Algorithm analysis is an important part of computational complexity theory, which provides theoretical estimation for the required resources of an algorithm to solve a specific computational problem. ...

Probabilistic analysis is a way of measuring the performance of an algorithm based on the likelihood of different inputs and outcomes. It assumes that the input data is drawn from a known ...

It does not specify how data will be organized in memory and what algorithms will be used for implementing the operations. These process of hiding details is also known as Abstraction. For example, ...

COMP 372 introduces the fundamental techniques for designing and analyzing algorithms. These include asymptotic notation and analysis, divide-and-conquer algorithms, dynamic programming, greedy ...

Amortized analysis, and Order Statics. Chapter 4: Graph algorithms, BFS, DFS, Spanning Tree, Flow Maximization Algorithms. Shortest Path Algorithms. Chapter 5: Binary search tree, Red black Tree, ...

"The book under review is an interesting elaboration that fills the gaps in libraries for concisely written and student-friendly books about essentials in computer science … I recommend this book for ...

Using recent results of Boyd, Diaconis and Xiao (2003), we show that minimizing this quantity to design the fastest averaging algorithm on the network is a semi-definite program (SDP). In general, ...

Large language models (LLMs) have seen rapid advancements, making significant strides in algorithmic problem-solving tasks. These models are being integrated into algorithms to serve as ...