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About 10,000 papers for “dynamic programming algorithm”
Camilla Colombo, Massimiliano Vasile, Gianmarco Radice · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · 2008
Presentation on trajectory and mission design
Cited by 0
Markus Salchegger · 1995
It is the main purpose of this paper to indicate a new algorithm for the optimization of a short-term hydro unit calculation problem. Methods for such problems are usually based on heuristic or mixed/heuristic mathematical approaches. This algorithm is based on BELLMAN's principle of optimality (dynamic programming) and avoids the use of any heuristic methods. A standard model for a hydro unit system (HUS) is presented. Because of the high requirements on the computation speed and memory (BELLMAN's curse of dimensionality) our contribution here is to indicate a method and an algorithm to reduce them. This should help to increase the number of systems for which such a purely mathematical approach will be technically applicable. Improvements will be achieved in the algorithmic structure (using an iterative Discrete Dynamic Programming Number Theoretical Numerics Parallel Computation Iterative Concept approach), the efficiency of the lattice generation (using number theoretical numerics t...
Cited by 0
Ming Li, Ruizhi Chen, Weilong Zhang et al. · Sensors · 2017
Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images.
Cited by 14Open access
Derong Liu, Qinglai Wei · IEEE Transactions on Neural Networks and Learning Systems · 2013
This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.
Cited by 731
Ziwei Zhao, Yan Piao, Che Liu · 2019
In recent years, as a related technology, stereo display(3D) has been growing more and more rapidly with the development of internet. However, some problems have arisen, either. To solve the problem about holes and poor matching in the process of virtual viewpoint generation, a new method for improving the disparity map of Dynamic Programming algorithm (DP) is proposed. Firstly, the RGB images of the left and right viewpoints are color-divided in the Hue-Intensity-Saturation space (HIS), and then the images is stereo-matched by the proposed method that DP algorithm and the Sum of absolute differences algorithm (SAD) to generate an initial disparity map. The cost function based on HIS space is added to the calculation process of matching cost, which increases the precision of disparity calculation. Then the linear interpolation and median filtering are used for eliminating the holes in the disparity map. The experimental simulation shows that compared with the traditional algorithm, the improved algorithm optimizes the quality of the synthetic disparity map, and reduces the mismatching rate. In addition to these, compared with standard disparity map, the improved method preserves the information of the initial image to some extent.
Cited by 0
Jian-Nan Lin, Yong-Zai Lu · International Journal of Systems Science · 1988
This paper addresses the development of a dynamic programming decomposition-coordination algorithm for the optimal control of discrete-time large-scale systems with multiple time delays. The systems under study can be converted into the corresponding augmented systems satisfying the Markov processes denning the augmented state vectors. The resulting dynamic programming can then be further decomposed into a series of subproblems which might be solved in parallel by combining the dynamic programming techniques with approaches to matrix partition. The entire system's solution is finally given by the iterative computation between the subproblems being the local control level and the coordinator. The major advantages of the proposed algorithm over others are to avoid two-point boundary-value problems of the systems with multiple time delays and to significantly reduce the computer memory and time required for such large-scale systems optimal control problems.
Cited by 0
Manuel S. Santos, J. Vigo‐Aguiar · Econometrica · 1998
In this paper we develop a discretized version of the dynamic programming algorithm and study its convergence and stability properties. We show that the computed value function converges quadratically to the true value function and that the computed policy function converges linearly, as the mesh size of the discretization converges to zero; further, the algorithm is stable. We also discuss several aspects of the implementation of our procedures as applied to some commonly studied growth models.
Cited by 119
Bautista Valhondo, Joaquín, Cano Pérez, Alberto, Companys Pascual, Ramón et al. · RECERCAT (Consorci de Serveis Universitaris de Catalunya)
We present some results attained with two variants of bounded dynamic programming algorithm to solve the Fm|block|Cmax problem using as experimental data the wellknown\nTaillard instances. We have improved the best known solutions for four of the Taillard’s instances.
Cited by 0Open access
Yuxuan Liu, Man Yue Lam, Reza Ahmadian · Renewable Energy · 2026
Optimising the operation and design of tidal range schemes is a long-standing and critical problem in their design which also affects their financial feasability. The optimisation process is very time consuming considering its complexity and the number of variables which leads to significant simplifications in the process. Enhancing the efficiency and accuracy of optimisation methods is essential for maximising their energy output. In this study, we propose an efficient tabular dynamic programming for tidal range scheme optimisation by refining the algorithm. Dynamic programming is capable of finding the global optimal solution given that the assumptions used to formulate the optimisation problem are valid. The algorithm refined in this study was tested with two different schemes, namely the West Somerset Lagoon and the North Wales Lagoon in the UK, and demonstrated accurate and efficient prediction of the scheme’s operation. The computational time required by the dynamic programming algorithm was significantly lower than the most promising optimisation approaches used for tidal range schemes, namely Genetic Algorithm, showing potential for real-time application. The refined algorithm includes provisions to simulate tidal range schemes with block formulation to account for the fact that turbines and sluice gates are usually installed in different blocks and are subjected to tidal phase differences. Results demonstrated that optimisation including the multi-block formulation lead to electricity generation up to 6% more annually. • First dynamic programming approach for multi-block tidal range scheme operations. • Operation strategy guaranteed globally optimal. • Tested on West Somerset and North Wales lagoons, with real-time potential.
Cited by 0Open access
Augustine O. Esogbue, Nazir A. Warsi · Computers & Mathematics with Applications · 1988
Cited by 0
Wai-sing James So · 2010
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Toby Dylan Hocking, Guillem Rigaill · 2017
A quadratic time dynamic programming algorithm can be used to compute an approximate solution to the problem of finding the most likely changepoints with respect to the Poisson likelihood, subject to a constraint on the number of segments, and the changes which must alternate: up, down, up, down, etc. For more info read <<a href="http://proceedings.mlr.press/v37/hocking15.html" target="_top">http://proceedings.mlr.press/v37/hocking15.html</a>> "PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data" by TD Hocking et al, proceedings of ICML2015.
Cited by 1Open 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(n n ) possible partitions. To date, the only algorithm that can find an optimal solution in O(3 n ) 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 0
Blagoj Delipetrev, Andréja Jonoski, Dimitri Solomatine · Journal of Hydroinformatics · 2015
In this article we present a novel nested dynamic programming (nDP) algorithm for multipurpose reservoir optimization with additional decision variables related to different water users. The nDP algorithm is built from two algorithms: (1) dynamic programming (DP) and (2) nested optimization algorithm implemented with Simplex and quadratic Knapsack methods. The novel idea is to include a nested optimization algorithm into the DP transition that reduces the initial problem dimension and alleviates the DP's curse of dimensionality. The nDP can solve multi-objective optimization problems, without significantly increasing the algorithm complexity and the computational expenses. Computationally, the nDP can handle dense and irregular variable discretization; it is coded in Java as a prototype application and has been successfully tested with eight objectives at the Knezevo reservoir in the Republic of Macedonia. The article presents a discussion on comparison of nDP with other DP methods and highlights the advantages of nDP.
Cited by 8Open access
Chun‐Hung Chen, Shi‐Chung Chang, I‐Kong Fong · 1989
This paper presents a new differential dynamic programming (DDP) algorithm for solving constrained optimal control problems. The basic idea is to relax all constraints but the system dynamics by using the multiplier method. For a given set of multipliers, there is an unconstrained dynamic optimization problem to which DDP applies effectively. The optimal solution is obtained by iteratively updating multipliers and solving the corresponding dynamic optimization problem. This new algorithm is convergent, easy to implement, and applicable to problems with quite general constraints and system dynamics.
Cited by 5
Ting‐Li Chien, Hsin-Chou Lai, Yung-Chien Lin et al. · 2011
The article researched the path planning of the multiple mobile robots moving to the target position in the unknown environment, and used the dynamic programming algorithm to solve the minimum displacement techniques on the supervised computer. The mobile robot has the shape of cylinder and its diameter, height and weight is 10cm, 15cm and 1.5kg. The controller of the mobile robot is MCS-51 microchip, and programs Keil-C language to control the mobile robot. The mobile robot contains a controller module (including two DC motor drivers), an obstacle detection module, a voice module, a wireless RF module, an encoder module, and a compass module. The mobile robot can modify the error of the orientation using the compass module, and uses encoder module to calculate and compensate the moving distance. We design the user interface of the multiple mobile robot system, and search the minimum distance using the proposed method on the experimental platform. In the experimental results, mobile robots can receive the command from the supervised computer via wireless RF interface, and move to the unknown target position, and find the optimal path on the many times test according to the proposed algorithm.
Cited by 4
팀 주제정보서비스 · 2020
Cited by 0
Biing-Feng Wang · SSRN Electronic Journal · 2022
Cited by 0Open access
Joseph L. Ganley, James P. Cohoon · 2002
An exact rectilinear Steiner minimal tree algorithm is presented that improves upon the time and space complexity of previous guarantees and is easy to implement. Experimental evidence is presented that demonstrates that the algorithm also works well in practice.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cited by 19
Hyeong Soo Chang, Min Fu, Steven I. Marcus · 2003
We present a novel algorithm, called "Simulated Annealing Multiplicative Weights", for approximately solving large (discrete-time) finite-horizon stochastic dynamic programming problems. The algorithm is "asymptotically efficient" in the sense that a finite-time bound for the sample mean of the optimal value function over a given finite policy space can be obtained, and the bound approaches the optimal value as the number of iterations increases. The algorithm updates a probability distribution over the given policy space with a very simple rule, and the sequence of distributions generated by the algorithm converges to a distribution concentrated only on the optimal policies for the given policy space. We also discuss how to reduce the computational cost of the algorithm to apply it in practice.
Cited by 8