Data Structures & AlgorithmsSorting Algorithms

Insertion Sort — Intuition & Analysis

LevelBeginner

Duration75 mins

TopicSorting Algorithms

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The Insertion Sort Algorithm

Sorting Like a Card Player

Have you ever picked up a hand of playing cards and organized them by suit and rank? If so, you've already performed Insertion Sort countless times without knowing it had a formal name in computer science.

When you pick up cards one at a time and slide each into its correct position among the cards you're already holding, you're executing the Insertion Sort algorithm. This natural, intuitive approach to ordering items is one of the oldest and most elegant sorting techniques known to computer science—and it harbors surprising efficiency in specific scenarios that make it invaluable in practice.

What You Will Learn

By the end of this page, you will understand Insertion Sort's fundamental mechanism, why humans naturally gravitate toward this approach, how it differs philosophically from Bubble Sort and Selection Sort, and the exact algorithmic steps that transform an unsorted array into a sorted one.

The Card-Sorting Analogy: Understanding Insertion Sort Intuitively

Before examining code or formal algorithms, let's deeply explore the analogy that gives Insertion Sort its power and intuition: sorting a hand of playing cards.

The scenario:

Imagine you're dealt a hand of cards face-down. You pick them up one at a time:

Pick up the first card — It's a 7. With only one card, your hand is trivially sorted.
Pick up the second card — It's a 3. You compare it with 7. Since 3 < 7, you slide the 3 to the left of 7. Your hand: [3, 7].
Pick up the third card — It's a 9. You compare it with 7. Since 9 > 7, it stays where it is (at the end). Your hand: [3, 7, 9].
Pick up the fourth card — It's a 5. You compare it with 9 (too big), then 7 (too big), then 3 (smaller!). You insert 5 between 3 and 7. Your hand: [3, 5, 7, 9].
Continue until all cards are picked up.

This process—picking up an element, finding its correct position among already-sorted elements, and inserting it there—is the essence of Insertion Sort.

The Key Insight

At any point during Insertion Sort, the portion of the array to the left of the current element is always sorted. We take the next unsorted element and 'insert' it into its correct position within this sorted portion. The sorted region grows by one element with each iteration.

Why this analogy matters:

The card-sorting analogy isn't just pedagogical—it reveals why Insertion Sort is:

Natural: Humans instinctively use this method because it's cognitively simple. We maintain a mental model of the sorted portion and only need to focus on placing one new item at a time.
Incremental: Unlike algorithms that process the entire array globally (like Merge Sort's divide-and-conquer), Insertion Sort builds the solution incrementally, one element at a time.
Online: You can start sorting before you have all the data. As new cards (elements) arrive, you simply insert them into the correct position. This property is called being an online algorithm.
Stable: When you insert a card equal to one already in your hand, you naturally place it after the existing one (or before—consistently). This preserves relative order of equal elements, making Insertion Sort stable.

Insertion Sort from First Principles

Let's formalize the card-sorting intuition into a precise algorithmic definition.

The Problem:

Given an array A of n elements, rearrange them such that A[0] ≤ A[1] ≤ A[2] ≤ ... ≤ A[n-1] (for ascending order).

Insertion Sort's Strategy:

Partition the array conceptually into two regions:

Sorted region: Initially contains just A[0]
Unsorted region: Contains A[1] through A[n-1]

Repeatedly:

Take the first element from the unsorted region
Insert it into its correct position within the sorted region
Expand the sorted region by one

Continue until the unsorted region is empty.

The Invariant

The fundamental invariant of Insertion Sort is: At the start of iteration i, elements A[0..i-1] are sorted and consist of the elements originally in A[0..i-1], but in sorted order. This invariant holds before the first iteration (the single-element array A[0..0] is trivially sorted) and is maintained by each insertion operation.

Why call it 'Insertion' Sort?

The name comes from the core operation: we insert each element into its proper place. Unlike Bubble Sort (which swaps adjacent elements to 'bubble' large elements upward) or Selection Sort (which 'selects' the minimum and places it at the front), Insertion Sort focuses on finding the right slot and inserting the element there.

The insertion operation in detail:

When inserting element A[i] into the sorted subarray A[0..i-1]:

Store A[i] in a temporary variable key
Compare key with elements from right to left in the sorted region
Shift elements larger than key one position to the right
When you find an element ≤ key (or reach the beginning), insert key into the vacated position

This shifting-and-inserting is what distinguishes Insertion Sort from algorithms that swap elements in place.

Step-by-Step Walkthrough: Tracing Insertion Sort

Let's trace through Insertion Sort on the array [5, 2, 8, 1, 9, 3] to see exactly how it works.

Initial state: [5, 2, 8, 1, 9, 3]

The sorted region is [5], unsorted region is [2, 8, 1, 9, 3].

Insertion Sort Execution Trace
Iteration	Key	Action	Resulting Array
i=1	key=2	Compare with 5. 5>2, shift 5 right. Insert 2 at position 0.	[2, 5, 8, 1, 9, 3]
i=2	key=8	Compare with 5. 5<8, stop. Insert 8 at position 2 (no shift needed).	[2, 5, 8, 1, 9, 3]
i=3	key=1	Compare with 8 (shift), 5 (shift), 2 (shift). Insert 1 at position 0.	[1, 2, 5, 8, 9, 3]
i=4	key=9	Compare with 8. 8<9, stop. Insert 9 at position 4 (no shift).	[1, 2, 5, 8, 9, 3]
i=5	key=3	Compare with 9 (shift), 8 (shift), 5 (shift). 2<3, stop. Insert 3 at position 2.	[1, 2, 3, 5, 8, 9]

Observations from the trace:

Element 8 required no shifts — When an element is already larger than all sorted elements, it's already in the correct position. This is why Insertion Sort is efficient on nearly-sorted data.
Element 1 required the most work — A very small element at the end of the array needs to shift past all previously sorted elements. This is the worst case for a single insertion.
The sorted region grows left-to-right — After iteration i, elements A[0..i] are sorted.
Shifts, not swaps — We don't swap pairs of elements. We shift a contiguous block right and drop the key into the gap. This is more efficient than the multiple swaps of Bubble Sort.

Visualizing Shifts

Think of shifting like making room in a bookshelf. Instead of repeatedly swapping books, you slide a whole section of books to the right, then place the new book in the opened slot. This single 'slide' operation replaces multiple swap operations.

The Algorithm in Pseudocode

Now that we understand the intuition and have traced an example, let's formalize Insertion Sort in pseudocode. This representation is language-agnostic and captures the algorithm's essence.

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INSERTION-SORT(A)
    n = length(A)
    
    for i = 1 to n - 1            // Start from second element
        key = A[i]                 // Element to be inserted
        j = i - 1                  // Last index of sorted region
        
        // Shift elements greater than key to the right
        while j >= 0 AND A[j] > key
            A[j + 1] = A[j]        // Shift element right
            j = j - 1              // Move to previous element
        
        A[j + 1] = key             // Insert key in correct position
    
    return A

Line-by-line explanation:

Line 4: We iterate from index 1 to n-1. Index 0 is already 'sorted' (single element).
Line 5: key holds the element we're currently inserting. We store it separately because the shifting will overwrite A[i].
Line 6: j starts at i-1, the last index of the sorted region.
Lines 9-11: The inner while loop performs the shifting. As long as we haven't reached the array's start AND the current element is larger than key, we shift that element right and move j left.
Line 13: Once the loop exits (either j < 0 or A[j] ≤ key), we insert key at position j + 1. This is the first position where the element to the left is ≤ key (or there's no element to the left).

Why j + 1, Not j?

The loop exits when A[j] ≤ key or j < 0. In either case, j points to the position before where key belongs. So we insert at j + 1. This is a common off-by-one source of bugs in Insertion Sort implementations.

Implementation in Multiple Languages

Let's translate the pseudocode into actual programming languages. Understanding implementations across languages helps cement the algorithm's structure.

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def insertion_sort(arr):
    """
    Sort array in-place using Insertion Sort.
    
    Args:
        arr: List of comparable elements
        
    Returns:
        The sorted list (same reference, modified in-place)
    """
    n = len(arr)
    
    for i in range(1, n):
        key = arr[i]
        j = i - 1
        
        # Shift elements greater than key to the right
        while j >= 0 and arr[j] > key:
            arr[j + 1] = arr[j]
            j -= 1
        
        # Insert key at its correct position
        arr[j + 1] = key
    
    return arr
 
 
# Example usage
data = [5, 2, 8, 1, 9, 3]
sorted_data = insertion_sort(data)
print(sorted_data)  # Output: [1, 2, 3, 5, 8, 9]

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function insertionSort<T>(arr: T[]): T[] {
    const n = arr.length;
    
    for (let i = 1; i < n; i++) {
        const key = arr[i];
        let j = i - 1;
        
        // Shift elements greater than key to the right
        while (j >= 0 && arr[j] > key) {
            arr[j + 1] = arr[j];
            j--;
        }
        
        // Insert key at its correct position
        arr[j + 1] = key;
    }
    
    return arr;
}
 
// Example usage
const data = [5, 2, 8, 1, 9, 3];
const sortedData = insertionSort(data);
console.log(sortedData);  // Output: [1, 2, 3, 5, 8, 9]

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public class InsertionSort {
    /**
     * Sort array in-place using Insertion Sort.
     *
     * @param arr Array of integers to sort
     */
    public static void insertionSort(int[] arr) {
        int n = arr.length;
        
        for (int i = 1; i < n; i++) {
            int key = arr[i];
            int j = i - 1;
            
            // Shift elements greater than key to the right
            while (j >= 0 && arr[j] > key) {
                arr[j + 1] = arr[j];
                j--;
            }
            
            // Insert key at its correct position
            arr[j + 1] = key;
        }
    }
    
    public static void main(String[] args) {
        int[] data = {5, 2, 8, 1, 9, 3};
        insertionSort(data);
        // data is now [1, 2, 3, 5, 8, 9]
    }
}

Insertion Sort vs. Bubble Sort vs. Selection Sort

We've now seen three quadratic sorting algorithms: Bubble Sort, Selection Sort, and Insertion Sort. While they share the same O(n²) worst-case complexity, their philosophies and practical behaviors differ significantly.

Philosophical Comparison of Quadratic Sorts
Aspect	Bubble Sort	Selection Sort	Insertion Sort
Core Operation	Swap adjacent elements	Find minimum, place at start	Insert into sorted portion
Does It Early-Terminate?	Yes (with optimization)	No	Yes (naturally)
Number of Swaps	O(n²) worst case	O(n) always	O(n²) worst case (shifts, not swaps)
Number of Comparisons	O(n²)	O(n²)	O(n²) worst, O(n) best
Stable?	Yes	No (naive implementation)	Yes
Adaptive?	Partially (with flag)	No	Yes (naturally)
Online?	No	No	Yes

Deep dive into the differences:

Bubble Sort works by repeatedly comparing adjacent elements and swapping if they're out of order. It's the most 'local' approach—only adjacent elements interact. The algorithm gets its name from large elements 'bubbling' to the end. While simple, it does excessive comparisons even when the array is nearly sorted (unless optimized with an early-exit flag).

Selection Sort takes a global view: scan the entire unsorted region to find the minimum, then swap it into place. It always does O(n²) comparisons regardless of input order, but only O(n) swaps. This makes it efficient when swaps are expensive (e.g., large objects in memory) but wasteful when the array is nearly sorted.

Insertion Sort is adaptive: its runtime depends on how sorted the input already is. On a perfectly sorted array, it does O(n) comparisons and zero shifts—just scans through. On a reverse-sorted array, it degrades to O(n²). This adaptivity makes it uniquely suited for nearly-sorted data.

The Practical Winner

Among quadratic sorts, Insertion Sort is generally the practical winner for small arrays or nearly-sorted data. It's the algorithm used in hybrid sorts like Timsort (Python's default) and Introsort (C++ STL) for small subarrays, because its low overhead and adaptivity outweigh its worst-case quadratic behavior.

Key Properties of Insertion Sort

Let's enumerate and explain the fundamental properties that define Insertion Sort's behavior and applicability.

Essential Properties

•In-Place Sorting — Insertion Sort uses O(1) extra memory (just the key variable and loop indices). It modifies the array in-place, making it memory-efficient for large datasets where auxiliary space is a concern.
•Stable Sorting — Equal elements maintain their relative order from the input. When inserting a key equal to an existing element, we place it after (not before) that element, preserving stability.
•Adaptive Sorting — The algorithm's runtime depends on input order. Nearly-sorted arrays are processed quickly; reverse-sorted arrays take maximum time. This adaptivity is Insertion Sort's distinguishing feature.
•Online Algorithm — Elements can be sorted as they arrive. You don't need the complete input upfront. Each new element is simply inserted into the already-sorted portion.
•Comparison-Based — Insertion Sort uses comparisons to determine element order. It doesn't assume anything about element values (like Counting Sort) and works with any comparable data type.
•Incremental Construction — The sorted region grows one element at a time. At any point, we have a valid partial solution.

Why These Properties Matter

These properties aren't just theoretical curiosities. In-place means embedded systems can use it. Stable means database records preserve secondary sort keys. Adaptive means live data streams can be maintained sorted efficiently. Online means sorted data structures can be built incrementally.

Loop Invariant: Proving Correctness

To rigorously understand why Insertion Sort works, we use the concept of a loop invariant—a property that holds before and after each iteration of a loop.

The Loop Invariant

Invariant: At the start of each iteration of the outer loop (for index i), the subarray A[0..i-1] contains the elements originally in those positions, but in sorted order.

Proving the invariant:

Initialization (before first iteration): When i = 1, the subarray A[0..0] contains a single element. A single-element array is trivially sorted. ✓

Maintenance (each iteration preserves it): Assume A[0..i-1] is sorted at the start of iteration i. The inner loop finds the correct position for A[i] within A[0..i-1], shifts elements right to make room, and inserts A[i]. After this, A[0..i] is sorted, so when i increments, A[0..(i+1)-1] = A[0..i] is sorted. ✓

Termination (invariant implies correctness): The loop terminates when i = n. At this point, i-1 = n-1, so A[0..n-1]—the entire array—is sorted. ✓

This formal reasoning proves that if Insertion Sort terminates, it produces a correctly sorted array. Since the outer loop runs exactly n-1 times and the inner loop always terminates (j decreases each iteration), the algorithm always terminates.

The inner loop invariant:

The inner loop (the while loop) has its own invariant:

Inner Invariant: At each iteration of the while loop, elements A[j+1..i] are greater than key and are correctly positioned relative to each other (they've been shifted right by one position).

This invariant ensures that when we finally place key at A[j+1], all elements to its right are larger, and all elements to its left are smaller or equal.

Variants and Modifications

The basic Insertion Sort can be modified in several ways to adapt to different scenarios or optimize for specific cases.

Common Variants

•Binary Insertion Sort — Instead of linear search to find the insertion point, use binary search. This reduces comparisons from O(n) to O(log n) per element, but shifts remain O(n), so overall complexity stays O(n²). Useful when comparisons are expensive.
•Recursive Insertion Sort — Express the algorithm recursively: sort A[0..n-2], then insert A[n-1]. This is pedagogically interesting but adds function call overhead without algorithmic benefit.
•Shell Sort (Generalization) — Sort elements that are 'gap' positions apart, then reduce the gap. When gap = 1, it's regular Insertion Sort, but earlier passes with larger gaps move elements closer to their final positions, reducing total work.
•Insertion Sort with Sentinel — Place a sentinel value (smaller than any real element) at position 0, then sort A[1..n-1]. This eliminates the j >= 0 boundary check, slightly improving performance.

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def binary_insertion_sort(arr):
    """
    Insertion Sort using binary search to find insertion position.
    Reduces comparisons but not shifts.
    """
    from bisect import bisect_left
    
    for i in range(1, len(arr)):
        key = arr[i]
        # Find position using binary search
        pos = bisect_left(arr, key, 0, i)
        # Shift elements and insert
        arr[pos+1:i+1] = arr[pos:i]
        arr[pos] = key
    
    return arr

Summary: The Insertion Sort Algorithm

We've thoroughly explored the Insertion Sort algorithm from intuition to implementation. Let's consolidate the key takeaways:

Key Takeaways

•Intuition — Insertion Sort mimics how humans sort playing cards: pick up elements one at a time and insert each into its correct position among the already-sorted elements.
•Mechanism — For each element (starting from index 1), store it in a 'key' variable, shift all larger elements in the sorted portion right by one, and insert the key in the vacated position.
•Properties — In-place (O(1) extra space), stable (preserves relative order), adaptive (faster on nearly-sorted input), and online (can sort data as it arrives).
•Invariant — At iteration i, elements A[0..i-1] are sorted. This invariant maintains correctness throughout execution.
•Philosophy — Unlike Bubble Sort's local swaps or Selection Sort's global minimum finding, Insertion Sort builds a growing sorted region by inserting elements into their correct positions.

What's next:

Now that we understand the algorithm's mechanics, the next page will explore how Insertion Sort builds the sorted portion incrementally—examining the process in even greater detail and understanding why this incremental approach leads to natural adaptivity.

Page Complete

You now understand the fundamental mechanics of Insertion Sort—the algorithm that mirrors human intuition for sorting. Next, we'll examine how the sorted portion grows incrementally and why this incremental construction gives Insertion Sort its unique advantages.

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Data Structures & AlgorithmsSorting Algorithms

Insertion Sort — Intuition & Analysis

LevelBeginner

Duration75 mins

TopicSorting Algorithms

1 / 4

The Insertion Sort Algorithm

Sorting Like a Card Player

What You Will Learn

The Card-Sorting Analogy: Understanding Insertion Sort Intuitively

Before examining code or formal algorithms, let's deeply explore the analogy that gives Insertion Sort its power and intuition: sorting a hand of playing cards.

The scenario:

Imagine you're dealt a hand of cards face-down. You pick them up one at a time:

Pick up the first card — It's a 7. With only one card, your hand is trivially sorted.
Pick up the second card — It's a 3. You compare it with 7. Since 3 < 7, you slide the 3 to the left of 7. Your hand: [3, 7].
Pick up the third card — It's a 9. You compare it with 7. Since 9 > 7, it stays where it is (at the end). Your hand: [3, 7, 9].
Pick up the fourth card — It's a 5. You compare it with 9 (too big), then 7 (too big), then 3 (smaller!). You insert 5 between 3 and 7. Your hand: [3, 5, 7, 9].
Continue until all cards are picked up.

This process—picking up an element, finding its correct position among already-sorted elements, and inserting it there—is the essence of Insertion Sort.

The Key Insight

Why this analogy matters:

The card-sorting analogy isn't just pedagogical—it reveals why Insertion Sort is:

Natural: Humans instinctively use this method because it's cognitively simple. We maintain a mental model of the sorted portion and only need to focus on placing one new item at a time.
Incremental: Unlike algorithms that process the entire array globally (like Merge Sort's divide-and-conquer), Insertion Sort builds the solution incrementally, one element at a time.
Online: You can start sorting before you have all the data. As new cards (elements) arrive, you simply insert them into the correct position. This property is called being an online algorithm.
Stable: When you insert a card equal to one already in your hand, you naturally place it after the existing one (or before—consistently). This preserves relative order of equal elements, making Insertion Sort stable.

Insertion Sort from First Principles

Let's formalize the card-sorting intuition into a precise algorithmic definition.

The Problem:

Given an array A of n elements, rearrange them such that A[0] ≤ A[1] ≤ A[2] ≤ ... ≤ A[n-1] (for ascending order).

Insertion Sort's Strategy:

Partition the array conceptually into two regions:

Sorted region: Initially contains just A[0]
Unsorted region: Contains A[1] through A[n-1]

Repeatedly:

Take the first element from the unsorted region
Insert it into its correct position within the sorted region
Expand the sorted region by one

Continue until the unsorted region is empty.

The Invariant

Why call it 'Insertion' Sort?

The insertion operation in detail:

When inserting element A[i] into the sorted subarray A[0..i-1]:

Store A[i] in a temporary variable key
Compare key with elements from right to left in the sorted region
Shift elements larger than key one position to the right
When you find an element ≤ key (or reach the beginning), insert key into the vacated position

This shifting-and-inserting is what distinguishes Insertion Sort from algorithms that swap elements in place.

Step-by-Step Walkthrough: Tracing Insertion Sort

Let's trace through Insertion Sort on the array [5, 2, 8, 1, 9, 3] to see exactly how it works.

Initial state: [5, 2, 8, 1, 9, 3]

The sorted region is [5], unsorted region is [2, 8, 1, 9, 3].

Insertion Sort Execution Trace
Iteration	Key	Action	Resulting Array
i=1	key=2	Compare with 5. 5>2, shift 5 right. Insert 2 at position 0.	[2, 5, 8, 1, 9, 3]
i=2	key=8	Compare with 5. 5<8, stop. Insert 8 at position 2 (no shift needed).	[2, 5, 8, 1, 9, 3]
i=3	key=1	Compare with 8 (shift), 5 (shift), 2 (shift). Insert 1 at position 0.	[1, 2, 5, 8, 9, 3]
i=4	key=9	Compare with 8. 8<9, stop. Insert 9 at position 4 (no shift).	[1, 2, 5, 8, 9, 3]
i=5	key=3	Compare with 9 (shift), 8 (shift), 5 (shift). 2<3, stop. Insert 3 at position 2.	[1, 2, 3, 5, 8, 9]

Observations from the trace:

Element 8 required no shifts — When an element is already larger than all sorted elements, it's already in the correct position. This is why Insertion Sort is efficient on nearly-sorted data.
Element 1 required the most work — A very small element at the end of the array needs to shift past all previously sorted elements. This is the worst case for a single insertion.
The sorted region grows left-to-right — After iteration i, elements A[0..i] are sorted.
Shifts, not swaps — We don't swap pairs of elements. We shift a contiguous block right and drop the key into the gap. This is more efficient than the multiple swaps of Bubble Sort.

Visualizing Shifts

The Algorithm in Pseudocode

Now that we understand the intuition and have traced an example, let's formalize Insertion Sort in pseudocode. This representation is language-agnostic and captures the algorithm's essence.

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INSERTION-SORT(A)
    n = length(A)
    
    for i = 1 to n - 1            // Start from second element
        key = A[i]                 // Element to be inserted
        j = i - 1                  // Last index of sorted region
        
        // Shift elements greater than key to the right
        while j >= 0 AND A[j] > key
            A[j + 1] = A[j]        // Shift element right
            j = j - 1              // Move to previous element
        
        A[j + 1] = key             // Insert key in correct position
    
    return A

Line-by-line explanation:

Line 4: We iterate from index 1 to n-1. Index 0 is already 'sorted' (single element).
Line 5: key holds the element we're currently inserting. We store it separately because the shifting will overwrite A[i].
Line 6: j starts at i-1, the last index of the sorted region.
Lines 9-11: The inner while loop performs the shifting. As long as we haven't reached the array's start AND the current element is larger than key, we shift that element right and move j left.
Line 13: Once the loop exits (either j < 0 or A[j] ≤ key), we insert key at position j + 1. This is the first position where the element to the left is ≤ key (or there's no element to the left).

Why j + 1, Not j?

Implementation in Multiple Languages

Let's translate the pseudocode into actual programming languages. Understanding implementations across languages helps cement the algorithm's structure.

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def insertion_sort(arr):
    """
    Sort array in-place using Insertion Sort.
    
    Args:
        arr: List of comparable elements
        
    Returns:
        The sorted list (same reference, modified in-place)
    """
    n = len(arr)
    
    for i in range(1, n):
        key = arr[i]
        j = i - 1
        
        # Shift elements greater than key to the right
        while j >= 0 and arr[j] > key:
            arr[j + 1] = arr[j]
            j -= 1
        
        # Insert key at its correct position
        arr[j + 1] = key
    
    return arr
 
 
# Example usage
data = [5, 2, 8, 1, 9, 3]
sorted_data = insertion_sort(data)
print(sorted_data)  # Output: [1, 2, 3, 5, 8, 9]

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function insertionSort<T>(arr: T[]): T[] {
    const n = arr.length;
    
    for (let i = 1; i < n; i++) {
        const key = arr[i];
        let j = i - 1;
        
        // Shift elements greater than key to the right
        while (j >= 0 && arr[j] > key) {
            arr[j + 1] = arr[j];
            j--;
        }
        
        // Insert key at its correct position
        arr[j + 1] = key;
    }
    
    return arr;
}
 
// Example usage
const data = [5, 2, 8, 1, 9, 3];
const sortedData = insertionSort(data);
console.log(sortedData);  // Output: [1, 2, 3, 5, 8, 9]

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public class InsertionSort {
    /**
     * Sort array in-place using Insertion Sort.
     *
     * @param arr Array of integers to sort
     */
    public static void insertionSort(int[] arr) {
        int n = arr.length;
        
        for (int i = 1; i < n; i++) {
            int key = arr[i];
            int j = i - 1;
            
            // Shift elements greater than key to the right
            while (j >= 0 && arr[j] > key) {
                arr[j + 1] = arr[j];
                j--;
            }
            
            // Insert key at its correct position
            arr[j + 1] = key;
        }
    }
    
    public static void main(String[] args) {
        int[] data = {5, 2, 8, 1, 9, 3};
        insertionSort(data);
        // data is now [1, 2, 3, 5, 8, 9]
    }
}

Insertion Sort vs. Bubble Sort vs. Selection Sort

Philosophical Comparison of Quadratic Sorts
Aspect	Bubble Sort	Selection Sort	Insertion Sort
Core Operation	Swap adjacent elements	Find minimum, place at start	Insert into sorted portion
Does It Early-Terminate?	Yes (with optimization)	No	Yes (naturally)
Number of Swaps	O(n²) worst case	O(n) always	O(n²) worst case (shifts, not swaps)
Number of Comparisons	O(n²)	O(n²)	O(n²) worst, O(n) best
Stable?	Yes	No (naive implementation)	Yes
Adaptive?	Partially (with flag)	No	Yes (naturally)
Online?	No	No	Yes

Deep dive into the differences:

The Practical Winner

Key Properties of Insertion Sort

Let's enumerate and explain the fundamental properties that define Insertion Sort's behavior and applicability.

Essential Properties

•In-Place Sorting — Insertion Sort uses O(1) extra memory (just the key variable and loop indices). It modifies the array in-place, making it memory-efficient for large datasets where auxiliary space is a concern.
•Stable Sorting — Equal elements maintain their relative order from the input. When inserting a key equal to an existing element, we place it after (not before) that element, preserving stability.
•Adaptive Sorting — The algorithm's runtime depends on input order. Nearly-sorted arrays are processed quickly; reverse-sorted arrays take maximum time. This adaptivity is Insertion Sort's distinguishing feature.
•Online Algorithm — Elements can be sorted as they arrive. You don't need the complete input upfront. Each new element is simply inserted into the already-sorted portion.
•Comparison-Based — Insertion Sort uses comparisons to determine element order. It doesn't assume anything about element values (like Counting Sort) and works with any comparable data type.
•Incremental Construction — The sorted region grows one element at a time. At any point, we have a valid partial solution.

Why These Properties Matter

Loop Invariant: Proving Correctness

To rigorously understand why Insertion Sort works, we use the concept of a loop invariant—a property that holds before and after each iteration of a loop.

The Loop Invariant

Invariant: At the start of each iteration of the outer loop (for index i), the subarray A[0..i-1] contains the elements originally in those positions, but in sorted order.

Proving the invariant:

Initialization (before first iteration): When i = 1, the subarray A[0..0] contains a single element. A single-element array is trivially sorted. ✓

Termination (invariant implies correctness): The loop terminates when i = n. At this point, i-1 = n-1, so A[0..n-1]—the entire array—is sorted. ✓

The inner loop invariant:

The inner loop (the while loop) has its own invariant:

Inner Invariant: At each iteration of the while loop, elements A[j+1..i] are greater than key and are correctly positioned relative to each other (they've been shifted right by one position).

This invariant ensures that when we finally place key at A[j+1], all elements to its right are larger, and all elements to its left are smaller or equal.

Variants and Modifications

The basic Insertion Sort can be modified in several ways to adapt to different scenarios or optimize for specific cases.

Common Variants

•Binary Insertion Sort — Instead of linear search to find the insertion point, use binary search. This reduces comparisons from O(n) to O(log n) per element, but shifts remain O(n), so overall complexity stays O(n²). Useful when comparisons are expensive.
•Recursive Insertion Sort — Express the algorithm recursively: sort A[0..n-2], then insert A[n-1]. This is pedagogically interesting but adds function call overhead without algorithmic benefit.
•Shell Sort (Generalization) — Sort elements that are 'gap' positions apart, then reduce the gap. When gap = 1, it's regular Insertion Sort, but earlier passes with larger gaps move elements closer to their final positions, reducing total work.
•Insertion Sort with Sentinel — Place a sentinel value (smaller than any real element) at position 0, then sort A[1..n-1]. This eliminates the j >= 0 boundary check, slightly improving performance.

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def binary_insertion_sort(arr):
    """
    Insertion Sort using binary search to find insertion position.
    Reduces comparisons but not shifts.
    """
    from bisect import bisect_left
    
    for i in range(1, len(arr)):
        key = arr[i]
        # Find position using binary search
        pos = bisect_left(arr, key, 0, i)
        # Shift elements and insert
        arr[pos+1:i+1] = arr[pos:i]
        arr[pos] = key
    
    return arr

Summary: The Insertion Sort Algorithm

We've thoroughly explored the Insertion Sort algorithm from intuition to implementation. Let's consolidate the key takeaways:

Key Takeaways

•Intuition — Insertion Sort mimics how humans sort playing cards: pick up elements one at a time and insert each into its correct position among the already-sorted elements.
•Mechanism — For each element (starting from index 1), store it in a 'key' variable, shift all larger elements in the sorted portion right by one, and insert the key in the vacated position.
•Properties — In-place (O(1) extra space), stable (preserves relative order), adaptive (faster on nearly-sorted input), and online (can sort data as it arrives).
•Invariant — At iteration i, elements A[0..i-1] are sorted. This invariant maintains correctness throughout execution.
•Philosophy — Unlike Bubble Sort's local swaps or Selection Sort's global minimum finding, Insertion Sort builds a growing sorted region by inserting elements into their correct positions.

What's next:

Page Complete

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