Programming challenges for implementing sorting algorithms.
As many of you already know, for comments we have exactly 7500 characters available for very long texts, so there's nothing to stop us from writing everything that comes to mind about a particular post. That's why I want to leave you with 10 challenges for implementing sorting algorithms. Feel free to write the necessary lines of code for your comment. 1. **Bubble Sort**: Implement the Bubble Sort algorithm and optimize it for the best performance possible. 2. **Insertion Sort**: Develop the Insertion Sort algorithm and test its efficiency on different datasets. 3. **Selection Sort**: Write code for Selection Sort and compare its speed with other implementations. 4. **Merge Sort**: Implement the Merge Sort algorithm and evaluate its performance in terms of time and space. 5. **Quick Sort**: Develop an efficient version of Quick Sort and verify its ability to handle large volumes of data. .6 **Heap Sort**: Write code for Heap Sort and analyze its time and space complexity. 7.**Radix Sort**: Implement the Radix Sort algorithm and test its efficiency on different input sizes. 8. **Counting Sort**: Develop an efficient implementation of Counting Sort and compare it with other sorting techniques. 9. **Shell Sort**: Write code for Shell Sort and explore how it performs compared to other approaches. 10. **Bucket Sort**: Implement Bucket Sort and evaluate its efficiency compared to more traditional sorting algorithms. These challenges will not only help you better understand sorting algorithms but also improve your programming skills and performance analysis.
[Geeks for geeks explanation of the more common algorithms:](https://www.geeksforgeeks.org/when-to-use-each-sorting-algorithms/) ## bubble is most common. had this one lying around ``` def bubble_sort(array) n = array.length loop do swapped = false (n - 1).times do |i| if array[i] > array[i + 1] array[i], array[i + 1] = array[i + 1], array[i] swapped = true end end break unless swapped end array end arr = [64, 34, 25, 12, 22, 11, 90] sorted_arr = bubble_sort(arr) puts "Bubbly array = #{sorted_arr}" ``` its preforms well hence why it is used alot as a pretty generic sorter along with it being really simple to read what it is doing. ##insertion ;) ``` def insertion_sort(array) (1...array.length).each do |i| key = array[i] j = i - 1 while j >= 0 && array[j] > key array[j + 1] = array[j] j -= 1 end array[j + 1] = key end array end arr = [22, 11, 99, 88, 9, 7, 42] sorted_arr = insertion_sort(arr) puts "Inserted array = #{sorted_arr}" ``` ##counting ``` def counting_sort(array, max_value) num_counts = Array.new(max_value + 1, 0) array.each do |item| num_counts[item] += 1 end sorted_array = [] num_counts.each_with_index do |count, item| count.times { sorted_array << item } end sorted_array end arr = [4, 2, 2, 8, 3, 3, 1] max_value = arr.max sorted_arr = counting_sort(arr, max_value) puts "Counted array = #{sorted_arr}" ``` which one is best determines on how large your data set is. other then that pick which makes the most sense to you.