Parallel Random Search Algorithm : Of Constrained Pseudo-Boolean Optimization for Large-scale Problems

Lev Kazakovtsev

ISBN 10: 3843317216 / ISBN 13: 9783843317214

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Random search methods are implemented to solve the wide variety of the large-scale discrete optimization problems when the implementation of the exact solution approaches is impossible due to large computational demands. Initially designed for unconstrained optimization, the variant probabilities method allows us to find the approximate solution of pseudo-Boolean optimization problems with constraints. Although, in case of the large-scale problems, the computational demands are also very high and the precision of the result depends on the spent time. The rapid development of the parallel processor systems and clusters allows to reduce significantly the time spent to find the acceptable solution with speed-up close to ideal. In this paper, we consider an approach to the parallelizing of the algorithms realizing the variant probability method with adaptation and partial rollback procedure for constrained pseudo-Boolean optimization problems. Existing optimization algorithms are adapted for the systems with shared memory (OpenMP) and cluster systems (MPI library). The parallel efficiency is estimated for the large-scale non-linear pseudo-Boolean optimization problems.

Seller Inventory # 9783843317214

Bibliographic Details

• Title: Parallel Random Search Algorithm : Of Constrained Pseudo-Boolean Optimization for Large-scale Problems
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3843317216
• ISBN 13: 9783843317214
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 107 grams

About this title

Synopsis

Random search methods are implemented to solve the wide variety of the large-scale discrete optimization problems when the implementation of the exact solution approaches is impossible due to large computational demands. Initially designed for unconstrained optimization, the variant probabilities method allows us to find the approximate solution of pseudo-Boolean optimization problems with constraints. Although, in case of the large-scale problems, the computational demands are also very high and the precision of the result depends on the spent time. The rapid development of the parallel processor systems and clusters allows to reduce significantly the time spent to find the acceptable solution with speed-up close to ideal. In this paper, we consider an approach to the parallelizing of the algorithms realizing the variant probability method with adaptation and partial rollback procedure for constrained pseudo-Boolean optimization problems. Existing optimization algorithms are adapted for the systems with shared memory (OpenMP) and cluster systems (MPI library). The parallel efficiency is estimated for the large-scale non-linear pseudo-Boolean optimization problems.

About the Author

Lev Alexaksandrovich Kazakovtsev, Ph.D. in Engineering, Associate Professor of the Institute of Management and Informatics of the Krasnoyarsk State Agrarian University.