job-finding process deteriorate with unemployment duration. Moreover, our analysis provides a set of useful results on dynamic programming with optimal learning. [source] Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domainELECTROPHORESIS, Issue 13 2008Gerardo A. Ceballos Abstract A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization. Next, a local alignment algorithm is applied on the coded sequences for peak pattern recognition. We also propose 2-D and 3-D representations of the found patterns for fast visual evaluation of the variability of chemical substances concentration in the analyzed samples. The proposed approach is tested on the analysis of intracerebral microdialysate data obtained by CE and LIF detection, achieving a correct detection rate of about 85% with a processing time of less than 0.3,s per 25,000-point electropherogram. Using a local alignment algorithm on low-resolution denoised electropherograms might have a great impact on high-throughput CE since the proposed methodology will substitute automatic fast pattern recognition analysis for slow, human based time-consuming visual pattern recognition methods. [source] Incorporating Penalty Function to Reduce Spill in Stochastic Dynamic Programming Based Reservoir Operation of Hydropower PlantsIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 5 2010Deependra Kumar Jha Non-member Abstract This paper proposes a framework that includes a penalty function incorporated stochastic dynamic programming (SDP) model in order to derive the operation policy of the reservoir of a hydropower plant, with an aim to reduce the amount of spill during operation of the reservoir. SDP models with various inflow process assumptions (independent and Markov-I) are developed and executed in order to derive the reservoir operation policies for the case study of a storage type hydropower plant located in Japan. The policy thus determined consists of target storage levels (end-of-period storage levels) for each combination of the beginning-of-period storage levels and the inflow states of the current period. A penalty function is incorporated in the classical SDP model with objective function that maximizes annual energy generation through operation of the reservoir. Due to the inclusion of the penalty function, operation policy of the reservoir changes in a way that ensures reduced spill. Simulations are carried out to identify reservoir storage guide curves based on the derived operation policies. Reservoir storage guide curves for different values of the coefficient of penalty function , are plotted for a study horizon of 64 years, and the corresponding average annual spill values are compared. It is observed that, with increasing values of ,, the average annual spill decreases; however, the simulated average annual energy value is marginally reduced. The average annual energy generation can be checked vis-à-vis the average annual spill reduction, and the optimal value of , can be identified based on the cost functions associated with energy and spill. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Optimization of Train Speed Profile for Minimum Energy ConsumptionIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 3 2010Masafumi Miyatake Member Abstract The optimal operation of railway systems minimizing total energy consumption is discussed in this paper. Firstly, some measures of finding energy-saving train speed profiles are outlined. After the characteristics that should be considered in optimizing train operation are clarified, complete optimization based on optimal control theory is reviewed. Their basic formulations are summarized taking into account most of the difficult characteristics peculiar to railway systems. Three methods of solving the formulation, dynamic programming (DP), gradient method, and sequential quadratic programming (SQP), are introduced. The last two methods can also control the state of charge (SOC) of the energy storage devices. By showing some numerical results of simulations, the significance of solving not only optimal speed profiles but also optimal SOC profiles of energy storage are emphasized, because the numerical results are beyond the conventional qualitative studies. Future scope for applying the methods to real-time optimal control is also mentioned. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Inf,sup control of discontinuous piecewise affine systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 13 2009J. Spjøtvold Abstract This paper considers the worst-case optimal control of discontinuous piecewise affine (PWA) systems, which are subjected to constraints and disturbances. We seek to pre-compute, via dynamic programming, an explicit control law for these systems when a PWA cost function is utilized. One difficulty with this problem class is that, even for initial states for which the value function of the optimal control problem is finite, there might not exist a control law that attains the infimum. Hence, we propose a method that is guaranteed to obtain a sub-optimal solution, and where the degree of sub-optimality can be specified a priori. This is achieved by approximating the underlying sub-problems with a parametric piecewise linear program. Copyright © 2008 John Wiley & Sons, Ltd. [source] Shortest path stochastic control for hybrid electric vehiclesINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 14 2008Edward Dean Tate Jr Abstract When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP-SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite-horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time-invariant causal state-feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite-horizon case, and for the same level of complexity as a discounted future-cost controller, the shortest-path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd. [source] Stochastic unit commitment problemINTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 1 2004Takayuki Shiina Abstract The electric power industry is undergoing restructuring and deregulation. We need to incorporate the uncertainty of electric power demand or power generators into the unit commitment problem. The unit commitment problem is to determine the schedule of power generating units and the generating level of each unit. The objective is to minimize the operational cost which is given by the sum of the fuel cost and the start-up cost. In this paper we propose a new algorithm for the stochastic unit commitment problem which is based on column generation approach. The algorithm continues adding schedules from the dual solution of the restricted linear master program until the algorithm cannot generate new schedules. The schedule generation problem is solved by the calculation of dynamic programming on the scenario tree. [source] Linear and non-linear optimization models for allocation of a limited water supply,IRRIGATION AND DRAINAGE, Issue 1 2004Bijan Ghahraman optimisation de l'irrigation; déficit d'irrigation; Iran Abstract One partial solution to the problem of ever-increasing demands on our water resources is optimal allocation of available water. A non-linear programming (NLP) optimization model with an integrated soil water balance was developed. This model is the advanced form of a previously developed one in which soil water balance was not included. The model also has the advantage of low computer run-time, as compared to commonly used dynamic programming (DP) models that suffer from dimensionality. The model can perform over different crop growth stages while taking into account an irrigation time interval in each stage. Therefore, the results are directly applicable to real-world conditions. However, the time trend of actual evapotranspiration (AET) for individual time intervals fluctuates more than that for growth-stage AETs. The proposed model was run for the Ardak area (45,km NW of the city of Mashhad, Iran) under a single cropping cultivation (corn) as well as a multiple cropping pattern (wheat, barley, corn, and sugar beet). The water balance equation was manipulated with net applied irrigation water to overcome the difficulty encountered with incorrect deep percolation. The outputs of the model, under the imposed seasonal irrigation water shortages, were compared with the results obtained from a simple NLP model. The differences between these two models (simple and integrated) became more significant as irrigation water shortage increased. Oversimplified assumptions in the previous simple model were the main causes of these differences. Copyright © 2004 John Wiley & Sons, Ltd. L'allocation optimale des ressources d'eau disponibles est une réponse partielle au problème de la demande sans cesse croissante de consommation d'eau. Un modèle d'optimisation à programmation non linéaire (NLP) qui intègre un bilan hydrique a été développé. Ce modèle est une version avancée d'un modéle précédent qui n'intégrait pas ce bilan hydrique. Il présente l'avantage de nécessiter moins de puissance informatique en comparaison des modèles à programmation dynamique (DP) généralement utilisés. Le modèle peut s'appliquer à différentes étapes de la croissance des cultures et prend en compte des fréquences d'irrigation variables. Ainsi, les résultats sont directement applicables aux conditions réelles. Le modèle proposé a été utilisé sur une seule culture (maïs) dans la région d'Ardak à 45,km nord-ouest de Mashad, Iran, et sur de multiples cultures (blé, orge, maïs, betterave sucrière). L'équation de bilan hydrique a été calibrée pour maîtriser les difficultés rencontrées avec des mesures d'infiltration incorrectes. Les résultats du modèle, dans le cadre de restrictions d'irrigation saisonnière imposées, ont été comparés avec ceux obtenus par un modèle simple NLP. Les différences entre ces deux modèles (simple et intégré) deviennent plus significatives à mesure que les restrictions d'irrigation augmentent. Les hypothèses trop simplistes du modèle simple sont la cause de ces différences. Copyright © 2004 John Wiley & Sons, Ltd. [source] Modeling and simulation of vehicle projection arrival,discharge process in adaptive traffic signal controlsJOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2010Fang Clara Fang Abstract Real-time signal control operates as a function of the vehicular arrival and discharge process to satisfy a pre-specified operational performance. This process is often predicted based on loop detectors placed upstream of the signal. In our newly developed signal control for diamond interchanges, a microscopic model is proposed to estimate traffic flows at the stop-line. The model considers the traffic dynamics of vehicular detection, arrivals, and departures, by taking into account varying speeds, length of queues, and signal control. As the signal control is optimized over a rolling horizon that is divided into intervals, the vehicular detection for and projection into the corresponding horizon intervals are also modeled. The signal control algorithm is based on dynamic programming and the optimization of signal policy is performed using a certain performance measure involving delays, queue lengths, and queue storage ratios. The arrival,discharge model is embedded in the optimization algorithm and both are programmed into AIMSUN, a microscopic stochastic simulation program. AIMSUN is then used to simulate the traffic flow and implement the optimal signal control by accessing internal data including detected traffic demand and vehicle speeds. Sensitivity analysis is conducted to study the effect of selecting different optimization criteria on the signal control performance. It is concluded that the queue length and queue storage ratio are the most appropriate performance measures in real-time signal control of interchanges. Copyright © 2010 John Wiley & Sons, Ltd. [source] Computation and analysis of multiple structural change modelsJOURNAL OF APPLIED ECONOMETRICS, Issue 1 2003Jushan Bai In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T2) for any number of breaks. Our method can be applied to both pure and partial structural change models. Second, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program. Copyright © 2002 John Wiley & Sons, Ltd. [source] Approximate dynamic programming based optimal control applied to an integrated plant with a reactor and a distillation column with recycleAICHE JOURNAL, Issue 4 2009Thidarat Tosukhowong Abstract An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small-scale process control applications. The offline computational procedure of ADP constructs an approximation of the optimal "cost - to - go" function, which parameterizes the optimal control policy with respect to the state variable. With the approximate "cost - to - go" function computed, a multistage optimization problem that needs to be solved online at every sample time can be reduced to a single-stage optimization, thereby significantly lessening the real-time computational load. Moreover, stochastic uncertainties can be addressed relatively easily within this framework. Nonetheless, the existing ADP method requires excessive offline computation when applied to a high-dimensional system. A case study of a reactor and a distillation column with recycle was used to illustrate this issue. Then, several ways were proposed to reduce the computational load so that the ADP method can be applied to high-dimensional integrated plants. The results showed that the approach is much more superior to NMPC in both deterministic and stochastic cases. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Accurate shape from focus based on focus adjustment in optical microscopyMICROSCOPY RESEARCH AND TECHNIQUE, Issue 5 2009Seong-O Shim Abstract Optical microscopy allows a magnified view of the sample while decreasing the depth of focus. Although the acquired images from limited depth of field have both blurred and focused regions, they can provide depth information. The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus,sharpness,is the crucial part for final 3D shape estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that have approximately the same initial depth as of the center pixel and then it re-adjusts the center value accordingly. Experiments were conducted on synthetic and microscopic objects, and the results show that the proposed technique generates better shape and takes less computation time in comparison with previous SFF methods based on focused image surface (FIS) and dynamic programming. Microsc. Res. Tech., 2009. © 2008 Wiley-Liss, Inc. [source] SOLVING DYNAMIC WILDLIFE RESOURCE OPTIMIZATION PROBLEMS USING REINFORCEMENT LEARNINGNATURAL RESOURCE MODELING, Issue 1 2005CHRISTOPHER J. FONNESBECK ABSTRACT. An important technical component of natural resource management, particularly in an adaptive management context, is optimization. This is used to select the most appropriate management strategy, given a model of the system and all relevant available information. For dynamic resource systems, dynamic programming has been the de facto standard for deriving optimal state-specific management strategies. Though effective for small-dimension problems, dynamic programming is incapable of providing solutions to larger problems, even with modern microcomputing technology. Reinforcement learning is an alternative, related procedure for deriving optimal management strategies, based on stochastic approximation. It is an iterative process that improves estimates of the value of state-specific actions based in interactions with a system, or model thereof. Applications of reinforcement learning in the field of artificial intelligence have illustrated its ability to yield near-optimal strategies for very complex model systems, highlighting the potential utility of this method for ecological and natural resource management problems, which tend to be of high dimension. I describe the concept of reinforcement learning and its approach of estimating optimal strategies by temporal difference learning. I then illustrate the application of this method using a simple, well-known case study of Anderson [1975], and compare the reinforcement learning results with those of dynamic programming. Though a globally-optimal strategy is not discovered, it performs very well relative to the dynamic programming strategy, based on simulated cumulative objective return. I suggest that reinforcement learning be applied to relatively complex problems where an approximate solution to a realistic model is preferable to an exact answer to an oversimplified model. [source] What you should know about approximate dynamic programmingNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 3 2009Warren B. Powell Abstract Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well-known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009 [source] Modeling the operation of multireservoir systems using decomposition and stochastic dynamic programmingNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 3 2006T.W. Archibald Abstract Stochastic dynamic programming models are attractive for multireservoir control problems because they allow non-linear features to be incorporated and changes in hydrological conditions to be modeled as Markov processes. However, with the exception of the simplest cases, these models are computationally intractable because of the high dimension of the state and action spaces involved. This paper proposes a new method of determining an operating policy for a multireservoir control problem that uses stochastic dynamic programming, but is practical for systems with many reservoirs. Decomposition is first used to reduce the problem to a number of independent subproblems. Each subproblem is formulated as a low-dimensional stochastic dynamic program and solved to determine the operating policy for one of the reservoirs in the system. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006 [source] Solving multi-objective dynamic optimization problems with fuzzy satisfying methodOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 5 2003Cheng-Liang Chen Abstract This article proposes a novel algorithm integrating iterative dynamic programming and fuzzy aggregation to solve multi-objective optimal control problems. First, the optimal control policies involving these objectives are sequentially determined. A payoff table is then established by applying each optimal policy in series to evaluate these multiple objectives. Considering the imprecise nature of decision-maker's judgment, these multiple objectives are viewed as fuzzy variables. Simple monotonic increasing or decreasing membership functions are then defined for degrees of satisfaction for these linguistic objective functions. The optimal control policy is finally searched by maximizing the aggregated fuzzy decision values. The proposed method is rather easy to implement. Two chemical processes, Nylon 6 batch polymerization and Penicillin G fed-batch fermentation, are used to demonstrate that the method has a significant potential to solve real industrial problems. Copyright © 2003 John Wiley & Sons, Ltd. [source] Stochastic optimization for the ruin probabilityPROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2003Manfred Schäl Prof. Dr. rer. nat. The Cramér-Lundberg insurance model is studied where the risk process can be controlled by reinsurance and by investment in a financial market. The performance criterion is the ruin probability. The problem can be imbedded in the framework of discrete-time stochastic dynamic programming. Basic tools are the Howard improvement and the verification theorem. Explicit conditions are obtained for the optimality of employing no reinsurance and of not investing in the market. [source] Alignment of protein sequences by their profilesPROTEIN SCIENCE, Issue 4 2004Marc A. Marti-Renom Abstract The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences. [source] Prediction of the transmembrane regions of ,-barrel membrane proteins with a neural network-based predictorPROTEIN SCIENCE, Issue 4 2001Irene Jacoboni Abstract A method based on neural networks is trained and tested on a nonredundant set of ,-barrel membrane proteins known at atomic resolution with a jackknife procedure. The method predicts the topography of transmembrane , strands with residue accuracy as high as 78% when evolutionary information is used as input to the network. Of the transmembrane ,-strands included in the training set, 93% are correctly assigned. The predictor includes an algorithm of model optimization, based on dynamic programming, that correctly models eight out of the 11 proteins present in the training/testing set. In addition, protein topology is assigned on the basis of the location of the longest loops in the models. We propose this as a general method to fill the gap of the prediction of ,-barrel membrane proteins. [source] Flexible and Robust Implementations of Multivariate Adaptive Regression Splines Within a Wastewater Treatment Stochastic Dynamic ProgramQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2005Julia C. C. Tsai Abstract This paper presents an automatic and more robust implementation of multivariate adaptive regression splines (MARS) within the orthogonal array (OA)/MARS continuous-state stochastic dynamic programming (SDP) method. MARS is used to estimate the future value functions in each SDP level. The default stopping rule of MARS employs the maximum number of basis functions Mmax, specified by the user. To reduce the computational effort and improve the MARS fit for the wastewater treatment SDP model, two automatic stopping rules, which automatically determine an appropriate value for Mmax, and a robust version of MARS that prefers lower-order terms over higher-order terms are developed. Computational results demonstrate the success of these approaches. Copyright © 2005 John Wiley & Sons, Ltd. [source] Further developments in the new approach to boundary condition iteration in optimal controlTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 6 2001Rein LuusArticle first published online: 3 SEP 2010 Abstract In solving the boundary value problem resulting from the use of Pontryagin's maximum principle, a transformation matrix is used to relate the sensitivity of the final state to the initial state. This avoids the need to solve the (n × n) differential equation to give the transition matrix, and yields very rapid convergence to the optimum. To ensure convergence, iterative dynamic programming (IDP) is used for a number of passes to yield good starting conditions for this boundary condition iteration procedure. Clipping technique is used to handle constraints on control. Five optimal control problems are used to illustrate and to test the procedure. Dans la résolution du problème de valeur limlte résultant de l'utilisation du principe maximum de Pontryagin, on utilise une matrice de transformation afin de relier la sensibilité de l'état final à l'état initial. Cela évite d'avoir à résoudre l'équation différentielle (n × n) pour obtenir la matrice de transition et permet une convergence trés rapide vers l'optimum. Pour assurer la convergence, on a recours à la programmation dynamique itérative (IDP) pour plusieurs passages afin de créer de bonnes conditions de démarrage pour cette méthode d'itération sur les conditions limites. On utilise la technique de l'écêtage pour manier les contraintes sur le contrôle. Cinq problèmes de contrôle optimal permettent d'illustrer et de vérifier la méthode. [source] Bang-Bang solution of nonlinear time-optimal control problems using a semi-exhaustivesearchTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 1 2001Yash P. GuptaArticle first published online: 27 MAR 200 Abstract At times, the objective is to seek a bang-bang control policy for nonlinear time-optimal control problems. The usefulness of iterative dynamic programming (IDP) has been shown in the literature for solving such problems. However, the convergence to the optimal solution has been obtained from about 50% of the guessed values near the optimum. In this paper, we present a semiexhaustive search method for seeking such solutions and a comparison is made with the IDP. The results show that the convergence can be obtained from a significantly higher number of guessed values chosen over a much wider region around the optimum. Dans certains cas, l'objectif est de chercher une méthode de contr,le bang-bang pour les problèmes de contr,le optimal en temps non linéaire. L'utilité de la programmation dynamique itérative (IDP) a été illustrée dans la littérature scientifique dans le but de résoudre de tels problèmes. Toutefois, la convergence de la solution optimale a été obtenue à environ 50% des valeurs estimées près de l'optimum. Dans cet article, on présente une méthode de recherche semi-exhaustive pour la recherche de telles solutions et une comparaison est faite avec l'IDP. Les résultats montrent que la convergence peut ,tre obtenue à partir d'un nombre beaucoup plus grand de valeurs estimées dans une région beaucoup plus large autour de l'optimum. [source] DISCRETIZED SUB-OPTIMAL TRACKER FOR NONLINEAR CONTINUOUS TWO-DIMENSIONAL SYSTEMSASIAN JOURNAL OF CONTROL, Issue 3 2004Chia-Wei Chen ABSTRACT The discretized quadratic sub-optimal tracker for nonlinear continuous two-dimensional (2-D) systems is newly proposed in this paper. The proposed method provides a novel methodology for indirect digital redesign for nonlinear continuous 2-D systems with a continuous performance index. This includes the following features: (1) the 2-D optimal-linearization approach of the nonlinear 2-D Roesser's model (RM), (2) the dynamic programming-based discretized quadratic optimal tracker for linear continuous 2-D systems, (3) the steady-state discretized quadratic sub-optimal tracker for linear continuous 2-D systems, and (4) the discretized quadratic sub-optimal tracker for nonlinear continuous 2-D systems. Illustrative examples are presented to demonstrate the effectiveness of the proposed procedure. [source] | ||||||||||||||||||||||