Jingjing Fan, Jianying Guo, Wang Li et al. · 2019
Dynamic programming is mainly used to solve the optimization problem of dynamic process divided into stages by time, which is very suitable for the fuel economy optimization of hybrid electric bus powertrain under fixed working cycle. In this paper, a dynamic programming algorithm for power distribution of hybrid electric bus powertrain system is proposed. Taking fuel economy of powertrain as objective function, throttle opening, motor torque command, clutch command and engine speed change command as control variables, battery state of charge, clutch state and engine speed as state variables, a mathematical model for dynamic programming optimal control is established. Then the optimal control model is solved and control laws are extracted to form a dynamic programming algorithm for power distribution of hybrid electric bus system. A real vehicle test is carried out, and the set target cycle condition is selected as the bus condition of a typical city in China, and the correctness and practicability of the developed dynamic programming algorithm are tested and verified. The dynamic programming algorithm is applied to drum test, and the test results show that the dynamic programming algorithm improves the fuel economy by 31.5% compared with the logic-based method.
Cited by 3
Haleh Shahzad, Ted H. Szymanski · 2016
Computational offloading can improve the energy efficiency of mobile devices, by executing some tasks of a mobile application in the cloud. In this paper, a new algorithm called 'Dynamic Programming with Randomization' (DPR) is presented. The DPR algorithm iteratively improves an offloading decision vector, by generating random bit strings with a biased probability of generating 0s, which represent a decision to offload a task. If fragments of these bit strings improve the decision vector, they are incorporated into the decision vector (which is similar to genetic optimization). The DPR algorithm also uses a hamming distance termination criterion, with a preference to offload tasks, to find a nearly-optimal offloading solution quickly. The DPR algorithm will offload as many tasks as possible to the cloud server when the network transmission bandwidth is high, thereby improving the total execution time of all tasks and minimizing the energy consumption of the mobile device. The DPR algorithm can find excellent quality solutions with low computational overhead, by using biased randomization. Furthermore, the DPR algorithm can scale to handle larger offloading problems without loosing computational efficiency or solution quality, as the computational time grows linearly (with a slope less than unity) with the problem size. Performance evaluation shows that the proposed DPR algorithm can minimize energy requirements while meeting an application's execution time constraints, and it is able to find a nearly-optimal offloading decision vector in a few iterations.
Cited by 12
Jian Li · Applied Mechanics and Materials · 2013
This paper puts forward an improved dynamic programming algorithm for bitonic TSP and it proves to be correct. Divide the whole loop into right-and-left parts through analyzing the key point connecting to the last one directly; then construct a new optimal sub-structure and recursion. The time complexity of the new algorithm is O ( n 2 ) and the space complexity is O ( n ); while both the time and space complexities of the classical algorithm are O ( n 2 ). Experiment results showed that the new algorithm not only reduces the space requirement greatly but also increases the computing speed by 2-3 times compared with the classical algorithm.
Cited by 3
Hans Strauβ, Gastón H. Gonnet · Repository for Publications and Research Data (ETH Zurich) · 1991
Cited by 0Open access
Alexander Yumaganov, Anton Agafonov, Vladislav Myasnikov · Annals of Computer Science and Information Systems · 2020
GPS sensors embedded in almost all mobile devices and vehicles generate a large amount of data that can be used in both practical applications and transportation research. Despite the high accuracy of location measurements in 3-5 meters on average, this data can not be used for practical use without preprocessing. The preprocessing step that is needed to identify the correct path as a sequence of road segments by a series of location measurements and road network data is called map matching. In this paper, we consider the offline map matching problem in which the whole trajectory is processed after it has been collected. We propose a map matching algorithm based on a dynamic programming approach. The experimental studies on the dataset collected in Samara, Russia, showed that the proposed algorithm outperforms other comparable algorithms in terms of accuracy.
Cited by 5Open access
Straub, Hans, Gonnet, Gaston H. · 1991
Cited by 0Open access
Mary W. Cooper, Keyvan Farhangian · Computers & Mathematics with Applications · 1984
Cited by 4
Jessica R. Creveling, Carling C. Hay, Cedric Hagen · Abstracts with programs - Geological Society of America · 2019
Cited by 0
Yusri Ikhwani, As`ary Ramadhan, Muhammad Bahit et al. · Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer · 2023
Finding the best single-elimination tournament design is important in scientific inquiry because it can have major financial implications for event organizers and participants. This research aims to create an optimal single-elimination tournament design using binary tree modeling with dummy techniques. Dynamic programming algorithms have been used to compute optimal single-elimination designs to overcome this effectively. This research method uses various implementations of sub-optimal algorithms and then compares their performance in terms of runtime and optimality as a solution to measure the comparison of sub-algorithms. This research shows that the difference in relative costs produced by various sub-algorithms with the same input is quite low. This is expected because quotes are generated as integer values from a small interval 1, ≤ 9, whereas costs tend to reach much higher values. From the comparison of these sub-algorithms, the best results among the sub-optimal algorithms were obtained in the Sub Optimal algorithm 3. We present the experimental findings achieved using the Python implementation of the suggested algorithm, with a focus on the best single-elimination tournament design solution.
Cited by 2Open access
R Alur, T Henzinger, G Fainekos et al. · Arizona State University Library Digital Repository (Arizona State University) · 2013
abstract: In this thesis we deal with the problem of temporal logic robustness estimation. We present a dynamic programming algorithm for the robust estimation problem of Metric Temporal Logic (MTL) formulas regarding a finite trace of time stated sequence. This algorithm not only tests if the MTL specification is satisfied by the given input which is a finite system trajectory, but also quantifies to what extend does the sequence satisfies or violates the MTL specification. The implementation of the algorithm is the DP-TALIRO toolbox for MATLAB. Currently it is used as the temporal logic robust computing engine of S-TALIRO which is a tool for MATLAB searching for trajectories of minimal robustness in Simulink/ Stateflow. DP-TALIRO is expected to have near linear running time and constant memory requirement depending on the structure of the MTL formula. DP-TALIRO toolbox also integrates new features not supported in its ancestor FW-TALIRO such as parameter replacement, most related iteration and most related predicate. A derivative of DP-TALIRO which is DP-T-TALIRO is also addressed in this thesis which applies dynamic programming algorithm for time robustness computation. We test the running time of DP-TALIRO and compare it with FW-TALIRO. Finally, we present an application where DP-TALIRO is used as the robustness computation core of S-TALIRO for a parameter estimation problem.
Cited by 0Open access
Talal Rahwan, Nicholas R. Jennings · 2008
Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of partitioning the set of agents into exhaustive and disjoint coalitions such that the social welfare is maximized. This coalition structure generation problem is extremely challenging due to the exponential number of partitions that need to be examined. Specifically, given n agents, there are O(nn) possible partitions. To date, the only algorithm that can find an optimal solution in O(3n) is the Dynamic Programming (DP) algorithm, due to Rothkopf et al. However, one of the main limitations of DP is that it requires a significant amount of memory. In this paper, we devise an Improved Dynamic Programming algorithm (IDP) that is proved to perform fewer operations than DP (e.g. 38.7% of the operations given 25 agents), and is shown to use only 33.3% of the memory in the best case, and 66.6% in the worst.
Cited by 1
Abdelmenem S. Elgabry, Tahani M. Allam, Mahmoud Fahmy · Advances in intelligent systems and computing · 2019
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N Karthikeyan, R Gnanaprakasam, P Vimal Kumar · 2014
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Ken‐ichi Iwata, H. Yamamoto · 2021
This paper gives a methodology for optimizing AIVF (almost instantaneous variable-to-fixed length) codes based on two algorithms: The first algorithm is the dynamic programming (DP) technique developed by Dubé and Haddad to construct a set of parse trees of AIVF codes. The second algorithm is the iterative optimization algorithm proposed by Fujita, Iwata, and Yamamoto, which can maximize the average parse length of AIVF codes. As a result, the proposed AIVF code achieves a longer average parse length than the known AIVF codes and Tunstall codes.
Cited by 3
Jagiellonian University Repository (Jagiellonian University) · 2013
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P. Chen · IRE Transactions on Automatic Control · 1962
An algorithm based on the concept of state and dynamic programming is derived for designing an optimum controller for a linear plant subject to noise. The controller is optimal in the sense that the behavior of the plant satisfies the expected mean quadratic performance index (EMQPI) defined in the paper. The algorithm generates the sequence of control signals which minimize the EMQPI. In addition, it gives the minimum of the EMQPI for the specified sequence of control signals. The control signal is found to consist of two components: 1) a linear combination of the system state variables, and 2) a noise-balance component which minimizes the noise-induced deviation of the actual plant output from the desired output. An example is given to illustrate the iterative procedure and the asymptotic behavior of the algorithm. The design is optimal for a class of system inputs, and is applicable to both sampling and continuous systems. The design procedure is developed to make full use of a digital computer. The basic principles of dynamic programming to the treatment of stochastic control processes are clearly illustrated in an introductory form so that it will be of interest to control engineers who may wish to familiarize themselves with dynamic programming techniques.
Cited by 3
Nan Lin · 2009
In the process of the text feature selection,the space dimension of the original characteristics is too high,excessive calculation,and there is greater irrelevance and redundancy features,a feature selection algorithm based on dynamic programming(DPFS) was proposed.First,selecting the feature from the original feature set based on dynamic programming and the analysis of features' relevance and classes' relevance,reserve features that are the strong correlation and the weak correlation;and then,for the feature subset based on dynamic programming,delete features that are weak correlation and redundant;finally,gaining a similar best features subset.The result of experiment shows that,this method is effective for reduced data-dimension and reduces the amount of calculation in the course of reduce data-dimension.
Cited by 1
Alessandro Alla, Maurizio Falcone, Dante Kalise · PAMM · 2013
Abstract We present a scheme for Hamilton‐Jacobi‐Bellman equations based on a semi‐Lagrangian discretization and an iterative method in the policy space. The scheme exploits the idea that a good initialization of the policy iteration procedure yields a faster numerical convergence to the optimal solution. The scheme features a pre‐processing step with value iterations on a coarse grid. Numerical tests assess the efficient performance of the method. (© 2013 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Cited by 6Open access
한준희 · Open Access System for Information Sharing (Pohang University of Science and Technology) · 2018
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Guangming Tan, Guang R. Gao · 2007
Dynamic programming is an efficient technique to solve combinatorial search and optimization problem. There have been many parallel dynamic programming algorithms. The purpose of this paper is to study a family of dynamic programming algorithm where data dependence appear between non-consecutive stages, in other words, the data dependence is non-uniform. This kind of dynnamic programming is typically called nonserial polyadic dynamic programming. Owing to the non-uniform data dependence, it is harder to optimize this problem for parallelism and locality on parallel architectures. In this paper, we address the chanllenge of exploiting fine grain parallelism and locality of nonserial polyadic dynamic programming on a multi-core architecture. We present a programming and execution model for multi-core architectures with memory hierarchy. In the framework of the new model, the parallelism and locality benifit from a data dependence transformation. We propose a parallel pipelined algorithm for filling the dynamic programming matrix by decomposing the computation operators. The new parallel algorithm tolerates the memory access latency using multi-thread and is easily improved with tile technique. We formulate and analytically solve the optimization problem determing the tile size that minimizes the total execution time. The
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