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Counting sort is a sorting technique based on keys between a specific range. It works by counting the number of objects having distinct key values (kind of hashing). Then do some arithmetic to calculate the position of each object in the output sequence.&nbsp;Counting sort makes assumptions about the data, for example, it assumes that values are going to be in the range of 0 to 10 or 10 – 99 etc, Some other assumptions counting sort makes are input data will be all real numbers. Like other algorithms this sorting algorithm is not a comparison-based algorithm, it hashes the value in a temporary count array and uses them for sorting. It uses a temporary array making it a non In Place algorithm.Counting Sort : <a href="https://www.geeksforgeeks.org/counting-sort/">https://www.geeksforgeeks.org/counting-sort/</a>

Counting Sort

Given an array of distinct integers, the task is to find the surpasser count for each element. A surpasser is an element to the right of the current element that is greater than it. Two approaches are discussed: a naive method using nested loops (O(n^2) time) and an efficient method using the merge step of merge sort (O(n log n) time). The problem involves calculating the surpasser count for each element based on these two methods.&nbsp; Check out the detailed solution for implementation. Read the full article <a href="https://www.geeksforgeeks.org/find-surpasser-count-of-each-element-in-array/">here</a>.

Surpasser Count of Each Element in Array

To make every value in the array unique, the minimum number of increment operations needed can be calculated. By either sorting the array or using a frequency array, we ensure that each element is distinct. The first approach sorts the array and adjusts duplicate elements to create a strictly increasing sequence. The second approach uses a frequency array to track duplicates and increment them as needed. Both methods aim to minimize the number of operations. The time complexity varies depending on the approach used, but the goal remains the same—to make the array unique with the least operations. For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/minimum-increment-operations-to-make-array-unique/">Minimum Increment operations to make Array unique</a>.

Minimum Increment operations to make Array unique

Arrange given non-negative integers to form the largest possible number by concatenating them. Convert numbers to strings and sort them using a custom comparator. Compare concatenated results to determine order. Handle cases where all numbers are zero. Example: Input [3, 30, 34, 5, 9] outputs "9534330". For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/given-an-array-of-numbers-arrange-the-numbers-to-form-the-biggest-number/">Arrange given numbers to form the biggest number</a>.

Arrange given numbers to form the biggest number

Check if a person can attend all meetings given an array of meeting start and end times. Meetings must not overlap, and sorting helps verify this efficiently. Use nested loops for naive checks or sorting for optimized checks. Example: Input [[2, 4], [1, 2], [7, 8], [5, 6], [6, 8]] gives output "false". Sorting ensures a time complexity of O(n*log(n)). For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/meeting-rooms-check-if-a-person-can-attend-all-meetings/">Meeting Rooms - Check if a person can attend all meetings</a>.

Meeting Rooms - Check if a person can attend all meetings

Find the minimum sum of two numbers formed using all digits of an array. Sort the array, build numbers from alternate digits, and add them for the result. The smallest digits take the most significant positions. This ensures an optimal sum. Example: Input [6, 8, 4, 5, 2, 3, 0] gives output "604". For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/minimum-sum-two-numbers-formed-digits-array/">Minimum sum of two numbers formed from digits of an array</a>.

Minimum sum of two numbers formed from digits of an array

To merge two sorted arrays a[] and b[] without extra space, rearrange elements such that the smallest n elements are in a[] and the rest are in b[], keeping both arrays sorted. This can be done using methods like insertion-sort logic, gap method, or swapping and sorting. Each approach ensures elements are sorted within their respective arrays after merging. The goal is efficient merging with minimal space usage. For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/merge-two-sorted-arrays-o1-extra-space/">Merge Two Sorted Arrays Without Extra Space</a>.

Merge Two Sorted Arrays Without Extra Space

Learn to find the minimum number of intervals to remove for making intervals non-overlapping. Explore sorting by starting values and sorting by ending values approaches, both with O(n*log n) time complexity. Understand greedy logic to handle overlaps by prioritizing intervals with smaller end points. Detailed Java implementation examples provided. Optimize interval management efficiently with these techniques. For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/minimum-removals-required-to-make-ranges-non-overlapping/">Non-Overlapping Intervals</a>.

Non-Overlapping Intervals

Learn how to insert and merge intervals in a sorted, non-overlapping array. Explore two approaches: Naive Insertion and Merging (O(n*log n)) and Contiguous Interval Merging (O(n)). Understand overlapping intervals, merging logic, and interval addition with detailed examples and code. Solve interval merging efficiently with optimal solutions. For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/insert-in-sorted-and-non-overlapping-interval-array/">Insert and Merge Interval</a>.

Insert and Merge Interval

Merge overlapping intervals by sorting and merging overlapping ranges. Learn three approaches: naive O(n²) time, optimized O(nlog(n)) space-efficient merging, and in-place O(nlog(n)) method. Each approach demonstrates merging intervals like [[1, 5], [2, 4], [4, 6]] into [[1, 6]]. Examples and Java implementations included for clear understanding. For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/merging-intervals/">Merge Overlapping Intervals</a>.

Merge Overlapping Interval

Learn how to count inversions in an array using two approaches: nested loops for O(n²) complexity and merge sort for O(n*log n) efficiency. Understand inversion counts, examples, and detailed explanations. Includes Java programs for both naive and merge sort methods. Explore optimized solutions for finding inversions. Suitable for understanding array sorting and inversion concepts. For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/inversion-count-in-array-using-merge-sort/">Count Inversions of an Array</a>.

Count Inversions of an Array

H-Index is a metric to measure a researcher's productivity and citation impact, calculated from citation counts. It finds the largest value H, where H papers have at least H citations each. Learn two approaches: Sorting for O(n*logn) time and Counting Sort for O(n) time. Examples and Java programs illustrate the concept. For more details, visit the GeeksforGeeks article: <a href="https://www.geeksforgeeks.org/what-is-h-index/">H-Index</a>.

H-Index

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