BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems

Resource Information

The work BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems represents a distinct intellectual or artistic creation found in European University Institute. This resource is a combination of several types including: Work, Language Material, Books.

Label

BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems

Statement of responsibility

by Urmila Diwekar, Amy David

Creator

• Diwekar, Urmila

Contributor

• SpringerLink (Online service)
• David, Amy.

Author

• Diwekar, Urmila
• David, Amy.

Subject

• Management science
• Mathematics
• Operations research
• System theory
• Algorithms
• Ergodic theory
• Dynamics

Language

eng

Summary

This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world

Member of

• SpringerBriefs in Optimization,
• Springer eBooks, Mathematics and Statistics Collection

Image bit depth

0

Literary form

non fiction

Series statement

SpringerBriefs in Optimization,