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Update Insertion Sort.md
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@ -29,3 +29,67 @@ for x = 1 : n
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y--
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y--
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list[y+1] = key
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list[y+1] = key
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```
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```
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## Code
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```java
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for(int i=1; i<numbers.length; i++) {
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key = numbers[i];
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j = i-1;
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while(j >= 0 && numbers[j] > key) {
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numbers[j + 1] = numbers[j];
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j--;
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}
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numbers[j + 1] = key;
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}
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## Attributes
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- Stable
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- Instead of swapping elements, all elements are shifted one position ahead
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- Adaptable
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- if input is already sorted then time complexity will be $`O(n)`$
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- Space complexity
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- In-place sorting, original array is updated rather than creating a new one, space complexity $`O(1)`$
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- Very low overhead
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## Time Complexity
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- Best Case
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- $`O(N)`$
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- Worst Case
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- $`O(N^2)`$
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- Average Case
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- $`O(N^2)`$
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Number of passes and comparisons
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- Number of passes
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- Insetion sort always require n-1 passes
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- Mininimum number of comparisons = $`n-1`$
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- maximum number of comparisons = $`\dfrac{n^2-n}{2}`$ or $`n(n-1)/2`$
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- Average number of comparisons = $`\dfrac{n^2-n}{4}`$ or $n(n-1)/4`$
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## When To Use
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- Good to use when the array is `nearly sorted`, and only a few elements are misplaced
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- Method is efficient - reduces the number of comparisons if in a pratially sorted array
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- Method will simply insert the unsorted elements into its correct place.
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## When not to use
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- In efficient for `inverse sorting`
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- Descending order is considered the worst unsorted case
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- Not as efficient if list is completely unsorted
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## Relation to Selection Sort
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- Outer loop over every index
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- Inner loop
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- Each pass (within the inner loop) increases the number of sorted items by one until there are no more unsorted items
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## Differences To Selection Sort
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Selection Sort
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- Taking the current item and swapping it with the smallest item on the right side of the list
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- More simple
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- One possible case $`O(n^2)`$
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Insertion Sort
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- Taking the current item and inserting into the right position by adjusting the list
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- more efficient (best case $`O(n)`$)
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