A reference manual for people who design and analyze algorithms.
Main Topics:
Algorithms are step-by-step procedures for solving computational problems. This reference provides a concise overview of major algorithmic paradigms, their applications, complexity analysis, and implementation details.
A measure of how long an algorithm would take to complete given an input of size n.
A paradigm that solves a problem by breaking it into smaller subproblems, solving each recursively, then combining the results.
Often O(n log n) for sorting algorithms
Algorithms that use random numbers to achieve good expected performance regardless of input.
Expected O(n log n) for randomized sorting
A method for solving complex problems by breaking them down into simpler subproblems and storing their solutions to avoid redundant computation.
Usually polynomial time, depending on problem dimensions
Specialized formats for organizing and storing data to enable efficient access and modification.
From O(1) to O(log n) for most operations in optimized structures
Algorithms that operate on graph data structures consisting of vertices connected by edges.
From O(V+E) to O(V²) depending on graph representation and algorithm