"Combinatorial Problems: Minimum Spanning Tree" is a beginner-friendly introduction to the concept of Minimum Spanning Trees (MST), a fundamental tool in computer science and engineering. This book provides clear explanations and practical examples to demystify MST algorithms, which are essential for efficiently connecting nodes in various networks while minimizing costs. Aimed at absolute beginners, it covers the basic principles, step-by-step algorithms, and real-world applications of MST in telecommunications, transportation, and more. Whether you're a student, aspiring engineer, or curious reader, this accessible guide equips you with the foundational knowledge to understand and apply MST effectively in solving connectivity challenges across different fields.

The design of approximation algorithms for spanning tree problems has become an exciting and important area of theoretical computer science and also plays a significant role in emerging fields such as biological sequence alignments and evolutionary tree construction. While work in this field remains quite active, the time has come to collect under

This well-written textbook on combinatorial optimization puts special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. The book contains complete (but concise) proofs, as well as many deep results, some of which have not appeared in any previous books.

Introductory courses in combinatorial optimization are popular at the upper undergraduate/graduate levels in computer science, industrial engineering, and business management/OR, owed to its wide applications in these fields. There are several published textbooks that treat this course and the authors have used many of them in their own teaching experiences. This present text fills a gap and is organized with a stress on methodology and relevant content, providing a step-by-step approach for the student to become proficient in solving combinatorial optimization problems. Applications and problems are considered via recent technology developments including wireless communication, cloud computing, social networks, and machine learning, to name several, and the reader is led to the frontiers of combinatorial optimization. Each chapter presents common problems, such as minimum spanning tree, shortest path, maximum matching, network flow, set-cover, as well as key algorithms, such as greedy algorithm, dynamic programming, augmenting path, and divide-and-conquer. Historical notes, ample exercises in every chapter, strategically placed graphics, and an extensive bibliography are amongst the gems of this textbook.

This title provides a comprehensive survey over the subject of probabilistic combinatorial optimization, discussing probabilistic versions of some of the most paradigmatic combinatorial problems on graphs, such as the maximum independent set, the minimum vertex covering, the longest path and the minimum coloring. Those who possess a sound knowledge of the subject mater will find the title of great interest, but those who have only some mathematical familiarity and knowledge about complexity and approximation theory will also find it an accessible and informative read.

This updated edition presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discusses binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. Includes 153 black-and-white illustrations and 23 tables.

Excerpt from The Probabilistic Minimum Spanning Tree Problem: Complexity and Combinatorial Properties In this paper we consider a natural probabilistic variation of this classical problem. In particular, we consider the case where not all the points are deterministically present, but are present with certain probability. Formally, given a weighted graph G (v, E) and a probability of presence p(s) for each subset S of V, we want to construct an a priori spanning tree of minimum expected length in the following sense: on any given instance of the problem delete the vertices and their adjacent edges among the set of absent vertices provided that the tree remains connected. The problem of finding an a priori spanning tree of minimum expected length is the probabilistic minimum spanning tree (pmst) problem. In order to clarify the definition of the pmst problem, consider the example in Figure 1. If the a priori tree is T and nodes are the only ones not present, the tree becomes ti. One can easily observe that if every node is present with probability p. 1 for all i E V then the problem reduces to the classical mst problem. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.