This section provides the schedule of lecture topics and a full set of lecture notes in two formats.

This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem.

These notes provide a tutorial treatment of topics of Pareto optimality, Lagrange multipliers, and computational algorithms for multiobjective optimization, with emphasis on applications to data networks.

To present students with a knowledge of the state-of-the art in the theory and practice of solving network flow problems. To provide students with a rigorous analysis of network flow algorithms. To help each student develop his or her own intuition about algorithm development and algorithm analysis. Have you seen network models elsewhere?

Oct 17, 2022 · Optimization algorithms are an essential aspect of deep learning, and PyTorch provides a wide range of optimization algorithms to help us train our neural networks effectively. In this article, we will explore various optimization algorithms in PyTorch and demonstrate how to implement them.

A common trait of most algorithm design techniques for network optimization is an iterative divide-and-conquer approach that somehow seeks to gradually approach the final solution by solving smaller intermediary subtasks.

Due to its widespread applications, graph and network optimization is an important subfield within the broad field of optimization. This article discusses some core graph and network optimization problems.

Recent advances in data structures, computer technology, and algorithm development have made it possible to solve classes of network optimization problems that until recently were intractable.

An extensive tutorial paper that surveys auction algorithms, a comprehensive class of algorithms for solving the classical linear network flow problem and its various special cases such as shortest path, max-flow, assignment, transportation, and transhipment problems.

Network optimization problems find numerous applications in transportation, in communication network design, in production and inventory planning, in facilities location and allocation, and in VLSI design.