What do you think?
Rate this book
The Probabilistic Minimum Spanning Tree Problem: Complexity and Combinatorial Properties
Explore a groundbreaking look at how randomness changes the classic minimum spanning tree.
This book studies the Probabilistic Minimum Spanning Tree, a model that minimizes expected cost or risk when presence of nodes is uncertain. It shows how a tree that works well on average can be very different from the best tree for a single instance, and why this makes the problem harder to solve.
Two main threads drive the practical motivation and rigorous analysis. You’ll see how a priori trees guide decisions in networks, transportation, and strategic planning, even when some nodes might be absent. The authors develop formulas to compute expected lengths, discuss exact complexity results, and connect PMST to other well-known problems, all while keeping a focus on real-world relevance and robustness of solutions.Understand how the PMST generalizes the classic MST and why robustness comes with higher complexity.Learn how expected tree length is derived under various dependency and independence assumptions.See the boundary between easy and hard cases, including when equal costs or complete graphs lead to faster solutions.Get a sense of how the PMST behaves as the probability of node presence changes, from near certainty to near absence. Ideal for readers of applied optimization, operations research, and algorithm design who want to see how uncertainty reshapes network planning and decision making.
This book studies the Probabilistic Minimum Spanning Tree, a model that minimizes expected cost or risk when presence of nodes is uncertain. It shows how a tree that works well on average can be very different from the best tree for a single instance, and why this makes the problem harder to solve.
Two main threads drive the practical motivation and rigorous analysis. You’ll see how a priori trees guide decisions in networks, transportation, and strategic planning, even when some nodes might be absent. The authors develop formulas to compute expected lengths, discuss exact complexity results, and connect PMST to other well-known problems, all while keeping a focus on real-world relevance and robustness of solutions.Understand how the PMST generalizes the classic MST and why robustness comes with higher complexity.Learn how expected tree length is derived under various dependency and independence assumptions.See the boundary between easy and hard cases, including when equal costs or complete graphs lead to faster solutions.Get a sense of how the PMST behaves as the probability of node presence changes, from near certainty to near absence. Ideal for readers of applied optimization, operations research, and algorithm design who want to see how uncertainty reshapes network planning and decision making.
50 pages, Hardcover
First published August 5, 2015
1 person want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.