Time Complexity

April 1, 2024 • Computer

mukesh juadi

mukesh juadi

Developer passionate about merging technology and creativity in software, games, websites, and more to create engaging experiences.

Time complexity measures the amount of time an algorithm takes to run as a function of the length of its input.

Time Complexity • 6 pages

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understand Time Complexity by mukesh juado

what is time complexity?

Time Complexity measures the amount of time an algorithm takes to run asa function of the length of its input.

it helps us understand how efficient an algorithm in therms of time.

example: imagine you're sorting a deck of cards. how long would it take?

Big O Notation.

Big O notation is mathematical notation
that describes the limiting behavior of a
function when the argument approaches a
particular value or infinity.

it's used to represent the upper bound of
the time an algorithm takes to run.

Big O notation helps us understand how the
runtime of an algorithm grows as the size of
the input grows.

it simplifies the analysis of algorithms’
efficiency by focusing on the most
significant factors affecting runtime.

Examples of Time Complexities.
O(1) - Constant Time: Operations that take
the same amount of time regardless of the
input size.

Example: Accessing an element in an array.

O(n) - Linear Time: Operations whose time
increases linearly with the size of the input.

Example: Searching for an element in an
unsorted list.

O(n*2) - Quadratic Time: Operations
whose time increases quadratically with
the size of the input.

Example: Bubble sort.

Understanding Time Complexity
with Sorting Algorithms.

Bubble Sort: O(n*2) - Compare adjacent
elements and swap them if they are in the
wrong order.

Quick Sort: O(n log n) - Divide and conquer
approach to sorting.

Insertion Sort: O(n*2) - Build a sorted
array one element at a time.

Time complexity helps us analyze the
efficiency of algorithms.

It's important to choose the right algorithm
based on the problem's input size.

Understanding time complexity empowers
us to write more efficient code.