I recently bought a very inexpensive Getorix GK-45X USB keyboard with RGB backlight. After connecting it to a Macbook Pro on an M1 processor, it became clear that the RGB backlight was not working. Even by pressing the magic combination Fn + Scroll Lock, it was not possible to turn on the backlight, only the backlight level of the MacBook screen changed.

There are several solutions to this problem, namely OpenRGB (does not work), HID LED Test (does not work). Only the kvmswitch utility worked:

https://github.com/stoutput/OSX-KVM

You need to download it from the github and allow it to run from the terminal in the Security panel of the System Settings.

As I understood from the description, after launching the utility sends pressing Fn + Scroll Lock, thus turning on/off the backlight on the keyboard.

# Category Archives: Techie

Technical articles

## Tree sort

Tree sort – binary search tree sort. Time complexity – O(n²). In such a tree, each node has numbers less than the node on the left, more than the node on the right, when coming from the root and printing the values from left to right, we get a sorted list of numbers. Surprising huh?

Consider the binary search tree schema:

Derrick Coetzee (public domain)

Try to manually read the numbers starting from the penultimate left node of the lower left corner, for each node on the left – a node – on the right.

It will turn out like this:

- The penultimate node at the bottom left is 3.
- She has a left branch – 1.
- Take this number (1)
- Next, take the vertex 3 (1, 3) itself
- To the right is branch 6, but it contains branches. Therefore, we read it in the same way.
- Left branch of node 6 number 4 (1, 3, 4)
- Node 6 itself (1, 3, 4, 6)
- Right 7 (1, 3, 4, 6, 7)
- Go up to the root node – 8 (1,3, 4 ,6, 7, 8)
- Print everything on the right by analogy
- Get the final list – 1, 3, 4, 6, 7, 8, 10, 13, 14

To implement the algorithm in code, you need two functions:

- Building a binary search tree
- Printing the binary search tree in the correct order

They assemble a binary search tree in the same way as they read it, a number is attached to each node on the left or right, depending on whether it is less or more.

Lua example:

```
Node = {value = nil, lhs = nil, rhs = nil}
function Node:new(value, lhs, rhs)
output = {}
setmetatable(output, self)
self.__index = self
output.value = value
output.lhs = lhs
output.rhs = rhs
output.counter = 1
return output
end
function Node:Increment()
self.counter = self.counter + 1
end
function Node:Insert(value)
if self.lhs ~= nil and self.lhs.value > value then
self.lhs:Insert(value)
return
end
if self.rhs ~= nil and self.rhs.value < value then
self.rhs:Insert(value)
return
end
if self.value == value then
self:Increment()
return
elseif self.value > value then
if self.lhs == nil then
self.lhs = Node:new(value, nil, nil)
else
self.lhs:Insert(value)
end
return
else
if self.rhs == nil then
self.rhs = Node:new(value, nil, nil)
else
self.rhs:Insert(value)
end
return
end
end
function Node:InOrder(output)
if self.lhs ~= nil then
output = self.lhs:InOrder(output)
end
output = self:printSelf(output)
if self.rhs ~= nil then
output = self.rhs:InOrder(output)
end
return output
end
function Node:printSelf(output)
for i=0,self.counter-1 do
output = output .. tostring(self.value) .. " "
end
return output
end
function PrintArray(numbers)
output = ""
for i=0,#numbers do
output = output .. tostring(numbers[i]) .. " "
end
print(output)
end
function Treesort(numbers)
rootNode = Node:new(numbers[0], nil, nil)
for i=1,#numbers do
rootNode:Insert(numbers[i])
end
print(rootNode:InOrder(""))
end
numbersCount = 10
maxNumber = 9
numbers = {}
for i=0,numbersCount-1 do
numbers[i] = math.random(0, maxNumber)
end
PrintArray(numbers)
Treesort(numbers)
```

An important nuance is that for numbers that are equal to the vertex, a lot of interesting mechanisms for hooking to the node have been invented, but I just added a counter to the vertex class, when printing, the numbers are returned by the counter.

### Links

https://gitlab.com/demensdeum /algorithms/-/tree/master/sortAlgorithms/treesort

### References

Convert Sorted Array to Binary Search Tree (LeetCode 108. Algorithm Explained) – YouTube

Sorting algorithms/Tree sort on a linked list – Rosetta Code

How to handle duplicates in Binary Search Tree? – GeeksforGeeks

Tree Sort | GeeksforGeeks – YouTube

## Bucket Sort

Bucket Sort – bucket sorting. The algorithm is similar to sorting by counting, with the difference that the numbers are collected into “buckets”-ranges, then the buckets are sorted using any other, sufficiently productive, sorting algorithm, and the final chord is the expansion of the “buckets” one by one, resulting in a sorted list.

The time complexity of the algorithm is **O(nk)**. The algorithm runs in linear time for data that obeys a uniform distribution. To put it simply, the elements must be in a certain range, without “splashes”, for example, numbers from 0.0 to 1.0. If among such numbers there are 4 or 999, then such a series, according to the laws of the yard, is no longer considered “even”.

Implementation example in Julia:

```
function bucketSort(numbers, bucketsCount)
buckets = Vector{Vector{Int}}()
for i in 0:bucketsCount - 1
bucket = Vector{Int}()
push!(buckets, bucket)
end
maxNumber = maximum(numbers)
for i in 0:length(numbers) - 1
bucketIndex = 1 + Int(floor(bucketsCount * numbers[1 + i] / (maxNumber + 1)))
push!(buckets[bucketIndex], numbers[1 + i])
end
for i in 0:length(buckets) - 1
bucketIndex = 1 + i
buckets[bucketIndex] = sort(buckets[bucketIndex])
end
flat = [(buckets...)...]
print(flat, "\n")
end
numbersCount = 10
maxNumber = 10
numbers = rand(1:maxNumber, numbersCount)
print(numbers,"\n")
bucketsCount = 10
bucketSort(numbers, bucketsCount)
```

The performance of the algorithm is also affected by the number of buckets, for more numbers it is better to take a larger number of buckets (Algorithms in a nutshell by George T. Heineman)

### Links

https://gitlab.com/demensdeum/algorithms/-/tree/master/sortAlgorithms/bucketSort

### References

https://www.youtube.com/watch?v=VuXbEb5ywrU

https://www.youtube.com/watch?v=ELrhrrCjDOA

https://medium.com/karuna-sehgal/an-introduction-to-bucket-sort-62aa5325d124

https://www.geeksforgeeks.org/bucket-sort-2/

https://ru.wikipedia.org/wiki/%D0%91%D0%BB%D0%BE%D1%87%D0%BD%D0%B0%D1%8F_%D1%81%D0%BE%D1%80%D1%82%D0%B8%D1%80%D0%BE%D0%B2%D0%BA%D0%B0

https://www.youtube.com/watch?v=LPrF9yEKTks

https://en.wikipedia.org/wiki/Bucket_sort

https://julialang.org/

https://www.oreilly.com/library/view/algorithms-in-a/9780596516246/ch04s08.html

## Radix Sort

Radix Sort – radix sort. The algorithm is similar to counting sort in that there is no comparison of elements, instead elements are *character-by-character* grouped into *buckets* (buckets), the bucket is selected by the index of the current number-character. Time complexity – O(nd).

Works like this:

- The input will be the numbers 6, 12, 44, 9
- Let’s create 10 buckets of lists (0-9) into which we will add/sort numbers bit by bit.

Further:

- Run a loop with counter i up to the maximum number of characters in the number
- At index i from right to left we get one character for each number, if there is no character, then we consider it to be zero
- The character is converted to a number
- Select a bucket by index – number, put the whole number there
- After finishing iterating over numbers, convert all buckets back to a list of numbers
- Get numbers sorted by digit
- Repeat until all digits run out

Radix Sort example in Scala:

```
import scala.collection.mutable.ListBuffer
import scala.util.Random.nextInt
object RadixSort {
def main(args: Array[String]) = {
var maxNumber = 200
var numbersCount = 30
var maxLength = maxNumber.toString.length() - 1
var referenceNumbers = LazyList.continually(nextInt(maxNumber + 1)).take(numbersCount).toList
var numbers = referenceNumbers
var buckets = List.fill(10)(ListBuffer[Int]())
for( i <- 0 to maxLength) { numbers.foreach( number => {
var numberString = number.toString
if (numberString.length() > i) {
var index = numberString.length() - i - 1
var character = numberString.charAt(index).toString
var characterInteger = character.toInt
buckets.apply(characterInteger) += number
}
else {
buckets.apply(0) += number
}
}
)
numbers = buckets.flatten
buckets.foreach(x => x.clear())
}
println(referenceNumbers)
println(numbers)
println(s"Validation result: ${numbers == referenceNumbers.sorted}")
}
}
```

The algorithm also has a version for parallel execution, for example on the GPU; there is also a variant of bit sort, which is probably** very interesting and truly breathtaking!**

### Links

https://gitlab.com/demensdeum/algorithms/-/blob/master/sortAlgorithms/radixSort/radixSort.scala

### Sources

https://ru.wikipedia.org/wiki/%D0%9F%D0%BE%D1%80%D0%B0%D0%B7%D1%80% D1%8F%D0%B4%D0%BD%D0%B0%D1%8F_%D1%81%D0%BE%D1%80%D1%82%D0%B8%D1%80%D0%BE%D0% B2%D0%BA%D0%B0

https://www.geeksforgeeks.org/radix-sort/

https://www.youtube.com/watch?v=toAlAJKojos

https://github.com/gyatskov/radix-sort

## Heapsort

Heapsort – heap sort. Time complexity – O(n log n), fast eh? I would call this sorting – sorting of falling stones. It seems to me that the easiest way to explain it is visually.

The input is a list of numbers, for example:

5, 0, 7, 2, 3, 9, 4

From left to right, a data structure is made – a binary tree, or as I call it – a pyramid. Pyramid elements can have a maximum of two child elements, with only one top element.

Let’s make a binary tree:

⠀⠀5

⠀0⠀7

2 3 9 4

If you look at the pyramid for a long time, you can see that these are just numbers from the array, going one after another, the number of elements in each floor is multiplied by two.

Then the fun begins, we sort the pyramid from bottom to top, using the falling pebbles method (heapify). Sorting could be started from the last floor (2 3 9 4 ), but it makes no sense because there is no floor below where one could fall.

Therefore, we start dropping elements from the penultimate floor (0 7)

⠀⠀5

⠀0⠀**7**

2 3 9 4

The first element to fall is selected on the right, in our case it is 7, then we look at what is under it, and below it are 9 and 4, nine is more than four, so also nine is more than seven! We drop 7 on 9, and raise 9 to place 7.

⠀⠀5

⠀0⠀9

2 3 **7** 4

Further, we understand that the seven has nowhere to fall below, go to the number 0, which is located on the penultimate floor on the left:

⠀⠀5

⠀**0**⠀9

2 3 7 4

We look at what is under it – 2 and 3, two is less than three, three is greater than zero, so we change zero and three in places:

⠀⠀5

⠀3⠀9

2 **0** 7 4

When you get to the end of the floor, go to the floor above and drop everything there if you can.

The result is a data structure – a heap (heap), namely max heap, because at the top is the largest element:

⠀⠀**9**

⠀3⠀7

2 0 5 4

If you return it to an array representation, you get a list:

[**9**, 3, 7, 2, 0, 5, 4]

From this we can conclude that by swapping the first and last element, we will get the first number in the final sorted position, namely 9 should be at the end of the sorted list, swap:

[4, 3, 7, 2, 0, 5, **9**]

Let’s look at the binary tree:

⠀⠀4

⠀3⠀7

2 0 5 9

The result is a situation in which the lower part of the tree is sorted, you just need to drop 4 to the correct position, repeat the algorithm, but do not take into account the already sorted numbers, namely 9:

⠀⠀**4**

⠀3⠀7

2 0 5 9

⠀⠀7

⠀3⠀**4**

2 0 5 9

⠀⠀7

⠀3⠀5

2 0 **4** 9

It turned out that we, having dropped 4, raised the next largest number after 9 – 7. Swap the last unsorted number (4) and the largest number (7)

⠀⠀4

⠀3⠀5

2 0 7 9

It turned out that now we have two numbers in the correct final position:

4, 3, 5, 2, 0, **7**, **9**

Next, we repeat the sorting algorithm, ignoring those already sorted, as a result we get heap:

⠀⠀0

⠀2⠀3

4 5 7 9

Or as a list:

0, 2, 3, 4, 5, 7, 9

### Implementation

The algorithm is usually divided into three functions:

- Heap creation
- Sifting algorithm (heapify)
- Replacing the last unsorted element and the first one

A heap is created by traversing the penultimate row of the binary tree using the heapify function, from right to left to the end of the array. Then the first replacement of numbers is made in the cycle, after which the first element falls / remains in place, as a result of which the largest element falls into first place, the cycle repeats with a decrease in participants by one, because after each pass, the sorted numbers remain at the end of the list.

Heapsort example in Ruby:

```
DEMO = true
module Colors
BLUE = "\033[94m"
RED = "\033[31m"
STOP = "\033[0m"
end
def heapsort(rawNumbers)
numbers = rawNumbers.dup
def swap(numbers, from, to)
temp = numbers[from]
numbers[from] = numbers[to]
numbers[to] = temp
end
def heapify(numbers)
count = numbers.length()
lastParentNode = (count - 2) / 2
for start in lastParentNode.downto(0)
siftDown(numbers, start, count - 1)
start -= 1
end
if DEMO
puts "--- heapify ends ---"
end
end
def siftDown(numbers, start, rightBound)
cursor = start
printBinaryHeap(numbers, cursor, rightBound)
def calculateLhsChildIndex(cursor)
return cursor * 2 + 1
end
def calculateRhsChildIndex(cursor)
return cursor * 2 + 2
end
while calculateLhsChildIndex(cursor) <= rightBound
lhsChildIndex = calculateLhsChildIndex(cursor)
rhsChildIndex = calculateRhsChildIndex(cursor)
lhsNumber = numbers[lhsChildIndex]
biggerChildIndex = lhsChildIndex
if rhsChildIndex <= rightBound
rhsNumber = numbers[rhsChildIndex]
if lhsNumber < rhsNumber
biggerChildIndex = rhsChildIndex
end
end
if numbers[cursor] < numbers[biggerChildIndex]
swap(numbers, cursor, biggerChildIndex)
cursor = biggerChildIndex
else
break
end
printBinaryHeap(numbers, cursor, rightBound)
end
printBinaryHeap(numbers, cursor, rightBound)
end
def printBinaryHeap(numbers, nodeIndex = -1, rightBound = -1)
if DEMO == false
return
end
perLineWidth = (numbers.length() * 4).to_i
linesCount = Math.log2(numbers.length()).ceil()
xPrinterCount = 1
cursor = 0
spacing = 3
for y in (0..linesCount)
line = perLineWidth.times.map { " " }
spacing = spacing == 3 ? 4 : 3
printIndex = (perLineWidth / 2) - (spacing * xPrinterCount) / 2
for x in (0..xPrinterCount - 1)
if cursor >= numbers.length
break
end
if nodeIndex != -1 && cursor == nodeIndex
line[printIndex] = "%s%s%s" % [Colors::RED, numbers[cursor].to_s, Colors::STOP]
elsif rightBound != -1 && cursor > rightBound
line[printIndex] = "%s%s%s" % [Colors::BLUE, numbers[cursor].to_s, Colors::STOP]
else
line[printIndex] = numbers[cursor].to_s
end
cursor += 1
printIndex += spacing
end
print line.join()
xPrinterCount *= 2
print "\n"
end
end
heapify(numbers)
rightBound = numbers.length() - 1
while rightBound > 0
swap(numbers, 0, rightBound)
rightBound -= 1
siftDown(numbers, 0, rightBound)
end
return numbers
end
numbersCount = 14
maximalNumber = 10
numbers = numbersCount.times.map { Random.rand(maximalNumber) }
print numbers
print "\n---\n"
start = Time.now
sortedNumbers = heapsort(numbers)
finish = Time.now
heapSortTime = start - finish
start = Time.now
referenceSortedNumbers = numbers.sort()
finish = Time.now
referenceSortTime = start - finish
print "Reference sort: "
print referenceSortedNumbers
print "\n"
print "Reference sort time: %f\n" % referenceSortTime
print "Heap sort: "
print sortedNumbers
print "\n"
if DEMO == false
print "Heap sort time: %f\n" % heapSortTime
else
print "Disable DEMO for performance measure\n"
end
if sortedNumbers != referenceSortedNumbers
puts "Validation failed"
exit 1
else
puts "Validation success"
exit 0
end
```

Without visualization, this algorithm is not easy to understand, so the first thing I recommend is to write a function that will print the current view of the binary tree.

### Links

https://gitlab.com/demensdeum/algorithms/-/blob/master/sortAlgorithms/heapsort/heapsort.rb

### References

http://rosettacode.org/wiki/Sorting_algorithms/Heapsort

https://www.youtube.com/watch?v=LbB357_RwlY

https://habr.com/ru/company/otus/blog/460087/

https://ru.wikipedia.org/wiki/Пирамидальная_сортировка

https://neerc.ifmo.ru/wiki/index.php?title=Сортировка_кучей

https://wiki5.ru/wiki/Heapsort

https://wiki.c2.com/?HeapSort

https://ru.wikipedia.org/wiki/Дерево (структура данных)

https://ru.wikipedia.org/wiki/Куча (структура данных)

https://www.youtube.com/watch?v=2DmK_H7IdTo

https://www.youtube.com/watch?v=kU4KBD4NFtw

https://www.youtube.com/watch?v=DU1uG5310x0

https://www.youtube.com/watch?v=BzQGPA_v-vc

https://www.geeksforgeeks.org/array-representation-of-binary-heap/

https://habr.com/ru/post/112222/

https://www.cs.usfca.edu/~galles/visualization/BST.html

https://www.youtube.com/watch?v=EQzqHWtsKq4

https://medium.com/@dimko1/%D0%B0%D0%BB%D0%B3%D0%BE%D1%80%D0%B8%D1%82%D0%BC%D1%8B-%D1%81%D0%BE%D1%80%D1%82%D0%B8%D1%80%D0%BE%D0%B2%D0%BA%D0%B8-heapsort-796ba965018b

https://ru.wikibrief.org/wiki/Heapsort

https://www.youtube.com/watch?v=GUUpmrTnNbw

## Bumblebee All Troubles

Recently, it turned out that users of modern Nvidia GPUs under Arch Linux do not need to use the bumblebee package at all, for example, for me it did not detect an external monitor when connected. I recommend removing the bumblebee package and all related packages, and installing prime using the instructions on the Arch Wiki.

Next, to launch all games on Steam and 3D applications, add prime-run, for Steam this is done like this prime-run %command% in additional launch options.

To check the correctness, you can use glxgears, prime-run glxgears.

https://bbs.archlinux.org/viewtopic.php? pid=2048195#p2048195

## Quicksort

Quicksort is a divide-and-conquer sorting algorithm. Recursively, in parts, parse the array of numbers, setting the numbers in the smaller and larger order from the selected pivot element, insert the pivot element itself into the hot-point between them. After a few recursive iterations, you’ll end up with a sorted list. Time complexity O(n^{2}).

Scheme:

- We start with the fact that we get a list of elements outside, sorting boundaries. In the first step, the sort boundaries will be from start to finish.
- Check that the boundaries of the beginning and end do not intersect, if this happens, then it’s time to finish
- Select some element from the list, call it pivot
- Shift to the right to the end to the last index, so as not to interfere
- Create a counter of *smaller numbers* while equal to zero
- Loop through the list from left to right, up to and including the last index where the anchor element is located
- Each element is compared to the pivot
- If it is less than the reference, then we swap it in places according to the index of the counter of smaller numbers. Increment the counter of smaller numbers.
- When the cycle reaches the anchor element, we stop, swap the anchor element with the element by the counter of smaller numbers.
- We run the algorithm separately for the left smaller part of the list, and separately for the right most part of the list.
- As a result, all recursive iterations will start to stop due to the check in paragraph 2
- Get sorted list

Quicksort was invented by the scientist Charles Antony Richard Hoare at Moscow State University, having learned Russian, he studied computer translation, as well as probability theory at the Kolmogorov school. In 1960, due to a political crisis, he left the Soviet Union.

An example implementation in Rust:

```
extern crate rand;
use rand::Rng;
fn swap(numbers: &mut [i64], from: usize, to: usize) {
let temp = numbers[from];
numbers[from] = numbers[to];
numbers[to] = temp;
}
fn quicksort(numbers: &mut [i64], left: usize, right: usize) {
if left >= right {
return
}
let length = right - left;
if length <= 1 {
return
}
let pivot_index = left + (length / 2);
let pivot = numbers[pivot_index];
let last_index = right - 1;
swap(numbers, pivot_index, last_index);
let mut less_insert_index = left;
for i in left..last_index {
if numbers[i] < pivot {
swap(numbers, i, less_insert_index);
less_insert_index += 1;
}
}
swap(numbers, last_index, less_insert_index);
quicksort(numbers, left, less_insert_index);
quicksort(numbers, less_insert_index + 1, right);
}
fn main() {
let mut numbers = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
let mut reference_numbers = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
let mut rng = rand::thread_rng();
for i in 0..numbers.len() {
numbers[i] = rng.gen_range(-10..10);
reference_numbers[i] = numbers[i];
}
reference_numbers.sort();
println!("Numbers {:?}", numbers);
let length = numbers.len();
quicksort(&mut numbers, 0, length);
println!("Numbers {:?}", numbers);
println!("Reference numbers {:?}", reference_numbers);
if numbers != reference_numbers {
println!("Validation failed");
std::process::exit(1);
}
else {
println!("Validation success!");
std::process::exit(0);
}
}
```

If you don't understand anything, I suggest watching a video by Rob Edwards from the San Diego State University https://www.youtube.com/watch?v=ZHVk2blR45Q it most simply, step by step, shows the essence and implementation of the algorithm.

### Links

https://gitlab.com/demensdeum/algorithms/-/tree/master/sortAlgorithms/quickSort

### References

https://www.youtube.com/watch?v=4s-aG6yGGLU

https://www.youtube.com/watch?v=ywWBy6J5gz8

https://www.youtube.com/watch?v=Hoixgm4-P4M

https://ru.wikipedia.org/wiki/Быстрая_сортировка

https://www.youtube.com/watch?v=Hoixgm4-P4M

https://www.youtube.com/watch?v=XE4VP_8Y0BU

https://www.youtube.com/watch?v=MZaf_9IZCrc

https://www.youtube.com/watch?v=ZHVk2blR45Q

http://rosettacode.org/wiki/Sorting_algorithms/Quicksort

https://www.youtube.com/watch?v=4s-aG6yGGLU

https://www.youtube.com/watch?v=dQw4w9WgXcQ

https://www.youtube.com/watch?v=maibrCbZWKw

https://www.geeksforgeeks.org/quick-sort/

https://www.youtube.com/watch?v=uXBnyYuwPe8

## Binary Insertion Sort

Binary Insertion Sort is a variant of insertion sort in which the insertion position is determined using a binary search. The time complexity of the algorithm is O(n^{2})

The algorithm works like this:

- Loop from zero to end of list
- In the loop, a number is selected for sorting, the number is stored in a separate variable
- Binary search looks for the index to insert this number against the numbers on the left
- After finding the index, the numbers on the left are shifted one position to the right, starting at the insertion index. The process will erase the number to be sorted.
- The previously stored number is inserted at the insertion index
- At the end of the loop, the entire list will be sorted

During a binary search, it is possible that the number will not be found, but the index is not returned. Due to the peculiarities of the binary search, the number closest to the desired one will be found, then to return the index it will be necessary to compare it with the desired one, if the desired one is less, then the desired one should be at the index on the left, and if it is greater or equal, then on the right.

Source code in Go:

```
package main
import (
"fmt"
"math/rand"
"time"
)
const numbersCount = 20
const maximalNumber = 100
func binarySearch(numbers []int, item int, low int, high int) int {
for high > low {
center := (low + high) / 2
if numbers[center] < item { low = center + 1 } else if numbers[center] > item {
high = center - 1
} else {
return center
}
}
if numbers[low] < item {
return low + 1
} else {
return low
}
}
func main() {
rand.Seed(time.Now().Unix())
var numbers [numbersCount]int
for i := 0; i < numbersCount; i++ {
numbers[i] = rand.Intn(maximalNumber)
}
fmt.Println(numbers)
for i := 1; i < len(numbers); i++ { searchAreaLastIndex := i - 1 insertNumber := numbers[i] insertIndex := binarySearch(numbers[:], insertNumber, 0, searchAreaLastIndex) for x := searchAreaLastIndex; x >= insertIndex; x-- {
numbers[x+1] = numbers[x]
}
numbers[insertIndex] = insertNumber
}
fmt.Println(numbers)
}
```

### Links

### References

https://www.geeksforgeeks.org/binary-insertion-sort/

https://www.youtube.com/watch?v=-OVB5pOZJug