Top 10 Algorithms and Data Structures that Competitive Programmers Should Ace

Here are the Top 10 Algorithms and Data Structures that Competitive Programmers Should Ace

Competitive programming, is the sport of coding, as we all know, it is all about "developing an optimum and efficient solution for a given problem statement." For this, Algorithms and Data Structures are used. Programming is enjoyable as a mental workout. And competitive programming is a mental sport because once you get into programming, you not only learn how to write code but also the "art of thinking," which involves breaking your code down into manageable pieces and applying logic-based creativity to problems from various angles.

Programming is a difficult job, and once you get into it, you'll face new difficulties and possibly have to handle certain problems that nobody else has ever encountered or for which there is no known answer. You are then required to use your problem-solving and logical skills to come up with a solution in the shortest amount of time possible. You must be good at these if you ever wanted to be proficient in competitive programming i.e., time and space complexity algorithm analysis, and good practice of all Data Structures like Array, List, Stack, Queue, Tree, Graph, Trie, etc.

Let us know, the top 10 Algorithms and Data Structures that Competitive Programmers Should Ace:

ALGORITHMS: 

1. Graph algorithms – This includes Breadth First Search (BFS), Depth First Search (DFS), Shortest Path from source to all vertices and from every vertex to every other vertex, Minimum Spanning tree, Topological Sort, Johnson's algorithm, Articulation Points (Cut Vertices) in Graph, Bridges in a graph, etc.
2. Dynamic programming – This includes Longest Common Subsequence, Ways to Cover a Distance, Minimum Partition, Edit Distance, Longest Increasing Subsequence, Longest Path in Matrix, Assembly Line Scheduling, Assembly Line Scheduling, Subset Sum Problem, etc.
3. Searching and Sorting – The search algorithms may either be applied to linear data structures or graphical data structures. It includes Binary Search, Ternary Search, Merge Sort, Quick Sort, Rabin-Karp Algorithm, KMP Algorithm, Z Algorithm, Counting Sort Etc.
4. Number theory and Other Mathematical – This includes Sieve of Eratosthenes, Segmented Sieve, Prime Factorization using Sieve, Goldbach's Conjecture Algorithm, Optimized Fibonacci Series Solution, Wilson's Theorem, Pollard's rho algorithm, etc.
5. Geometrical and Network Flow Algorithms – Many complex cryptographic algorithms are based on fairly simple modular arithmetic. It includes Convex Hull, Graham Scan, Line Intersection, Interval Tree, Matrix Exponentiation and this, Maxflow Ford Fulkerson Algo and Edmond Karp Implementation, Min cut, etc.

DATA STRUCTURES:

1. Binary Indexed Tree or Fenwick tree – It's displayed as an array. Let's say it's a BITree array []. The sum of a few elements from the input array is stored at each node of the binary-indexed tree. The size of the input array, given as n, is the same as the size of the Binary Indexed Tree.
2. Segment tree – Pre-processing and querying can be done with it quickly. Pre-processing takes O(n) time with a segment tree, while a range minimum query has an O time complexity (Logn). The additional storage needed for the segment tree is O(n).
3. K-Dimensional Tree – A K-D Tree is a binary search tree in which each node's data is a point in space in the K-Dimensional space. In a nutshell, it is a data structure for arranging points in a K-Dimensional space that uses space partitioning (more on that below).
4. Disjoint Set – A data structure known as a disjoint set manages a set of elements that have been divided into several distinct (non-overlapping) subsets.
5. Trie – A particular key from a set can be stored and searched for using this kind of k-ary search tree. Search complexity can be reduced to the ideal level using Trie (key length).

Conclusion: You need solid knowledge of algorithms and data structures to be a skilled programmer in a competitive environment. This is because most issue statements include substantial test cases that must be resolved using an efficient and optimized way in order to avoid a timeout fault and the test case failing.