Geon Cho, Dong X. Shaw · INFORMS journal on computing · 1997
The Tree Knapsack Problem (TKP) can be regarded as a 0–1 knapsack problem on a rooted tree T such that if a node is selected into a knapsack, then all nodes on the path from the selected node to the root node must also be selected into the knapsack. In this paper, we develop a pseudopolynomial-time algorithm for TKP, the depth-first dynamic programming algorithm. We prove that a TKP can be solved by our algorithm in θ(nH) time, where n is the number of nodes in T and H is the given capacity. We also report the computational results of the depth-first dynamic programming algorithm.
Cited by 45
Ming Bai, Yan Zhuang, Wei Wang · Journal of Control Theory and Applications · 2009
Cited by 4
Bodhibrata Nag · Journal of The Institution of Engineers (India) Series B · 2013
Cited by 5
Kexin Hao, Jian Li, Ziwei Wang et al. · 2024
In order to solve the problem that it is difficult and inaccurate to extract road boundaries from laser point clouds, a road boundary extraction method based on a dynamic programming algorithm is proposed. First, the point clouds are projected, and an obstacle grid map is obtained using the dual height difference algorithm to retain the ground features near the curb. Considering the distance from the boundary points to the driving trajectory, the dynamic programming algorithm is used to extract the road boundary. Starting from the driving trajectory, the algorithm searches for the white obstacle points in the grid maps on both sides, and the point with the minimum total cost is considered the boundary point. This study used the IQmulus & TerraMobilita datasets for experiments. The results showed that the accuracy and recall rates of the extraction results from both datasets were above 98.7%, and the F1 scores were both above 98.4%.
Cited by 0
Ulrike Ritzinger, Jakob Puchinger, Richard F. Hartl · HAL (Le Centre pour la Communication Scientifique Directe) · 2011
International audience
Cited by 0Open access
BAHADIR DURAK · Figshare · 2019
Dynamic Programming Algorithm for the Static Tray Splitting Problem
Cited by 0Open access
Roger K. Moore · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1979
The problems of speech recognition and orthographic word correction have been greatly mitigated by the use of dynamic programming techniques for finding the distance between two finite sequences. This paper extends the technique into two dimensions, and presents an algorithm for finding the distance between two finite areas. Applications of the algorithm are suggested.
Cited by 35
Tongtiegang Zhao, Xiaohui Lei, Yunzhong Jiang et al. · 2012
Cited by 2
E. Loukakis · International Journal of Computer Mathematics · 2003
A signed graph H =( V , E , σ ) consists of a graph G =( V , E ) together with a sign function σ : E M { m , +}. A fundamental concept in both, the theory and applications of signed graphs is that of balance . We present a new dynamic programming algorithm to detect balance in signed graphs which traverses each line of the signed graph at most once. Furthermore the computer implementation of the proposed algorithm requires three linear arrays of length | V | only, and the graph need not be stored in computer's memory. Thus the proposed algorithm is optimal with respect to both time and space complexities: The line index for balance of an unbalanced signed graph is the minimum number of lines needed to be removed to make the signed graph balanced. Barahona has proved that finding the line index for balance of a signed graph is an NP- complete problem. In this paper, we prove a stronger result, namely that the problem remains NP- complete even when restricted to signed graphs having negative signs only.
Cited by 7
R.L.J.M. van de Leensel, Olaf E. Flippo, Arie M. C. A. Koster et al. · Data Archiving and Networked Services (DANS) · 1996
Technological innovations and growing consumer demand have led to a variety of design and expansion problems in telecommunication networks. In particular, local access net- works have received a lot of attention, since they account for approximately 60% of total investments in communication facilities. In this paper we consider the Local Access Network Expansion Problem, in which growing demand can be satisfied by expanding cable capacities and/or installing concentrators in the network. The problem is known to be NP-hard. We present a pseudo-polynomial dynamic programming algorithm, with time complexity O( nB²) and storage requirements O( nB ), where n refers to the size of the network, and B to an upper bound on concentrator capacity. The cost structure in the network is assumed to be decomposable, but may be non-convex, non-concave, and node and edge dependent otherwise. Computational results indicate that the algorithm is very efficient and can solve medium to large scale problems to optimality within (fractions of) seconds to minutes.
Cited by 1Open access
Auapong Yaicharoen, Nghia Thi, Kotaro Hashikura et al. · 2025
A dynamic programming algorithm is applied to the genetic algorithm feature selection program to reduce the training time. After each generation, an accuracy score of each genome in a selected population is obtained and stored for later use. An array data structure and conversion of a genome information to an array index is used to achieve the O(1) time complexity when storing and retrieving accuracy score of each genome. The demonstrate the benefit of dynamic programming, training time data from the genetic algorithm feature selection program solving a classification problem with three machine learning models are recorded and compared. The results in one of the machine learning models show significant improvement, especially when the feature size of a data set and population size is large.
Cited by 0
Yonghui Zhang, Xinke Yang · 2023
To address the voltage distribution problem and enhance the power generation efficiency and stability of the photovoltaic system, a voltage distribution method based on dynamic programming algorithm is proposed in this study. Firstly, a comprehensive analysis of the working principle and voltage distribution characteristics of photovoltaic power optimize rs is performed, and a mathematical model is established to abstract the voltage distribution problem as a constrained optimization problem. Then, the dynamic programming algorithm is utilized to solve the optimization problem and obtain the optimal voltage distribution scheme. To validate the effectiveness of the proposed method, a series of experiments are conducted and compared with traditional static allocation methods and other dynamic allocation methods. The experimental results demonstrate that the voltage distribution method based on the dynamic programming algorithm achieves significant improvements in voltage optimization for photovoltaic power optimize rs, enabling better adaptability to varying environmental conditions and changes in light intensity, resulting in enhanced power generation efficiency and system stability.
Cited by 0
Hongjuan Wang, Chunyan Huang · 2023
With the continuous development and reform of higher education system, budget management and performance evaluation of colleges and universities become increasingly important. The purpose of this study is to explore and propose a method of university budget performance evaluation based on dynamic pr
Cited by 0Open access
Fei Gao, Hongbin Tu, Zhu Delan et al. · International journal of agricultural and biological engineering · 2024
In order to solve the immaturity of decision-making methods in the regulation of winter heating in greenhouses, this study proposed a solution to the problem of greenhouse winter heating regulation using a dynamic programming algorithm. A mathematical model that included indoor environmental state variables, optimization decision variables, and outdoor random variables was established. The temperature is kept close to the expected value and the energy consumption is low. The model predicts the control solution by considering the cost function within the next 10 steps. The two-stage planning method was used to optimize the state of each moment step by step. The temperature control strategy model was obtained by training the relationship between indoor temperature, outdoor temperature, and heating time after optimization using a regression algorithm. Based on a typical Internet of Things (IoT) structure, the greenhouse control system was designed to regulate the optimal control according to the feedback of the current environment. Through testing and verification, the optimized control method could stabilize the temperature near the target value. Compared to the threshold control (threshold interval of 2.0°C) under similar weather conditions, the optimized control method reduced the temperature fluctuation range by 0.9°C and saved 7.83 kW•h of electricity, which is about 14.56% of the total experimental electricity consumption. This shows that the dynamic programming method is feasible for environmental regulation in actual greenhouse production, and further research can be expanded in terms of decision variables and policy models to achieve a more comprehensive, scientific, and precise regulation. Key words: greenhouse; dynamic programming; heating control; model predictive control; Internet of Things DOI: 10.25165/j.ijabe.20241704.8221 Citation: Gao F, Tu H B, Zhu D L, Liu M Y, Shi C Y, Zhang R, et al. Application of dynamic programming algorithm in winter heating control of greenhouse. Int J Agric & Biol Eng, 2024; 17(4): 60–66.
Cited by 0Open access
Zeng, Henwei (author) · Research Repository (Delft University of Technology) · 2023
Several algorithms can often be used to solve a complex problem, such as the SAT problem or the graph coloring problem. Those algorithms differ in terms of speed based on the size or other features of the problem. Some algorithms perform much faster on a small size while others perform noticeably better on a larger instance. The optimization problem in this case is to select the best-performing algorithm based on the problem features, resulting in a much faster overall runtime. This is defined as the algorithm selection problem. Many different approaches have been used to solve this problem, such as constructing optimal decision trees. However, there is little published data on using optimal decision trees for algorithm selection and one study reveals a problem in finding a feasible solution on a large number of problem instances. We provide new insights into solving algorithm selection using a dynamic programming approach. The motivation to use this novel approach is that recent studies suggest it has lower scalability issues compared to the traditional optimal decision tree algorithms, due to several efficient techniques such as caching and frequency counting method. The investigation has shown that compared to the integer programming method, the dynamic programming approach is significantly faster and is able to solve large problem instances.
Cited by 0Open access
NTUR (臺灣機構典藏) · 1989
Cited by 0
Thanh Hai Nguyen, Trung Thanh Ngo · Ministry of Science and Technology Vietnam · 2025
Cited by 0Open access
Luke Riley, Katie Atkinson, Paul E. Dunne et al. · 2016
Coalition formation in characteristic function games entails agents partitioning themselves into a coalition structure and assigning the numeric rewards of each coalition via a payoff vector. Various coalition structure generation algorithms have been proposed that guarantee that an optimal coalition structure is found. We present the Synergy Coalition Group-based Dynamic Programming (SCGDP) algorithm that guarantees that an optimal coalition structure and a least core stable payoff vector is found. This is completed by extending the existing results for the Synergy Coalition Group (SCG) representation to show that only coalitions in the SCG are needed to find a weak-least core stable payoff vector. The SCGDP algorithm builds on this result by performing only the search operations necessary to guarantee that coalitions in the SCG of the given characteristic function game are found. The number of operations required is significantly less for many coalition-value distributions compared to the original Dynamic Programming (DP) algorithm [34] that finds an optimal coalition structure (e.g. only ~60% of DP's coalition lookup operations are performed in SCGDP for 18 agents using a normal coalition-value distribution). Our experimental results show that a lower bound for these operations in SCG-DP converges onto 50%. This is an increase on the ~33% bound of the optimal dynamic programming (ODP) algorithm [14], but ODP does not search for a stable solution.
Cited by 0
Yunfei Hu, Le Qi, Yuanyuan Ji et al. · Journal of Navigation · 2024
The Automatic Identification System (AIS) is extensively used in monitoring vessel traffic, and ship navigation related information can be obtained from the AIS data. However, AIS data contain extensive redundant information, which leads to the general need to compress the data when applying it in practice or conducting research. In this paper, a three-dimensional compression of ship trajectories using the Dynamic Programming algorithm has been proposed. The AIS data near the ports of Long Beach and San Francisco in the United States were used to test and compare the Dynamic Programming algorithm with the Top-down Time-ratio algorithms. The experimental results show that the proposed algorithm can better retain the position and time information at low compression ratio such as 1%, 20% and 40%. Moreover, the algorithm is applicable to ship trajectories with different motion modes such as steering, mooring and straight ahead. The results show that the proposed algorithm can reasonably solve the problem of AIS data redundancy and ensure the quality of data, which is of practical significance for water transport, transport planning and other related research.
Cited by 0
Pedro Maristany de las Casas, Antonio Sedeño‐Noda, Ralf Borndörfer · Computers & Operations Research · 2024
Cited by 3