Description: 考虑整数线性规划问题max（ci*xi）试设计一个解此问题的动态规划算法，并分析算法的计算复杂性-Consider the integer linear programming problem max (ci* xi) try to design a solution of this problem, dynamic programming algorithm, and analysis of algorithms computational complexity Platform: |
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Author:wu |
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Description: JavaScript is an implementation of the ECMAScript language standard and is typically used to enable programmatic access to computational objects within a host environment. It can be characterized as a prototype-based object-oriented[6] scripting language that is dynamic, weakly typed and has first-class functions. It is also considered a functional programming language[1] like Scheme and OCaml because it has closures and supports higher-order functions Platform: |
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Author:darulor |
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Description: 针对传统的算法如遗传算法、粒子群算法等在TSP问题上求解精确性和求解规模上都还有一定的不足，本文提出了一种基于动态规划思想的粒子群优化算法。该算法用动态规划的方法实现粒子间的信息交互和粒子的进化，并且将粒子群中的粒子按无标度信息指导网络拓扑图的方式进行连接。仿真结果表明该方法能有效地减小误差率，提高解的精确，同时还保持了较低的计算复杂度，具有良好的稳健性。-TSP problem solving for the traditional algorithms such as genetic algorithms, particle swarm optimization accuracy and solving the scale, there is a certain lack of a dynamic programming algorithm-based particle swarm optimization algorithm. The algorithm using dynamic programming method to achieve the evolution of information exchange between the particles and the particles, and particle swarm particle scale-free information guide network topology map to connect. The simulation results show that this method can effectively reduce the error rate and improve the solution precision, while maintaining a low computational complexity, good soundness. Platform: |
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Author:程徐 |
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Description: Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian
domains. It amounts to an incremental method for dynamic programming which imposes limited computational
demands. It works by successively improving its evaluations of the quality of particular actions at particular states.
This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins
(1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions
are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions
to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed
each iteration, rather than just one. Platform: |
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Author:amin |
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Description: 给定n种物品和一背包。物品i的重量是wi，体积是bi，其价值为vi，背包的容量为c，容积为d。问应如何选择装入背包中的物品，使得装入背包中物品的总价值最大？在选择装入背包的物品时，对每种物品i只有两种选择，即装入背包或者不装入背包。不能将物品i装入背包多次，也不能只装入部分的物品i。试设计一个解此问题的动态规划算法，并分析算法的计算复杂性。(Given n items and a knapsack. The weight of the item I is wi, the volume is Bi, its value is VI, the capacity of the knapsack is C, the volume is d. How do you choose to choose the items in the backpack to make the most of the total value of the items in the backpack? When choosing a backpack, there are only two options for each item I, that is, to be loaded into a backpack or not to be loaded into a backpack. You can't load an item I into a backpack many times, and you can't just load a part of the item I. A dynamic programming algorithm for solving this problem is designed and the computational complexity of the algorithm is analyzed.) Platform: |
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Author:为了
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