Data Structure & Algorithms
  • 🖌️Unlocking the Power of Algorithms with C#
  • Data Structure
    • Data Structure
    • Big O
    • Array
    • Linked Lists
    • Stacks
    • Queues
    • Hash Tables
    • Trees
    • Graphs
    • Heap Sort
    • ParkingLot Algorithm
    • LRU cache
    • Priority Queue
  • Algorithms
    • Algorithm
    • Recursion
    • Sorting
    • Searching
    • Dynamic Programming
  • Problems
    • Array
      • Two Sum
      • Two Sum II - Input Array Is Sorted
      • Contains Duplicate
      • Maximum Subarray
      • House Robber
      • Move Zeroes
      • Rotate Array
      • Plus One
      • Find number of subarrays with even length
      • Find number of subarrays with even sum
      • Find Missing Element
      • Reduce Array Size to The Half
      • Remove Duplicates
      • Merge Sorted Arrays
      • Arrays Intersection
      • 3Sum
      • Trapping Rain Water
      • Maximum sum of a subarray
      • Longest Subarray
      • Subarray Sum Equals K
      • Container With Most Water
      • Missing Number
      • Roman to Integer
      • First Missing Positive
      • Unique Integers That Sum Up To 0
      • Integer to Roman
      • Flatten
    • String
      • Check if two strings are permutation of each other
      • String Compression
      • Palindrome Permutation
      • Determine if a string has all Unique Characters
      • One Away
      • Longest Substring Without Repeating Characters
      • Valid Palindrome
      • Valid Palindrome II
      • Backspace String Compare
      • First Unique Character in a String
      • Is Subsequence
      • URLify a given string
      • String has same characters at same position
      • Number of Ways to Split a String
      • Check whether two Strings are anagram of each other
      • Print last `n` lines of a big file or big string.
      • Multiply Strings
    • Matrix
      • Search a 2D Matrix
      • Search a 2D Matrix II
      • Rotate Matrix
      • Spiral Matrix
      • Set Matrix Zeroes
    • Bit Manipulation
      • Sum of Two Integers
      • Reverse Number
      • Reverse Number II
      • Binary Bits Count
      • Binary Bits Count II
    • Stack
      • Valid Parentheses
      • Balance or not expression
      • Decode String
    • Tree
      • Binary Tree Inorder Traversal
      • Binary Tree Preorder Traversal
      • Binary Tree Postorder Traversal
      • Binary Tree Level Order Traversal
      • Binary Tree Return All Root-To-Leaf Paths
      • Binary Tree Height-Balanced
      • Valid Binary Search Tree
      • Binary Tree Sum of all left leaves.
    • Linked List
      • Linked List Delete Middle Node
      • Merge Sorted Linked List
      • Reverse Linked List
      • Remove Duplicates from Sorted List
      • Remove Duplicates from Unsorted List
      • Linked List Cycle
    • Graph
      • The Number Of Islands
      • Number of Closed Islands
      • Max Area of Island
      • Rotting Oranges
      • Number of Provinces
      • Course Schedule
      • Surrounded Regions
      • Snakes and Ladders
      • Widest Path Without Trees
      • Knight Probability in Chessboard
      • Possible moves of knight
      • Check Knight Tour Configuration
      • Steps by Knight
      • Network Delay Time
    • Greedy
      • Best Time to Buy and Sell Stock
      • Best Time to Buy and Sell Stock II
      • Smallest Subset Array
      • Jump Game
    • Backtracking
      • Towers of Hanoi
      • Subsets
      • Combination Sum
      • Sudoku Solver
      • Word Search
    • Heap
      • Kth Largest Element in an Array
      • Top K Frequent Elements
    • Sorting
      • Order Colors String
    • Recursion
      • Number To Text
      • Divide Number
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  1. Problems

Matrix

Here are some common approaches that can be used to solve problems related to 2D matrices in data structures and algorithms:

  1. Iterative Approach: This involves looping over the rows and columns of the matrix to perform some operation on each element. This is a simple and straightforward approach that works well for many problems.

  2. Dynamic Programming: This approach is used for optimization problems, where the solution depends on solutions to smaller subproblems. It involves storing the results of these subproblems to avoid redundant computation. For example, finding the maximum sum submatrix in a 2D matrix can be solved using dynamic programming.

  3. Depth-First Search (DFS) and Breadth-First Search (BFS): These are graph traversal algorithms that can be used to solve problems involving traversing a 2D matrix. For example, finding the number of islands in a binary matrix can be solved using DFS or BFS.

  4. Divide and Conquer: This approach involves dividing the problem into smaller subproblems and solving them independently. For example, Strassen’s algorithm for matrix multiplication uses a divide and conquer approach.

  5. Sliding Window: This approach involves maintaining a ‘window’ of elements as you traverse the matrix. This approach is useful for problems that involve finding a submatrix that meets a certain condition.

  6. Two Pointers: This approach involves maintaining two pointers that traverse the matrix, often at different speeds or from different directions. This approach is useful for problems that involve searching for a pair of elements that meet a certain condition.

Remember, the best approach to use depends on the specific problem you’re trying to solve. It’s always a good idea to understand the problem thoroughly and consider different approaches before deciding on the best one.

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Last updated 1 year ago