DSA Visualizer
An interactive tool to visualize data structures and algorithms. Choose a concept below to get started.
Complexity Analysis
Time and Space complexity are crucial concepts that describe how the resource usage (time or memory) of an algorithm grows as the input size increases. Understanding Big O, Omega (Ω), and Theta (Θ) notations is fundamental to analyzing algorithm efficiency.
Big O notation describes the worst-case scenario or an upper bound on the time or space an algorithm will take. It tells us that the algorithm's performance will not be worse than this rate of growth.
Common Growth Rates
This chart visualizes how the number of operations (Y-axis) grows as the input size 'n' (X-axis) increases. Adjust the slider to see how the growth rates compare.
Constant
Logarithmic
Linear
Log-Linear
Quadratic