Programming Concepts Intermediate

What is Big O Notation?

A mathematical notation that describes the worst-case performance of an algorithm as input size grows.

Big O describes how an algorithm scales. O(1) is constant time (hash lookup). O(log n) is logarithmic (binary search). O(n) is linear (simple loop). O(n log n) is linearithmic (efficient sorting). O(n²) is quadratic (nested loops). O(2^n) is exponential.

Understanding Big O helps choose the right algorithm and data structure. For example, searching a sorted array with binary search O(log n) is vastly faster than linear search O(n) for large datasets.

Related Terms

A data structure that follows Last-In-First-Out (LIFO) ordering, where elements are added and removed from the same end (top).

Garbage Collection

An automatic memory management process that identifies and reclaims memory no longer in use by a program.

A function that captures and retains access to variables from its enclosing scope, even after that scope has finished executing.

SOLID Principles

Five design principles for writing maintainable, flexible object-oriented code: Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion.

A reusable solution template for commonly occurring problems in software design.

Observer Pattern

A design pattern where an object (subject) maintains a list of dependents (observers) that are notified automatically of state changes.

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