Abstract: In 1997, the term critical chain was coined as an expansion of the critical path in project management and gained much popularity. The critical path has been a major breakthrough in modern management as it facilitates project management, thereby enabling effective and efficient management of gigantic projects almost independent of their size. The basic structure is the precedence diagram of the project, a directed graph which shows the project's logic the dependencies between the activities of the project. Adding estimated duration to each activity creates the project network. The longest path in this network is the critical path, whose length is a lower bound on the duration of the project. A major flaw of the critical path method is the disregard of the required resources and their availability, which might have significant effects on the project's schedule. Resource requirements and constraints are the primary concern of critical chains. However, there are much confusion and misconc

• Publication Date: 31-May-2020
• DOI: 10.15224/978-1-63248-188-7-19
• Views: 0
• Downloads: 0

Abstract: With the recent boom in big data analytics, many application areas require optimization algorithms that work at massive scale. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. In this note, we consider a distributed method for solving large-scale optimization problems called alternating minimization algorithm (AMA), and its implementation with TensorFlow is briefly reported.

• Publication Date: 31-May-2020
• DOI: 10.15224/978-1-63248-188-7-20
• Views: 0
• Downloads: 0