Network flows: theory, algorithms, and applicationsBringing together the classic and the contemporary aspects of the field, this comprehensive introduction to network flows provides an integrative view of theory, algorithms, and applications. It offers indepth and selfcontained treatments of shortest path, maximum flow, and minimum cost flow problems, including a description of new and novel polynomialtime algorithms for these core models. For professionals working with network flows, optimization, and network programming. 
What people are saying  Write a review
User ratings
5 stars 
 
4 stars 
 
3 stars 
 
2 stars 
 
1 star 

User Review  Flag as inappropriate
Approachable, powerful text.
Review: Network Flows: Theory, Algorithms, and Applications
User Review  Joecolelife  GoodreadsDr.Tom and other authors have done a great job in writing this book. The book covers a lot of topics in Network Programming and a variety of algorithms are cited. A must buy for any serious student taking a course in Network Programming. Read full review
Contents
INTRODUCTION  1 
PATHS TREES AND CYCLES  23 
ALGORITHM DESIGN AND ANALYSIS  53 
Copyright  
25 other sections not shown
Common terms and phrases
adjacency list algorithm performs Application arc costs arc flows arc i,j arc lengths augmenting path algorithm bipartite capacity scaling algorithm commodity constraints cost flow problem define denote Dijkstra's algorithm directed cycle directed network directed path discussion distance label example Exercise feasible flow feasible solution Fibonacci heap flow algorithms formulation implementation integer iteration labelcorrecting algorithm Lagrangian multiplier Lagrangian relaxation Lemma linear programming lower bound matching matrix maximum flow problem minimum cost flow minimum spanning tree multicommodity flow problem negative cycle network contains network flow problem network G network simplex algorithm node potentials nonnegative objective function value operation optimal solution optimality conditions path from node polynomial preflowpush algorithm reduced cost residual network st cut satisfies scaling phase Section shortest path distances shortest path problem Show shown in Figure simplex method sink node source node Suppose Theorem undirected units of flow upper bound variables zero