计算机算法英语常用术语(实用3篇)

计算机算法英语常用术语 篇一

In the field of computer science, algorithms play a crucial role in solving complex problems and optimizing tasks. Understanding algorithmic concepts and terminologies is essential for computer scientists and programmers. In this article, we will explore some common algorithmic terms in English.

1. Algorithm: An algorithm is a step-by-step procedure or a set of rules for solving a specific problem or performing a specific task.

2. Complexity: Complexity refers to the amount of time and resources required to execute an algorithm. It is often measured in terms of time complexity (how long it takes to run) and space complexity (how much memory it requires).

3. Efficiency: Efficiency measures how well an algorithm utilizes resources. An efficient algorithm completes a task in the most optimal way, minimizing time and resource consumption.

4. Data Structure: A data structure is a way of organizing and storing data in a computer's memory. Examples include arrays, linked lists, stacks, queues, and trees.

5. Sorting: Sorting is the process of arranging a collection of items in a specific order. Common sorting algorithms include bubble sort, insertion sort, selection sort, merge sort, and quicksort.

6. Searching: Searching is the process of finding a specific item or element within a collection of items. Common searching algorithms include linear search, binary search, and hash tables.

7. Recursion: Recursion is a programming technique where a function calls itself to solve a smaller instance of the same problem. It is often used in divide and conquer algorithms.

8. Dynamic Programming: Dynamic programming is a method for solving complex problems by breaking them down into smaller overlapping subproblems. It involves storing the solutions to these subproblems and reusing them when needed.

9. Greedy Algorithm: A greedy algorithm makes locally optimal choices at each step with the hope of finding a global optimum. However, it does not guarantee the best solution in all cases.

10. Complexity Classes: Complexity classes categorize algorithms based on their time and space complexity. Examples include P (polynomial time), NP (nondeterministic polynomial time), and NP-complete.

Understanding these algorithmic terms is essential for designing efficient algorithms, analyzing their performance, and solving real-world problems. By familiarizing ourselves with these concepts, we can better navigate the world of computer science and contribute to technological advancements.

计算机算法英语常用术语 篇二

In the realm of computer science, algorithms are the building blocks of software systems and applications. They define the logic and steps required to solve problems and automate tasks. In this article, we will delve into more algorithmic terms commonly used in English.

1. Heuristic: A heuristic is a rule of thumb or a practical approach used to solve problems that may not have an optimal solution. It is often used when an exact solution is too time-consuming or computationally expensive.

2. Brute Force: Brute force is a straightforward approach that solves a problem by trying all possible solutions. While it guarantees finding the correct solution, it may not be efficient for large problem sizes.

3. Graph: A graph is a data structure that represents a set of objects, called vertices, and the connections between them, called edges. Graph algorithms are used to solve problems involving relationships and networks.

4. Tree: A tree is a hierarchical data structure consisting of nodes connected by edges. It is widely used in algorithms related to hierarchical relationships and sorting.

5. Hashing: Hashing is a technique used to map data to a fixed-size array. It enables quick retrieval of data by using a hash function to calculate the index where the data is stored.

6. Divide and Conquer: Divide and conquer is an algorithmic paradigm that breaks a problem into smaller subproblems, solves them independently, and combines the solutions to obtain the final result. It is commonly used in sorting and searching algorithms.

7. Big O Notation: Big O notation is used to describe the upper bound or worst-case time complexity of an algorithm. It provides a way to compare the efficiency of different algorithms without considering constant factors.

8. Backtracking: Backtracking is a technique used to find all possible solutions to a problem by incrementally building a solution and undoing choices that lead to dead ends. It is commonly used in constraint satisfaction problems.

9. Parallel Algorithms: Parallel algorithms are designed to execute tasks simultaneously on multiple processing units, such as multicore processors or distributed systems. They aim to achieve faster execution times and better resource utilization.

10. Randomized Algorithms: Randomized algorithms use randomization to solve problems more efficiently or to provide approximate solutions. They are often used in optimization and machine learning tasks.

By familiarizing ourselves with these algorithmic terms, we can better understand the principles and techniques behind computer algorithms. This knowledge empowers us to design efficient and scalable software systems, tackle complex computational problems, and contribute to the advancement of technology.

计算机算法英语常用术语 篇三

计算机算法英语常用术语

　　对于时刻需要和国际接轨的码农们,英语的重要性是毋庸置疑的。下面是小编整理的计算机算法常用术语中英对照，希望能帮到大家!

　　计算机算法常用术语(中英)

　　Sorting 排序

　　Searching 查找

　　Median and Selection 中位数

　　Generating Permutations 排列生成

　　Generating Subsets 子集生成

　　Generating Partitions 划分生成

　　Generating Graphs 图的.生成

　　Calendrical Calculations 日期

　　Job Scheduling 工程安排

　　Satisfiability 可满足性

　　Graph Problems -- polynomial 图论-多项式算法

　　Connecte

　　Topological Sorting 拓扑排序

　　Minimum Spanning Tree 最小生成树

　　Shortest Path 最短路径

　　Transitive Closure and Reduction 传递闭包

　　Matching 匹配

　　Triangulation 三角剖分

　　Voronoi Diagrams Voronoi图

　　Nearest Neighbor Search 最近点对查询

　　Range Search 范围查询

　　Point Location 位置查询

　　Intersection Detection 碰撞测试

　　Bin Packing 装箱问题

　　Medial-Axis Transformation 中轴变换

　　Polygon Partitioning 多边形分割

　　Simplifying Polygons 多边形化简

　　Shape Similarity 相似多边形

　　Motion Planning 运动规划

　　Maintaining Line Arrangements 平面分割

　　Minkowski Sum Minkowski和

　　Set and String Problems 集合与串的问题

　　Set Cover 集合覆盖

　　Set Packing 集合配置

　　String Matching 模式匹配

　　Approximate String Matching 模糊匹配

　　Text Compression 压缩

　　Cryptography 密码

　　Finite State Machine Minimization 有穷自动机简化

　　Longest Common Substring 最长公共子串

　　Shortest Common Superstring 最短公共父串

　　DP——Dynamic Programming——动态规划

　　Eulerian Cycle / Chinese Postman Euler回路/中国邮路

　　Edge and Vertex Connectivity 割边/割点

　　Network Flow 网络流

　　Drawing Graphs Nicely 图的描绘

　　Drawing Trees 树的描绘

　　Planarity Detection and Embedding 平面性检测和嵌入

　　Graph Problems -- hard 图论-NP问题

　　Clique 最大团

　　Independent Set 独立集

　　Vertex Cover 点覆盖

　　Traveling Salesman Problem 旅行商问题

　　Hamiltonian Cycle Hamilton回路

　　Graph Partition 图的划分

　　Vertex Coloring 点染色

　　Edge Coloring 边染色

　　Graph Isomorphism 同构

　　Steiner Tree Steiner树

　　Feedback Edge/Vertex Set 最大无环子图

　　Computational Geometry 计算几何

　　Convex Hull 凸包

　　recursion —— 递归

　　Data Structures 基本数据结构

　　Dictionaries 字典

　　Priority Queues 堆

　　Graph Data Structures 图

　　Set Data Structures 集合

　　Kd-Trees 线段树

　　Numerical Problems 数值问题

　　Solving Linear Equations 线性方程组

　　Bandwidth Reduction 带宽压缩

　　Matrix Multiplication 矩阵乘法

　　Determinants and Permanents 行列式

　　Constrained and Unconstrained Optimization 最值问题

　　Linear Programming 线性规划

　　Random Number Generation 随机数生成

　　Factoring and Primality Testing 因子分解/质数判定

　　Arbitrary Precision Arithmetic 高精度计算

　　Knapsack Problem 背包问题

　　Discrete Fourier Transform 离散Fourier变换

　　Combinatorial Problems 组合问题