Here is a more strictly categorized list of algorithms, with brief explanations for each category:
1. Sorting Algorithms
- Quick Sort: Efficient sorting by partitioning arrays around a pivot.
- Merge Sort: Divide-and-conquer sorting that merges sorted subarrays.
- Heap Sort: Uses a binary heap to repeatedly extract the maximum element.
2. Search Algorithms
- Binary Search: Efficiently finds an item in a sorted list by dividing the search space.
- Depth-First Search (DFS): Explores as far as possible along each branch before backtracking.
- Breadth-First Search (BFS): Explores all neighbors at the present depth before moving on to the next depth level.
3. Dynamic Programming Algorithms
- Knapsack Problem: Optimizes item selection to maximize value without exceeding weight.
- Longest Common Subsequence (LCS): Finds the longest subsequence common to two sequences.
- Fibonacci Sequence: Calculates Fibonacci numbers efficiently using dynamic programming.
4. Graph Algorithms
- Dijkstra’s Algorithm: Finds shortest paths in weighted graphs.
- Bellman-Ford Algorithm: Computes shortest paths accommodating negative weights.
- Kruskal’s Algorithm: Finds a minimum spanning tree by adding the smallest edges.
5. Greedy Algorithms
- Huffman Coding: Compresses data by creating a prefix-free binary tree.
- Prim’s Algorithm: Builds a minimum spanning tree by adding the nearest vertex.
- Activity Selection: Selects the maximum number of non-overlapping activities.
6. Divide and Conquer Algorithms
- Merge Sort: Recursively divides and merges arrays.
- Quick Sort: Recursively partitions arrays around a pivot.
- Strassen’s Algorithm: Efficiently multiplies large matrices.
7. Backtracking Algorithms
- N-Queens Problem: Places N queens on an NxN board without conflicts.
- Sudoku Solver: Fills Sudoku grids by ensuring no conflicts arise.
- Hamiltonian Path: Finds a path visiting each vertex exactly once.
8. Machine Learning Algorithms
- Linear Regression: Models the relationship between variables.
- Decision Trees: Tree-like models for classification and regression.
- K-Means Clustering: Partitions data into k clusters by minimizing variance.
9. String Algorithms
- KMP Algorithm: Efficient string searching by using pattern information.
- Rabin-Karp Algorithm: Searches for patterns using hashing.
- Longest Palindromic Substring: Finds the longest palindromic substring.
10. Computational Geometry Algorithms
- Convex Hull: Finds the smallest convex polygon containing all points.
- Line Intersection: Detects intersections between line segments.
- Voronoi Diagram: Partitions space based on distance to a set of points.
11. Mathematical Algorithms
- Euclidean Algorithm: Finds the greatest common divisor (GCD) of two numbers.
- Sieve of Eratosthenes: Finds all prime numbers up to a specified integer.
- Fast Fourier Transform (FFT): Computes the discrete Fourier transform and its inverse.
12. Cryptographic Algorithms
- RSA Algorithm: Public key encryption and decryption.
- AES Algorithm: Symmetric encryption for secure data transmission.
- SHA-256 Algorithm: Hashing algorithm for data integrity and security.
13. Optimization Algorithms
- Gradient Descent: Optimizes functions by iteratively moving towards the steepest descent.
- Simulated Annealing: Probabilistically optimizes to avoid local minima.
- Genetic Algorithm: Uses natural selection principles to find optimal solutions.
14. Numerical Algorithms
- Newton-Raphson Method: Finds successively better approximations to the roots of a real-valued function.
- Gauss-Seidel Method: Iteratively solves linear systems of equations.
- Runge-Kutta Methods: Solves ordinary differential equations (ODEs).
15. Data Structure Algorithms
- Union-Find: Efficiently manages a partition of a set into disjoint subsets.
- AVL Tree: Self-balancing binary search tree.
- Hashing: Maps data to fixed-size values for efficient lookup.
This structured categorization covers a broad spectrum of algorithms, ensuring familiarity with diverse problem-solving techniques in algorithm engineering.
标签：Sort,Search,Algorithm,工程师,很乱,算法,Algorithms,data,Finds From： https://www.cnblogs.com/augustone/p/18344931