👨‍💻
Cracking-Interview
  • Data Structures and Algorithms
  • Arrays
    • 1. Two Sums
    • 2. Rotate Array
    • 3.Remove Duplicates from sorted array
    • 4.Merge Two Sorted Array
    • 5.Majority Element
    • 6.Rotate Image
    • 7.Merge Intervals
    • 8.Increasing Triplet Subsequence
    • 9.Set Matrix Zeroes
    • 10.Product of Array Except Self
    • 11.Container With Most Water
    • 12.Next Permutation
    • 13.Next Greater Element III
    • 14.Largest Number
    • 15.Candy
    • 16.Shortest Unsorted Continuous Subarray
    • 17.Sort Colors(Sort array of 0, 1, 2)
    • 18.Queue Reconstruction by Height
    • 19.Task Scheduler
    • 20.Trapping Rain Water
    • 21.Search a 2D Matrix II
    • 22. Spiral Matrix
    • 23.Median of Two Sorted Arrays
    • 24.Count inversion of an array
    • 25. Reverse Pairs
    • 26. Count Servers that Communicate
    • 27. 3Sum
    • 28. 4Sum
    • 29. Game of Life
    • 30. Sort the Matrix Diagonally
    • 31. Push Dominoes
    • 32. Corporate Flight Bookings
    • 33. Rotating the Box
    • 34. Interval List Intersections
    • 35. Insert Interval
    • 36. Minimum Moves to Equal Array Elements II
    • 37. Pairs of Songs With Total Durations Divisible by 60
    • 38. Remove Covered Intervals
    • Juggling Algorithm
    • Moore’s Voting Algorithm
    • Two Pointer Method
  • Linked List
    • 1.Odd-Even Linked List
    • 2.Add Two Numbers
    • 3. Insert in a Sorted List
    • 4.Rotate List
    • 5.Palindrome Linked List
    • 6.Point of insertion between two Linked List
    • 7.Delete Node in a Linked List
    • 8.Middle of the Linked List
    • 9.Linked List Cycle
    • 10. Swapping Nodes in a Linked List
    • 11.Swap Nodes in Pairs
    • 12.Merge Two Sorted Lists
    • 13.Reverse a Linked List
    • 14. Reverse Linked List II
    • 15.Copy List with Random Pointer
    • 16.Remove Duplicates from Sorted List
    • 17. Merge k Sorted Lists
    • 18. Remove Nth Node From End of List
    • 19. Sort List
    • 20. Reverse Nodes in k-Group
    • 21. Partition List
    • 22. Flattening a Linked List
    • Basic Implementation of Singly Linked List
    • Basic Implementation of Doubly Linked List
  • Strings
    • 1.Reverse Words in a String
    • 2. Is Subsequence
    • 3.Valid Anagram
    • 4.Add Binary
    • 5.Longest Common Prefix
    • 7.Valid Palindrome
    • 8.Implement strStr()
    • 9.String to Integer (atoi)
    • 10.Count and Say
    • 11.Longest Substring Without Repeating Characters
    • 12. Longest Substring with At Most K Distinct Characters
    • 13. Longest Substring with At Least K Repeating Characters
    • 14. Find All Anagrams in a String
    • 15. Permutation in String
    • 16. Maximum Number of Vowels in a Substring of Given Length
    • 17. Partition Labels
    • 18. Minimum Window Substring
    • 19. Compare Version Numbers
    • 20. Shortest Distance to a Character
    • 21. Count Number of Homogenous Substrings
    • 22. Remove Duplicate Letters
    • 23. Count Sorted Vowel Strings
    • 24. Get Equal Substrings Within Budget
    • 25. Sentence Similarity III
    • 26. Shifting Letters
    • 27. Longest Happy Prefix (imp)
    • 28. Sort Characters By Frequency
    • 29. String Compression
    • 30. Palindrome Pairs
    • 31. Shortest Palindrome(Imp)
    • 32. One Edit Distance
    • 33. Can Make Palindrome from Substring
    • 34. Greatest Common Divisor of Strings
    • KMP Algorithm for Pattern Searching
    • Rabin-Karp Algorithm for Pattern Searching
  • Binary Tree
    • Terminology and Formula
    • N-ary Tree
      • 1. N-ary Tree Preorder Traversal
      • 2. N-ary Tree Postorder Traversal
      • 3. N-ary Tree Level Order Traversal
    • Tree Traversals
      • 1.In-order Traversal
      • 2.Pre-order Traversal
      • 3.Post-order Traversal
      • 4.Level-order Traversal
      • 5.Vertical Order Traversal
      • 6. Binary Tree Level Order Traversal II
    • Problems Related to Binary Trees
      • 1.Maximum/Minimum Depth of Binary Tree
      • 2.Lowest Common Ancestor of a Binary Tree
      • 3.Symmetric Tree
      • 4.Path Sum
      • 5.Tree : Top View
      • 6. Diameter of Binary Tree
      • 7.Populating Next Right Pointers in Each Node
      • 8.Balanced Binary Tree
      • 9.Binary Tree Zigzag Level Order Traversal
      • 10.Invert Binary Tree
      • 11.Binary Tree Right Side View
      • 12.Validate Binary Tree Nodes
      • 13.Merge Two Binary Trees
      • 14.Flatten Binary Tree to Linked List
      • 15. Construct Binary Tree from Preorder and Inorder Traversal
      • 16. Construct Binary Tree from Inorder and Postorder Traversal
      • 17. Binary Tree Maximum Path Sum
      • 18. Sum Root to Leaf Numbers
      • 19. Subtree of Another Tree
      • 20. Even Odd Tree
      • 21. Delete Nodes And Return Forest
      • 22. Maximum Binary Tree
      • 23. Linked List in Binary Tree
      • 24. Longest Univalue Path
      • 25. Sum of Distances in Tree
      • 26. Distribute Coins in Binary Tree
      • 27. Find Duplicate Subtrees
      • 28. Serialize and Deserialize BST
      • 29. Serialize and Deserialize Binary Tree
      • 30. Path Sum III
      • 31. Delete Leaves With a Given Value
      • 32. All Possible Full Binary Trees
      • 33. All Nodes Distance K in Binary Tree
      • 34. Maximum Width of Binary Tree
  • Stack and Queue
    • Problems
      • 1.Min Stack
      • 2. Max Stack
      • 3.Valid Parentheses
      • 4.Evaluate Reverse Polish Notation
      • 5.Infix And Prefix and Postfix Conversions
      • 6.Next Greater Element I
      • 7.Next Greater Element II
      • 8.Sliding Window Maximum
      • 9.Gas Station (Circular Tour Problem)
      • 10.Daily Temperatures
      • 11.Decode String
      • 12. Largest Rectangle in Histogram
      • 13. Maximal Rectangle
      • 14. Minimum Remove to Make Valid Parentheses
      • 15. Map of Highest Peak
      • 16. Longest Valid Parentheses
      • 17. Online Stock Span
      • 18. Score of Parentheses
      • 19. Remove K Digits(imp)
      • 20. Design a Stack With Increment Operation
      • 21. Final Prices With a Special Discount in a Shop
      • 22. Valid Parenthesis String
      • 23. Reveal Cards In Increasing Order
      • 24. Remove All Adjacent Duplicates In String
      • 25. Number of Visible People in a Queue
      • 26.Number following a pattern
    • Implementation
      • Implementation of Queue
      • Implementation of Stack
      • Implementation of Circular Queue
      • Implement Queue using Stacks
      • Implement Stack using Queues
  • Hash Table
    • Designing and Definitions
      • Techniques of hashing and collision resolution
      • Design HashSet
      • Design HashMap
    • Problems
      • 1.Intersection of Two Arrays
      • 2.Happy Number
      • 3.Contains Duplicate
      • 4.Find the Duplicate Number
      • 5. Find All Duplicates in an Array
      • 6. Isomorphic Strings
      • 7. Group Anagrams
      • 8. Single Number
      • 9.Valid Sudoku
      • 10.Jewels and Stones
      • 11.First Missing Positive
      • 12. LRU Cache (V.IMP)
      • 13. Encode and Decode TinyURL
      • 14. Alphabet Board Path
  • Binary Search
    • Template I
      • Basic Binary Search
      • 1.Search in a Sorted Rotated Array
      • 5.Sqrt(x)
    • Template II
      • 2.Find Minimum in Rotated Sorted Array
    • Template III
      • 3.Find Peak Element
      • 4. Find First and Last Position of Element in Sorted Array
  • Binary Search Tree
    • 1.Is This a Binary Search Tree
    • 2.Search in a Binary Search Tree
    • 3.Insert into a Binary Search Tree
    • 4.Delete Node in a BST
    • 5.Lowest Common Ancestor of a Binary Search Tree
    • 6.Construct Binary Search Tree from Preorder Traversal
    • 7.Convert Sorted Array to Binary Search Tree
    • 8. Convert Sorted List to Binary Search Tree
    • 9.Kth Smallest Element in a BST
    • 10.Trim a Binary Search Tree
    • 11.Two Sum IV - Input is a BST
    • 12. Binary Search Tree to Greater Sum Tree / Greater Tree
    • 13. Balance a Binary Search Tree
    • 14. Binary Search Tree Iterator
    • 15. Unique Binary Search Trees
    • 16. Unique Binary Search Trees II
    • 17. Inorder Successor in BST
  • Bit manipulation
    • Bitwise Operations
    • 1.Reverse Bits
    • 2.Counting Bits
    • 3.Number of 1 Bits
    • 4.Hamming Distance
    • 5. Power of Two
    • 6. Complement of Base 10 Integer
  • Graph
    • 1.Maximum number of edges to be removed to contain exactly K connected components in the Graph
    • 2. Social Networking Graph
    • 3.The Flight Plan
    • 4.Is it a tree?
    • 5.Possible Bipartition
    • 6.Longest path in an undirected tree
    • 7.Keys and Rooms
    • 8.Is Graph Bipartite?
    • 9.Number of Islands
    • 10. Number of Provinces
    • 11.Surrounded Regions
    • 12. All Paths From Source to Target
    • 13.Word Ladder
    • 14.Rotting Oranges
    • 15.Course Schedule
    • 16.Course Schedule II
    • 17. Minimum Number of Vertices to Reach All Nodes
    • 18.Network Delay Time
    • 19. 01 Matrix
    • 20. Cheapest Flights Within K Stops
    • 21. Critical Connections in a Network
    • 22.Word Search
    • 23. Minimum Time to Collect All Apples in a Tree
    • 24. Time Needed to Inform All Employees
    • 25. As Far from Land as Possible
    • 26. Clone Graph
    • 27. Min Cost to Connect All Points
    • 28. Find the City With the Smallest Number of Neighbors at a Threshold Distance
    • 29. Number of Operations to Make Network Connected
    • 30. Open the Lock
    • 31. Word Search II
    • 32. Number of Ways to Arrive at Destination
    • 33. Knight On Chess Board
    • 34. Shortest Bridge
    • 35. Pacific Atlantic Water Flow
    • 36. Making A Large Island
    • 37. Path with Maximum Probability
    • Graph Algorithms
      • 1.DFS
      • 2.BFS
      • 3.Prim's MST
      • 4.Kruskal (MST)
      • 5.Dijkstra’s Algorithm(Single Source Shortest Path)
      • 6. Floyd Warshall's Algorithm(All Pair Sortest Path)
      • 7. Topological Sort
      • 8. Find if the given edge is a bridge in graph.
  • Disjoint Sets(Union-Find)
    • 1.Implementation of union find
    • 2.Social Network Community
    • 3. Graph Connectivity With Threshold
    • 4. Redundant Connection
    • 5. Smallest String With Swaps
    • 6. Accounts Merge
  • Heap and Priority Queue
    • Concepts of Heap
      • Building Max Heap
    • 1.Kth Largest Element in an Array
    • 2.Kth Largest Element in a Stream
    • 3.Top K Frequent Elements
    • 4.Kth Smallest Element in a Sorted Matrix
    • 5. Find Kth Largest XOR Coordinate Value
    • 6. Top K Frequent Words
    • 7. Find Median from Data Stream
    • 8. Maximum Average Pass Ratio
    • 9. Longest Happy String
    • 10. K Closest Points to Origin
    • 11. Reorganize String
    • 12. Find the Kth Largest Integer in the Array
    • 13. Smallest range in K lists
  • Trie
    • 1. Implement Trie (Prefix Tree)
    • 2. Replace Words
    • 3.Design Add and Search Words Data Structure
    • 4. Search Suggestions System
  • Dynamic Programming
    • 1.House Robber
    • 2.Climbing Stairs
    • 3.Longest Palindromic Substring
    • 4.Longest Palindromic Subsequence
    • 5.Best Time to Buy and Sell Stock
    • 6.Best Time to Buy and Sell Stock with Cooldown
    • 7.Unique Paths
    • 8.Minimum Path Sum
    • 9.Longest Increasing Subsequence
    • 10.Longest Common Subsequence
    • 11.Longest Consecutive Sequence
    • 12.Coin Change
    • 13.Coin Change 2
    • 14.Target Sum
    • 15.Partition Equal Subset Sum
    • 16. Last Stone Weight II
    • 17.Word Break
    • 18. Word Break II
    • 19.Burst Balloons
    • 20. Uncrossed Lines
    • 21. Wildcard Matching
    • 22. Maximal Square
    • 23. Perfect Squares
    • 24. Decode Ways
    • 25. Minimum Cost For Tickets
    • 26. Number of Dice Rolls With Target Sum
    • 27. Delete Operation for Two Strings
    • 28. Maximum Length of Repeated Subarray
    • 29. Longest String Chain
    • 30. Maximum Length of Pair Chain
    • 31. Minimum Falling Path Sum
    • 32. Predict the Winner
    • 33. Delete and Earn
    • 34. House Robber III
    • 35. Partition to K Equal Sum Subsets
    • 36. Different Ways to Add Parentheses
    • 37. Count Square Submatrices with All Ones
    • 38. 2 Keys Keyboard
    • 39. Palindrome Partitioning II
    • 40. Integer Break
    • 41.Cutting a Rod
    • 42. Number of Longest Increasing Subsequence
    • 43. Russian Doll Envelopes
    • 44. 0-1 Knapsack
    • 45. Interleaving String
    • 46. Edit Distance
    • 47. Minimum Insertion Steps to Make a String Palindrome
    • 48.Jump Game
    • 49. Shortest Common Supersequence
    • 50. Length of Longest Fibonacci Subsequence
    • 51. Maximum Alternating Subsequence Sum
    • 52. Palindromic Substrings
    • 53. Distinct Subsequences
    • 54. Best Team With No Conflicts
    • 55. Partition Array for Maximum Sum
    • 56. Ones and Zeroes
  • Greedy Algorithms
    • 1.Activity Selection
    • 2. N meetings in one room
    • 3.Minimum Platforms
    • 4. Car Pooling
    • 5. Maximum Population Year
    • 6.Meeting Rooms
    • 7. Equal Sum Arrays With Minimum Number of Operations
    • 8. Two City Scheduling
    • 9. Non-overlapping Intervals
    • 10. Minimum Number of Arrows to Burst Balloons
    • 11. Maximum Profit in Job Scheduling{IMP}
    • 12.Huffman Coding
    • 13. Minimum Cost Tree From Leaf Values
    • 14.Maximum Number of Events That Can Be Attended
    • 15. Video Stitching
    • 16. Minimum Number of Taps to Open to Water a Garden
  • Backtracking
    • 1.Subsets and Subset II
    • 2.Combinations
    • 3.Generate Parentheses
    • 4.Letter Combinations of a Phone Number
    • 5.Palindrome Partitioning
    • 6.Combination Sum and Combination Sum II
    • 7.Permutations
    • 8. Permutations II
    • 9. Letter Case Permutation
    • 10. Split a String Into the Max Number of Unique Substrings
    • 11. Path with Maximum Gold
    • 12. N-Queens
    • 13. N-Queens II
    • 14. Sudoku Solver
    • 15. Permutation Sequence
    • 16. Beautiful Arrangement
  • Sub Array and Sliding Window Problems
    • 1.Maximum Subarray Sum
    • 2.Maximum Product Subarray
    • 3.Maximum Sum Circular Subarray
    • 4. Maximum Absolute Sum of Any Subarray
    • 5. K-Concatenation Maximum Sum
    • 6.Subarray Sum Equals K
    • 7. Continuous Subarray Sum
    • 8. Contiguous Array
    • 9.Minimum Size Subarray Sum >=k (positive no)
    • 10. Max Subarray sum of positive no <= k
    • 11. Maximum Number of Non-Overlapping Subarrays With Sum Equals Target
    • 12. Maximum Subarray Sum with One Deletion
    • 13. Shortest Subarray with Sum at Least K (Negative Numbers)
    • 14. Frequency of the Most Frequent Element
    • 15. Count Number of Nice Subarrays
    • 16. Binary Subarrays With Sum
    • 17.Number of Substrings Containing All Three Characters
    • 18. Find Two Non-overlapping Sub-arrays Each With Target Sum
    • 19. Maximum Points You Can Obtain from Cards
    • 20. Longest Repeating Character Replacement
    • 21. Subarrays with K Different Integers
    • 22. Replace the Substring for Balanced String
    • 23. Maximize the Confusion of an Exam / Max Consecutive Ones III
    • 24. Subarray Sums Divisible by K
  • Design
    • 1. Design Twitter
    • 2. Design Browser History
    • 3. Snapshot Array
  • Maths
    • Formula
    • 1.Count Prime
    • 2.Pascal's Triangle
    • 3.Extra Long Factorials
    • 4.Factorial Trailing Zeroes
    • 5.Excel Sheet Column Number
    • 6.HCF and LCM
    • 7. Fizz Buzz
    • 8. Rearrange an array so that arr[i] becomes arr[arr[i]]
    • 9. XOR Queries of a Subarray
    • 10. Find second largest element
  • Sorting-Algorithms
    • Merge Sort
    • Quick Sort
    • Heap Sort
    • Selection Sort
    • Bubble Sort
    • Insertion Sort
  • Expected Interview MCQ and Questions c++
  • Concepts in C++
  • System Design Questions
  • 💾DBMS
    • Introduction
    • RBMS
    • JOINS
    • SQL
  • 🖥️OS
    • OS and Its Types
    • Process Concept and Threads
    • Process Synchronization
    • Deadlock in Operating System
    • Memory Management
    • Imp Questions of OS for Interviews
  • 🚙OOPS
    • Introduction and Definitions
  • 📨COMPUTER-NETWORKS
    • OSI | TCP IP Model
    • Topology
    • Network Devices
    • IP Address
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  • Solution:(DP )
  • S1
  • S2 (Optimized space)

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  1. Dynamic Programming

33. Delete and Earn

Given an array nums of integers, you can perform operations on the array.

In each operation, you pick any nums[i] and delete it to earn nums[i] points. After, you must delete every element equal to nums[i] - 1 or nums[i] + 1.

You start with 0 points. Return the maximum number of points you can earn by applying such operations.

Example 1:

Input: nums = [3,4,2]
Output: 6
Explanation: Delete 4 to earn 4 points, consequently 3 is also deleted.
Then, delete 2 to earn 2 points.
6 total points are earned.

Example 2:

Input: nums = [2,2,3,3,3,4]
Output: 9
Explanation: Delete 3 to earn 3 points, deleting both 2's and the 4.
Then, delete 3 again to earn 3 points, and 3 again to earn 3 points.
9 total points are earned.

Solution:(DP )

Similar to house robber with little variations We calc the max points by including and excluding a point

S1

class Solution
{
public:
    int deleteAndEarn(vector<int> &nums)
    {

        map<int, int> m;
        for (int i = 0; i < nums.size(); i++)
        {
            m[nums[i]]++;
        }

        vector<int> v;
        for (auto itr = m.begin(); itr != m.end(); itr++)
        {
            v.push_back(itr->first);
        }

        int prev = -1;
        int use = 0;
        int avoid = 0;

        for (int i = 0; i < v.size(); i++)
        {
            int score = 0;
            if (v[i]-1 == prev)
            {
                score = v[i] * m[v[i]] + avoid;
            }
            else
            {
                score = v[i] * m[v[i]] + use;
            }
            avoid = use;
            use = max(use, score);
            prev = v[i];
        }

        return use;
    }
};

S2 (Optimized space)

class Solution
{
public:
    int deleteAndEarn(vector<int> &nums)
    {

        map<int, int> m;
        for (int i = 0; i < nums.size(); i++)
        {
            m[nums[i]]++;
        }

        int prev = -1;
        int use = 0;
        int avoid = 0;

        for (auto itr = m.begin(); itr != m.end(); itr++)
        {
            int score = 0;
            if (itr->first - 1 == prev)
            {
                score = itr->first * itr->second + avoid;
            }
            else
            {
                score = itr->first * itr->second + use;
            }
            avoid = use;
            use = max(use, score);
            prev = itr->first;
        }

        return use;
    }
};
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