Background
Sen, Suvrajeet was born on August 11, 1954 in Bombay, India. Son of Baidyanath and Lakshmi (Roy) Sen. came to the United States, 1977.
- Photos
- Works
- Photos
- Works
book
Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications)
(Motivation Stochastic Linear Programming with recourse re...)
Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.
http://www.amazon.com/gp/product/1461368456/?tag=2022091-20
Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications) by Julia L. Higle (1996-02-29)
http://www.amazon.com/gp/product/B01F827HKA/?tag=2022091-20
Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications)
(Motivation Stochastic Linear Programming with recourse re...)
Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.
http://www.amazon.com/gp/product/0792338405/?tag=2022091-20
Suvrajeet Sen
Edit Profileresearcher engineering educator
Suvrajeet Sen, American Engineering educator, researcher. Achievements include development of stochastic decomposition, a new algorithm for stochastic optimization; development of on-line tools for academic advising; development of real-time algorithms for traffic control. Recipient ELITE Project award, 1996, Integrated Planning award, 1994,National Science Foundation.
Education
Bachelor in Engineering (honorary), Birla Institute Technology and Science, Pilani, India, 1977; Master in Engineering in Engineering Management, U. Louisiana, 1979; Doctor of Philosophy in Operations Research, Virginia Polytechnic Institute, Blacksburg, 1982.
Career
Research associate, Pennsylvania State University, State College, 1981-1982;
assistant professor, U. Arizona, Tucson, 1982-1987;
associate professor, 1987-1994;
professor, since 1994. Chair Institute for Operations Research and the Management Sciences Telecommunications, Providence, Rhode Island, 1995-1996. Founder Institute for Operations Research and the Management Sciences Optimization Section Providence, Rhode Island, 1995-1996.
Program co-chair Institute for Operations Research and the Management Sciences Conference, Phoenix, 1993. Program chair Institute for Operations Research and the Management Sciences Telecommunications Conference, Boca Raton, Florida, 1998.
Achievements
Works
book
-
Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications)
(Motivation Stochastic Linear Programming with recourse re...)
-
Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications)
(Motivation Stochastic Linear Programming with recourse re...)
- Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications) by Julia L. Higle (1996-02-29)
-
Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications)
Membership
Volunteer Cmty. Food Bank, Tucson, since 1996. Member Institute for Management Science and Operations Research, Mathematics Programming Society.
Connections
Married (divorced December 9, 1993). Children:Shurid Kyle, Sonali Tanya.
