Core Concepts

Tags: #◇CCDSALG #◇Algorithms #◇Computer Science #◇Computational Complexity #◇Efficiency #◇Algorithm Design Paradigms #◇Data Structures #◇Time Complexity #◇Space Complexity

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◇Overview

Algorithm Analysis

◇Key Concepts

◇Algorithm ◇Algorithm Analysis ◇Input Size ◇Running Time ◇Frequency Count

◇Details / Theory

◇Algorithm ◇Algorithm Analysis ◇Input Size ◇Running Time ◇Frequency Count ◇Asymptotic Notation ◇Recurrences

◇Complexity

◇Common Pitfalls

Confusing input size with input value — input size is the number of data items (or bits), not their magnitude Mixing up running time (function of input size) with wall-clock time (hardware-dependent) Performing frequency count but forgetting to simplify using asymptotic notation — raw counts are less meaningful without Big-O classification Misapplying asymptotic notation — e.g., $O(n^2)$ is an upper bound, not a tight bound (use $\Theta$ for tight bounds)

◇Summary

TL;DR — Core concepts of algorithm analysis: algorithm (finite procedure), algorithm analysis (measuring efficiency), input size (problem scale), running time (operations as a function of input size), frequency count (operation tallying), asymptotic notation ($O$/$\Omega$/$\Theta$), and recurrences (for recursive algorithms).

◇Links

Related: - ◇Algorithm - ◇Algorithm Analysis - ◇Input Size - ◇Running Time - ◇Frequency Count - ◇Asymptotic Notation - ◇Recurrences Source / Reference:

◇References & Navigation

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