Algorithm and Data Structure Review

本文深入探讨了常用的数据结构如数组、链表、堆、树等,并详细介绍了各种算法的基本思想、实现方式及应用场景,包括排序、搜索、图算法、字符串处理等。

常用总结。

1、常见数据结构

线性:
          数组:Merge Sorted Array
          链表:Merge k Sorted ListsPartition List
          队列,
          堆栈,
          块状数组(数组+链表)
          hash表,
          双端队列
        位图(bitmap)

树:
   二叉树: Minimum Depth of Binary Tree, Path Sum II, Inorder Travel
        堆(大顶堆、小顶堆)
          trie树(字母树or字典树)
          后缀树,
        后缀树组
          二叉排序/查找树,
          B+/B-,
        AVL树
        Treap
          红黑树
          splay树
        线段树
          树状数组

图:图

其它:并查集

2、常见算法

(1)       基本思想:枚举,
               递归: 
Flatten Binary Tree to Linked ListGenerate ParenthesesLetter Combinations of a Phone Number
               分治,
               模拟&贪心: Gray CodeInsert IntervalJump Game II, Multiply Strings, Next PermutationPalindrome NumberPascal’s Triangle II
贪心,动态规划,剪枝,回溯
               二分查找:Median of Two Sorted ArraysSearch in Rotated Sorted Array

(2)       图算法:深度优先遍历与广度优先遍历, 最短路径,最小生成树,拓扑排序

(3)       字符串算法:
                   字符串查找: Implement strStrLength Of Last WordLongest Common Prefix, 
                                     Longest Palindromic SubstringLongest Non-Repeating SubString
                   双指针: Minimum Window Substring
                   hash算法,KMP算法

(4)       排序算法:冒泡,插入,选择,快排,归并排序,堆排序,
               桶排序: First Missing Positive

(5)       动态规划:Distinct SubsequencesEdit DistanceInterleaving StringJump GameLargest Rectangle in Histogram
  背包问题,最长公共子序列,最优二分检索树

(6)       数论问题:素数问题,整数问题,进制转换,同余模运算,
                     进制转换:Integer To Roman
                     乘除法:Divide Two Integers 

(7)       排列组合:排列和组合算法

(8)       其它:LCA与RMQ问题
               
 (9) 水箱问题:Trapping Rain WaterContainer With Most Water

3. 常见设计题
(1)海量数据处理


基于遗传算法的新的异构分布式系统任务调度算法研究(Matlab代码实现)内容概要:本文档围绕基于遗传算法的异构分布式系统任务调度算法展开研究,重点介绍了一种结合遗传算法的新颖优化方法,并通过Matlab代码实现验证其在复杂调度问题中的有效性。文中还涵盖了多种智能优化算法在生产调度、经济调度、车间调度、无人机路径规划、微电网优化等领域的应用案例,展示了从理论建模到仿真实现的完整流程。此外,文档系统梳理了智能优化、机器学习、路径规划、电力系统管理等多个科研方向的技术体系与实际应用场景,强调“借力”工具与创新思维在科研中的重要性。; 适合人群:具备一定Matlab编程基础,从事智能优化、自动化、电力系统、控制工程等相关领域研究的研究生及科研人员,尤其适合正在开展调度优化、路径规划或算法改进类课题的研究者; 使用场景及目标:①学习遗传算法及其他智能优化算法(如粒子群、蜣螂优化、NSGA等)在任务调度中的设计与实现;②掌握Matlab/Simulink在科研仿真中的综合应用;③获取多领域(如微电网、无人机、车间调度)的算法复现与创新思路; 阅读建议:建议按目录顺序系统浏览,重点关注算法原理与代码实现的对应关系,结合提供的网盘资源下载完整代码进行调试与复现,同时注重从已有案例中提炼可迁移的科研方法与创新路径。
【微电网】【创新点】基于非支配排序的蜣螂优化算法NSDBO求解微电网多目标优化调度研究(Matlab代码实现)内容概要:本文提出了一种基于非支配排序的蜣螂优化算法(NSDBO),用于求解微电网多目标优化调度问题。该方法结合非支配排序机制,提升了传统蜣螂优化算法在处理多目标问题时的收敛性和分布性,有效解决了微电网调度中经济成本、碳排放、能源利用率等多个相互冲突目标的优化难题。研究构建了包含风、光、储能等多种分布式能源的微电网模型,并通过Matlab代码实现算法仿真,验证了NSDBO在寻找帕累托最优解集方面的优越性能,相较于其他多目标优化算法表现出更强的搜索能力和稳定性。; 适合人群:具备一定电力系统或优化算法基础,从事新能源、微电网、智能优化等相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于微电网能量管理系统的多目标优化调度设计;②作为新型智能优化算法的研究与改进基础,用于解决复杂的多目标工程优化问题;③帮助理解非支配排序机制在进化算法中的集成方法及其在实际系统中的仿真实现。; 阅读建议:建议读者结合Matlab代码深入理解算法实现细节,重点关注非支配排序、拥挤度计算和蜣螂行为模拟的结合方式,并可通过替换目标函数或系统参数进行扩展实验,以掌握算法的适应性与调参技巧。
UPDATE_GRAPH_PROMPT = """ You are an AI expert specializing in graph memory management and optimization. Your task is to analyze existing graph memories alongside new information, and update the relationships in the memory list to ensure the most accurate, current, and coherent representation of knowledge. Input: 1. Existing Graph Memories: A list of current graph memories, each containing source, target, and relationship information. 2. New Graph Memory: Fresh information to be integrated into the existing graph structure. Guidelines: 1. Identification: Use the source and target as primary identifiers when matching existing memories with new information. 2. Conflict Resolution: - If new information contradicts an existing memory: a) For matching source and target but differing content, update the relationship of the existing memory. b) If the new memory provides more recent or accurate information, update the existing memory accordingly. 3. Comprehensive Review: Thoroughly examine each existing graph memory against the new information, updating relationships as necessary. Multiple updates may be required. 4. Consistency: Maintain a uniform and clear style across all memories. Each entry should be concise yet comprehensive. 5. Semantic Coherence: Ensure that updates maintain or improve the overall semantic structure of the graph. 6. Temporal Awareness: If timestamps are available, consider the recency of information when making updates. 7. Relationship Refinement: Look for opportunities to refine relationship descriptions for greater precision or clarity. 8. Redundancy Elimination: Identify and merge any redundant or highly similar relationships that may result from the update. Memory Format: source -- RELATIONSHIP -- destination Task Details: ======= Existing Graph Memories:======= {existing_memories} ======= New Graph Memory:======= {new_memories} Output: Provide a list of update instructions, each specifying the source, target, and the new relationship to be set. Only include memories that require updates. """ EXTRACT_RELATIONS_PROMPT = """ You are an advanced algorithm designed to extract structured information from text to construct knowledge graphs. Your goal is to capture comprehensive and accurate information. Follow these key principles: 1. Extract only explicitly stated information from the text. 2. Establish relationships among the entities provided. 3. Use "USER_ID" as the source entity for any self-references (e.g., "I," "me," "my," etc.) in user messages. CUSTOM_PROMPT Relationships: - Use consistent, general, and timeless relationship types. - Example: Prefer "professor" over "became_professor." - Relationships should only be established among the entities explicitly mentioned in the user message. Entity Consistency: - Ensure that relationships are coherent and logically align with the context of the message. - Maintain consistent naming for entities across the extracted data. Strive to construct a coherent and easily understandable knowledge graph by eshtablishing all the relationships among the entities and adherence to the user’s context. Adhere strictly to these guidelines to ensure high-quality knowledge graph extraction.""" DELETE_RELATIONS_SYSTEM_PROMPT = """ You are a graph memory manager specializing in identifying, managing, and optimizing relationships within graph-based memories. Your primary task is to analyze a list of existing relationships and determine which ones should be deleted based on the new information provided. Input: 1. Existing Graph Memories: A list of current graph memories, each containing source, relationship, and destination information. 2. New Text: The new information to be integrated into the existing graph structure. 3. Use "USER_ID" as node for any self-references (e.g., "I," "me," "my," etc.) in user messages. Guidelines: 1. Identification: Use the new information to evaluate existing relationships in the memory graph. 2. Deletion Criteria: Delete a relationship only if it meets at least one of these conditions: - Outdated or Inaccurate: The new information is more recent or accurate. - Contradictory: The new information conflicts with or negates the existing information. 3. DO NOT DELETE if their is a possibility of same type of relationship but different destination nodes. 4. Comprehensive Analysis: - Thoroughly examine each existing relationship against the new information and delete as necessary. - Multiple deletions may be required based on the new information. 5. Semantic Integrity: - Ensure that deletions maintain or improve the overall semantic structure of the graph. - Avoid deleting relationships that are NOT contradictory/outdated to the new information. 6. Temporal Awareness: Prioritize recency when timestamps are available. 7. Necessity Principle: Only DELETE relationships that must be deleted and are contradictory/outdated to the new information to maintain an accurate and coherent memory graph. Note: DO NOT DELETE if their is a possibility of same type of relationship but different destination nodes. For example: Existing Memory: alice -- loves_to_eat -- pizza New Information: Alice also loves to eat burger. Do not delete in the above example because there is a possibility that Alice loves to eat both pizza and burger. Memory Format: source -- relationship -- destination Provide a list of deletion instructions, each specifying the relationship to be deleted. """ def get_delete_messages(existing_memories_string, data, user_id): return DELETE_RELATIONS_SYSTEM_PROMPT.replace( "USER_ID", user_id ), f"Here are the existing memories: {existing_memories_string} \n\n New Information: {data}" 帮我按照上述思路分析一下
07-05
### 代码概述 这段代码定义了三个 Prompt 模板(提示词模板):`UPDATE_GRAPH_PROMPT`、`EXTRACT_RELATIONS_PROMPT` 和 `DELETE_RELATIONS_SYSTEM_PROMPT`,以及一个函数 `get_delete_messages`。这些 Prompt 被用于构建图记忆(graph memory)的更新、提取和删除逻辑,通常与知识图谱系统配合使用。 --- ### 代码解析 #### 1. `UPDATE_GRAPH_PROMPT` ```python UPDATE_GRAPH_PROMPT = """ You are an AI expert specializing in graph memory management and optimization... """ ``` - **功能**:该 Prompt 用于指导 AI 模型分析现有图记忆,并根据新的信息对关系进行更新。 - **输入**: - `existing_memories`: 已有的图记忆列表。 - `new_memories`: 新增的记忆内容。 - **输出**:需要执行的更新操作列表,每条包含源节点、目标节点和新关系。 - **核心原则**: - 更新只发生在源节点和目标节点一致的情况下; - 优先使用更准确或更近期的信息; - 提升语义一致性并避免冗余。 #### 2. `EXTRACT_RELATIONS_PROMPT` ```python EXTRACT_RELATIONS_PROMPT = """ You are an advanced algorithm designed to extract structured information from text to construct knowledge graphs... """ ``` - **功能**:用于从用户文本中提取结构化的关系信息,形成图记忆节点和边。 - **输入**:自然语言文本。 - **输出**:提取出的三元组形式(source -- relationship -- destination)的知识图谱数据。 - **关键点**: - 只提取明确提及的信息; - 对“我”、“你”等自指代词使用 `USER_ID` 表示; - 关系描述应简洁通用; - 实体名称需保持一致性。 #### 3. `DELETE_RELATIONS_SYSTEM_PROMPT` ```python DELETE_RELATIONS_SYSTEM_PROMPT = """ You are a graph memory manager specializing in identifying, managing, and optimizing relationships within graph-based memories... """ ``` - **功能**:用于判断是否应该删除图记忆中的某些关系。 - **输入**: - `existing_memories`: 现有图记忆; - `new_text`: 新输入的信息; - **输出**:要删除的关系列表。 - **删除依据**: - 关系过时或不准确; - 与新信息矛盾; - 不删除可能存在多种不同目标节点的相同类型关系。 #### 4. `get_delete_messages` 函数 ```python def get_delete_messages(existing_memories_string, data, user_id): return DELETE_RELATIONS_SYSTEM_PROMPT.replace( "USER_ID", user_id ), f"Here are the existing memories: {existing_memories_string} \n\n New Information: {data}" ``` - **作用**:封装生成删除任务所需的两个输入部分: - 替换 Prompt 中的 `USER_ID` 占位符为实际用户ID; - 构建包含现有记忆和新信息的输入字符串; - **返回值**: - 第一个元素是处理后的 Prompt; - 第二个元素是拼接好的上下文信息,供模型处理使用。 --- ### 知识点 1. **Prompt Engineering(提示工程)** - 使用标准化模板引导大模型完成特定任务,如提取、更新、删除图记忆; - 提高模型理解任务意图的一致性和准确性。 2. **知识图谱关系管理** - 包括新增、更新、删除图谱中的实体间关系; - 强调语义一致性、时间有效性及信息准确性。 3. **文本到图谱映射机制** - 将非结构化文本转化为结构化的三元组形式(源-关系-目标); - 支持基于图数据库的认知推理和记忆增强应用。
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