利用动态规划算法解决购物单问题

购物单问题是0-1背包问题的一种应用,解决购物单问题已有贪婪法,动态规划法,蚁群算法,回溯法等.动态规划算法是求解决策过程最优化的方法,通常用于求解具有某种最优性质的问题.根据动态规划原理解决购物单问题,说明了动态规划算法解决实际生活问题的高效性.     

国际大学生程序设计竞赛试题与算法分析(三)──动态规划及其应用──最短路问题

动态规划实际上是研究一类最优化问题的方法，在经济、工程技术、企业管理、工农业生产及军事等领域中都有广泛的应用。近年来，在ＡＣＭ／ＩＣＰＣ中，使用动态规划（或部分应用动态规划思维）求解的题不仅常见，而且形式也多种多样。而在与此相近的各类信息学竞赛中，应用动态规划解题已经成为一种趋势，这和动态规划的优势不无关系。 与其说动态规划是一种算法，不如说是一种思维方法来得贴切。因为动态规划没有固定的框架，即便是应用到同一道题上，也可以建立多种形式的求解算法。许多隐式图上的算法，例如求单源最短路径的 Ｄｉｊｋｓ…     

精确求解一类动态规划问题的神经网络

根据一类动态规划问题的特点，提出一种能够精确求解此问题的神经网络。ＬＤＰＮＮ具有结构简单、易于硬件实现、求解速度快并且能够求得精确最优解等优点，特点适合于大规模动态规划问题的求解。在复杂系统的实时优化与控制等方面具有广阔的应用前景。     

动态规划的思想及其在图像缩放中的应用

动态规划经常用于求解某些具有最优性质的问题。如今随着对动态规划算法的日渐深入的研究,动态规划被用在生产调度等各个方面。该文介绍了动态规划的基本思想,包括动态规划模型的基本要素,动态规划的特点及设计一个动态规划算法的基本步骤。同时,结合Seam Caving图像缩放方法[1],具体介绍了动态规划算法在图像缩放方面的应用。     

系统动力学仿真技术在区域规划决策中的实际应用

系统动力学(System Dynamics)仿真技术是研究社会、经济、管理、生态等非工程系统的有力工具,在国外已被成功地用于工业企业管理(工业动力学)、城市规划(城市动力学)、世界发展趋势(世界动力学)等各种问题的研究。本文介绍用系统动力学仿真技术建立地区人口-经济发展的动态模型及其在我国某地区发展规划决策中的实际应用。     

精确求解一类动态规划问题的神经网络

根据一类动态规划问题（ＤＦＤＰ）的特点，提出一种能够精确求解此问题的神经网络（ＬＤＰＮＮ）。ＬＤＰＮＮ具有结构简单、易于硬件实现、求解速度快并且能够求得精确最优解等优点，特别适合于大规模动态规划问题的求解。在复杂系统的实时优化与控制等方面具有广阔的应用前景。     

供应链中生产商与零售商决策问题的研究

在需求率是价格的函数,产品存在常数腐烂率,允许缺货且缺货部分补足的假设下,本文分别建立了供应链上生产商与零售商各自单独决策和双方联合决策的优化决策模型,并且应用数学规划方法证明了双方联合决策比单独决策能够给供应链能带来更多的经济利润.最后用数值例子验证了模型的有效性.     

最短路径动态规划问题及C语言实现探讨

动态规划算法是一种研究多阶段决策问题的算法.用动态规划方法求最短路问题,要求所求问题具有明显的阶段。该文以动态规划理论为指导,研究了动态规划算法求解最短路径的基本原理及步骤,编写了基于动态规划算法的C语言程序,辅助完成最短路径的求解。     

运用遗传规划解决异或问题的新方法*

介绍了一种用遗传规划这种新的搜索优化技术解决经典异或问题的新途径.遗传规划实质是使用广义的计算机程序来描述问题,并且可以根据环境状况动态改变计算机程序的结构.根据遗传规划特征,引入两种思路、三种方法对异或问题进行求解,取得了很好的效果.与神经网络相比,遗传规划可以动态进化学习并取得显式的数学表达式.     

基于不定期决策过程的最短路径搜索算法

不定期决策过程具有广泛的应用领域。该文针对基于不定期决策过程的段数不定线路网,在动态规划的基础上,提出了SPUCN算法(ShortestPathProblemofUncertainColumnNetwork),该算法通过对UCN模型进行分析转换,建立起能够以动态规划基本方程进行分析求解的理想模型,然后对该模型进行动态规划的后向分析求解,文章给出了算法的正确性和理论性证明,最后通过实例验证了算法的有效性。     

A reconfigurable manager for dynamically reconfigurable hardware

Dynamic reconfiguration has been a technology solution in search of the right problem to solve. Effective use of the technology requires new programming and task management models. This article describes an approach to dynamic reconfiguration that reduces reconfiguration latency to the point where dynamic multimedia applications can now exploit such platforms.     

Applications of dynamic programming and other optimization methods in pest management

Pest management is a multistage decision process in a stochastic and observable system. A control model of a pest ecosystem is characterized by discontinuous cost functions and nonlinear, stochastic state equations describing the interactions among a large namber of ecosystem components. Dynamic programming has been the optimization technique which has been most widely applied to pest management analysis, but several other optimization methods have also proven useful. This paper reviews a number of data-based applications of optimization methods to pest management. The comparative advantages of alternative optimization methods are discussed.     

ARM嵌入式系统C语言编程

无操作系统支持的嵌入式系统软件，包括系统引导(BOOT)、驱动程序、动态内存管理、I／O、通信以及应用软件等方面。本文详细介绍了嵌入式平台上用C语言编写系统软件和应用软件的方法。虽然是针对ARM平台介绍的，但基本经验和算法也适合于其他嵌入式平台的软件设计。     

Dynamic programming in Datalog with aggregates

Dynamic programming is a general technique for solving optimization problems. It is based on the division of problems into simpler subproblems that can be computed separately. In this paper, we show that Datalog with aggregates and other nonmonotonic constructs can express classical dynamic programming optimization problems in a natural fashion, and then we discuss the important classes of queries and applications that benefit from these techniques     

基于PowerBuilder的动态报表设计

在开发MIS系统时需要创建大量报表,这些报表具有不确定性,其格式、内容和数量经常根据需求发生变化。文章提出了一种新的动态报表生成技术,即采用空模版与报表描述文件组合和动态合成方法,根据用户的需求,快速方便地增加、修改报表。该技术为MIS的开发和应用提供了有效的解决工具。该文详细介绍了基于PowerBuilder动态报表设计的方法和编程要点等。     

Approximate Dynamic Programming for Self-Learning Control

This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950’s for solving optimal control problems of nonlinear dynamical systems. Due to its high computational complexity, the applications of dynamic programming have been limited to simple and small problems. The key step in finding approximate solutions to dynamic programming is to estimate the performance index in dynamic programming. The optimal control signal can then be determined by minimizing (or maximizing) the performance index. Artificial neural networks are very efficient tools in representing the performance index in dynamic programming. This paper assumes the use of neural networks for estimating the performance index in dynamic programming and for generating optimal control signals, thus to achieve optimal control through self-learning.     

基于Agent的网络分布式动态防护体系

从系统科学的观点出发，提出了网络分布式动态防护体系的概念，研究了其基于系统工程与面向Agent的设计思想和方法。据此设计了基于Agent的分布式动态防护体系，论述了数据获取、信息处理、决策响应与辅助等Agent的功能原理及其动态协作关系。该体系具有本质的智能性和动态性，能有效检测并阻断多种入侵，实现了分布动态防护。     

An EMSR-based approach for revenue management with integrated upgrade decisions

We consider the revenue management problem of capacity control with integrated upgrade decision-making. The dynamic programming formulation of this problem is hard to solve to optimality, even in the single-leg case, because multiple hierarchical resource types must be considered simultaneously. Therefore, in this paper, we propose a new heuristic approach that generalizes the idea behind the well-known single-leg EMSR-a procedure to multiple resource types. Similar to EMSR-a, our approach is based on the computation of protection levels, but additionally allows for the integrated consideration of upgrades. In addition, we derive control policies for typical demand arrival patterns. As an extension, we propose a generalization of our approach that allows for arbitrarily ordered prices with respect to the upgrade hierarchy. Furthermore, we perform a number of computational experiments to investigate the performance of the new approach compared to other capacity control methods that incorporate upgrades. We consider typical airlines′ single-leg scenarios with 10 (re)optimizations throughout the booking horizon. The results show that our approach can significantly outperform existing methods in terms of the total achieved revenue, including dynamic programming decomposition approaches that are proposed in literature, as well as successive planning approaches that are widely used in commercial revenue management systems.     

A Common Medium for Programming Operations-Research Models

Operations research is an interdisciplinary science that develops scientific methods for modeling and solving decision-making problems. Arguably, the two most established and commonly practiced OR methodologies are mathematical programming (also called optimization) and discrete-event system simulation (called simulation). Mathematical programming describes a decision problem as decision variables and as mathematical functions of variables that capture the objective and problem constraints. For dynamic systems that are too complex to allow exact, closed-form solutions in continuous time, simulation can identify and describe events of interest and model their occurrences at discrete points in time. Python can glue together independent software environments to improve the software development cycle for sophisticated operations-research applications, which integrate different modeling methodologies.     

Using dynamic programming for solving variational problems invision

Dynamic programming is discussed as an approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, is numerically stable, and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the approach's efficacy, applying dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a time-delayed discrete dynamic programming algorithm. A parallel procedure for decreasing computational costs is discussed