Birdie Blog

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双层规划问题

综述和一些应用

双层规划问题 \[\begin{aligned} \min_{(x,y)}\quad &f(x,y) \\ \mathrm{s.t.}\quad &g(x,y)\leqslant0\\ \quad &y\in S(x), \end{aligned}\] 其中 $S(x)$ 表示下层问题： \[\begin{aligned} \min_{y}\quad &a...

Posted by Birdie on January 22, 2024

Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt

NIPS23 k-opt learn to imporve + mask优化不可行探索 + 双流解码器

Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt 用柔性神经k-Opt学习搜索路由问题的可行和不可行区域 开源：https://github.com/yining043/NeuOpt 摘要 在本文中，我们提出了神经k-Opt (NeuO...

Posted by Birdie on January 21, 2024

DeepACO Neural-enhanced Ant Systems for Combinatorial Optimization

NIPS23 神经蚁群系统 构造+局部搜索 泛用的元启发式

DeepACO Neural-enhanced Ant Systems for Combinatorial Optimization DeepACO:用于组合优化的神经增强蚁群系统 代码：https://github.com/henry-yeh/DeepACO 摘要 蚁群优化算法是一种元启发式算法，已成功地应用于各种组合优化问题。传统上，针对特定问题定制蚁群算法需要知识驱动的启发...

Posted by Birdie on January 20, 2024

Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift

NIPS23 集成学习提高泛化性

Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift 配送移位下基于集成的车辆路径问题深度强化学习 摘要 虽然在独立同分布情况下表现良好，但大多数现有的VRP神经方法在分布变化的情况下难以泛化。为了解决这个问题，我们提出了一种基于集成的vrp深...

Posted by Birdie on January 19, 2024

Deep Reinforcement Learning for the Electric Vehicle Routing Problem With Time Windows

EVRPTW

Deep Reinforcement Learning for the Electric Vehicle Routing Problem With Time Windows 文章原文：https://ieeexplore.ieee.org/document/9520134 发表在2022 IEEE Transactions on Intelligent Transportation Sy...

Posted by Birdie on January 18, 2024

Combinatorial Optimization with Policy Adaptation using Latent Space Search

NIPS23 潜在空间采样学习+推理时候搜索

NIPS23 Combinatorial Optimization with Policy Adaptation using Latent Space Search 基于潜在空间搜索的策略自适应组合优化 来自InstaDeep 代码和数据集： https://github.com/instadeepai/compass （我没读代码，理解的不是很透彻，没有附录感觉讲的也不是很清楚，需...

Posted by Birdie on January 17, 2024

Winner Takes It All-Training Performant RL Populations for Combinatorial Optimization

NIPS23 多智能体（种群）学习

Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization 赢家通吃:训练用于组合优化的高性能RL种群 来自InstaDeep 摘要 将强化学习(RL)应用于组合优化问题是有吸引力的，因为它不需要专家知识或预先解决的实例。然而，由于其固有的复杂性，期望智能体在一次推理中解...

Posted by Birdie on January 16, 2024

BQ-NCO Bisimulation Quotienting for Efficient Neural Combinatorial Optimization

NIPS23 新的马尔可夫决策过程表示并改进学习框架

BQ-NCO Bisimulation Quotienting for Efficient Neural Combinatorial Optimization BQ-NCO:高效神经组合优化的双模拟分法（Bisimulation Quotienting不知道中文怎么翻译） 来自Naver Labs Europe 代码：naver/bq-nco (github.com) 摘要 尽管基...

Posted by Birdie on January 16, 2024

batch manufacturing

随机过程

实验目的 Write programs to solve batch manufacturing problem Formulate and solve both discounted and average cost problems Realize both value iteration and policy iteration algorithms paramete...

Posted by Birdie on January 15, 2024

递归下降预测分析程序

编译原理 Oberon-0复杂性度量工具ALIOTH 手工编写递归下降预测分析程序

手工编写递归下降预测分析程序 1 开发环境与开发工具 1.1 操作系统 Windows 11 1.2 编程语言 java语言，JDK版本1.7.2 1.3 开发工具 Visual Studio Code + cmd 2 实验内容 2.0 实验目的 实验四要求你利用 Java 语言手工编写一个 Oberon-0 语言的语法分析程序，该语法分析程序执行与实验三自动生成的...

Posted by Birdie on June 22, 2022

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