dynamic programming (DP) is a fundamental and powerful algorithmic paradigm taught in most undergraduate (and many graduate) algorithms classes. DP problems are challenging for many computer science students because t...
详细信息
ISBN:
(纸本)9798400705311
dynamic programming (DP) is a fundamental and powerful algorithmic paradigm taught in most undergraduate (and many graduate) algorithms classes. DP problems are challenging for many computer science students because they require identifying unique problem structures and a refined understanding of recursion. In this paper, we present dpvis, a Python library that helps students understand DP through a frame-by-frame animation of dynamic programs. dpvis can easily generate animations of dynamic programs with as little as two lines of modifications compared to a standard Python implementation. For each frame, dpvis highlight the cells that have been read from and written to during an iteration. Moreover, dpvis allows users to test their understanding by prompting them with questions about the next operation performed by the algorithm. We deployed dpvis as a learning tool in an undergraduate algorithms class, and report on the results of a survey. The survey results suggest that dpvis is especially helpful for visualizing the recursive structure of DP. Although some students struggled with the installation of the tool (which has been simplified since the reported deployment), essentially all other students found the tool to be useful for understanding dynamic programs. dpvis is available at https://***/itsdawei/dpvis.
A fundamental task in conformance checking is to compute optimal alignments between a given event log and a process model. In general, it is known that this unavoidably incurs high computational costs which, in turn, ...
详细信息
Stable and efficient video object trajectory tracking can fully reflect video information and has been widely used in different fields. As a novel study, this research proposes the data capture method of sports video ...
详细信息
CRISPR-based lineage tracing has emerged as a promising approach for studying cell transformations during development and disease progression. However, the high ratio of cells to mutations, coupled with missing data f...
详细信息
This work addresses the problem of jointly optimizing the binary offloading decision making and the communication resource allocation for a system using time-division multiple access (TDMA). The goal is to minimize th...
详细信息
In this paper, we develop a decision model based on dynamic programming to create an optimal crop plan for 2024-2030 in rural northern China. Considering different marketing scenarios, uncertainties, and crop substitu...
详细信息
ISBN:
(纸本)9798400711862
In this paper, we develop a decision model based on dynamic programming to create an optimal crop plan for 2024-2030 in rural northern China. Considering different marketing scenarios, uncertainties, and crop substitutions and complementarities, this paper solves the problem with three sub-problems. First, the optimal cropping plan for 2024-2030 is calculated based on the cropping scenario for 2023. It is assumes that expected sales, cropping costs, yields and marketing prices are stable. Secondly, a multi-objective planning model was developed. It aimed at profitability and stability and took into account uncertainties. Finally, the planting plan was further optimised and it considered crop substitutability and complementarity, and the relationship between expected sales and selling prices and planting costs. Through dynamic planning and Monte Carlo simulation, this paper takes into account uncertainty and crop relationships and it solves the optimal crop planting plan for the years 2024-2030 with a maximum profit of about $7.737 million. Therefore, this paper proposes a decision model based on dynamic programming and it takes into account various factors of the planting plan for the next seven years. An effective method is provided for solving the crop planting strategy problem in sustainable agricultural development.
dynamic programming (DP) is one of the most challenging topics in algorithms courses. Although there exist animation tools that assist with the understanding of DP algorithms, few existing tools are aimed at scaffoldi...
详细信息
This paper presents a switching control strategy as a criterion for policy selection in stochastic dynamic programming problems over an infinite time horizon. In particular, the Bellman operator, applied iteratively t...
详细信息
This paper presents a switching control strategy as a criterion for policy selection in stochastic dynamic programming problems over an infinite time horizon. In particular, the Bellman operator, applied iteratively to solve such problems, is generalized to the case of stochastic policies, and formulated as a discrete-time switched affine system. Then, a Lyapunov-based policy selection strategy is designed to ensure the practical convergence of the resulting closed-loop system trajectories towards an appropriately chosen reference value function. This way, it is possible to verify how the chosen reference value function can be approached by using a stabilizing switching signal, the latter defined on a given finite set of stationary stochastic policies. Finally, the presented method is applied to the Value Iteration algorithm, and an illustrative example of a recycling robot is provided to demonstrate its effectiveness in terms of convergence performance. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
In this paper, a novel data-driven optimal control method based on reinforcement learning concepts is introduced. The proposed algorithm performs as a workaround to solving the Hamilton-Jacobi-Bellman equation. The ma...
详细信息
In this paper, a novel data-driven optimal control method based on reinforcement learning concepts is introduced. The proposed algorithm performs as a workaround to solving the Hamilton-Jacobi-Bellman equation. The main concept behind the proposed algorithm is the so-called IsoCost hypersurface (ICHS), which is a hypersurface in the state space of the system formed by points from which a specific amount of cost is spent by the control strategy in order to asymptotically stabilize the system. The fact that the control strategy requires to spend equal costs in order to stabilize all points on an ICHS is the reason for the naming of the IsoCost concept. Additional assumptions and definitions are mentioned before providing the theory of ICHS optimality. This theory proves, by contradiction, that the ICHS corresponding to the optimal control policy surrounds the ICHSs corresponding to other non-optimal control solutions. This paves the path to finding the optimal control solution using dynamic programming. The proposed method is implemented on the linear, fixed-base inverted pendulum, cart-pole and torsional pendulum bar system models and the results are compared with that of literature. The performance of this method in terms of cost, settling time and computation time is shown using numeric and illustrative comparisons.
Cut-off grade for a mineral deposit is a grade that is used to classify the material as ore or waste. In order to get maximum profit from a mineral deposit, an optimum schedule of cut-off grades must be used. This pap...
详细信息
Cut-off grade for a mineral deposit is a grade that is used to classify the material as ore or waste. In order to get maximum profit from a mineral deposit, an optimum schedule of cut-off grades must be used. This paper describes the use of dynamic programming in cut-off grades optimisation and further extension of the method regarding with depletion rates and rehabilitation cost. Partial depletion rates have been used for calculating mining and rehabilitation costs. The software developed in this work guarantees the global optimum for different depletion rates.
暂无评论