DSA · Theory

Time Complexity of Sorting Algorithms

Theory

58/810

Time Complexity of Sorting Algorithms

Time Complexity of Sorting Algorithms is a DSA topic. In plain words you use it for names A→Z in the roll list. Don’t start with a slogan — start with that picture.

Smallest example: n log n vs n². Type it, run it, and say what you see. If you can do that from memory, you know Time Complexity of Sorting Algorithms.

From the example next to this theory: Time Complexity of Sorting Algorithms — big-O describes how work grows with input size — ignore constants.

Trap — unstable sort when equal keys matter. Fix that before you talk about advanced DSA.

Viva — what is Time Complexity of Sorting Algorithms? Then show n log n vs n². Then name the trap.

Exam tip

What is Time Complexity of Sorting Algorithms? Show this: n log n vs n². Trap: unstable sort when equal keys matter.

Example

def demo(n):
    # O(1)
    x = n + 1
    # O(n)
    total = sum(range(n))
    # O(n^2)
    pairs = 0
    for i in range(n):
        for j in range(n):
            pairs += 1
    return x, total, pairs

print(demo(3))

Time Complexity of Sorting Algorithms — big-O describes how work grows with input size — ignore constants.

Short notes

DefTime Complexity of Sorting Algorithms — names A→Z in the roll list.
Rulen log n vs n²
Trapunstable sort when equal keys matter
Usethe roll list

DSA · Theory

Time Complexity of Sorting Algorithms

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Theory

58/810

Time Complexity of Sorting Algorithms

Time Complexity of Sorting Algorithms is a DSA topic. In plain words you use it for names A→Z in the roll list. Don’t start with a slogan — start with that picture.

Smallest example: n log n vs n². Type it, run it, and say what you see. If you can do that from memory, you know Time Complexity of Sorting Algorithms.

From the example next to this theory: Time Complexity of Sorting Algorithms — big-O describes how work grows with input size — ignore constants.

Trap — unstable sort when equal keys matter. Fix that before you talk about advanced DSA.

Viva — what is Time Complexity of Sorting Algorithms? Then show n log n vs n². Then name the trap.

Exam tip

What is Time Complexity of Sorting Algorithms? Show this: n log n vs n². Trap: unstable sort when equal keys matter.

Example

def demo(n):
    # O(1)
    x = n + 1
    # O(n)
    total = sum(range(n))
    # O(n^2)
    pairs = 0
    for i in range(n):
        for j in range(n):
            pairs += 1
    return x, total, pairs

print(demo(3))

Time Complexity of Sorting Algorithms — big-O describes how work grows with input size — ignore constants.

Short notes

DefTime Complexity of Sorting Algorithms — names A→Z in the roll list.
Rulen log n vs n²
Trapunstable sort when equal keys matter
Usethe roll list

Time Complexity of Sorting Algorithms

Example

Short notes

Questions

Time Complexity of Sorting Algorithms

Example

Short notes

Questions