STOCHASTICOPT
Efficient Approximation for Stochastic Optimization
| Coordinatore | THE HEBREW UNIVERSITY OF JERUSALEM.
Organization address
address: GIVAT RAM CAMPUS contact info |
| Nazionalità Coordinatore | Israel [IL] |
| Totale costo | 100˙000 € |
| EC contributo | 100˙000 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2009-RG |
| Funding Scheme | MC-IRG |
| Anno di inizio | 2010 |
| Periodo (anno-mese-giorno) | 2010-04-01 - 2014-03-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
THE HEBREW UNIVERSITY OF JERUSALEM.
Organization address
address: GIVAT RAM CAMPUS contact info |
IL (JERUSALEM) | coordinator | 100˙000.00 |
Mappa
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Obiettivo del progetto (Objective)
'The primary goal of this research is to develop a general and constructive theory for a wide class of stochastic optimization problems in various research areas such as operations research, inventory and production management, project and machine scheduling, mathematical finance and theoretical computer science. This research project proposes to investigate efficient approximation methods for stochastic dynamic programming problems. The proposed project aims to establish when stochastic dynamic problems have FPTAS, develop generic tools to unify the creation of FPTAS, and develop efficient algorithms for realistic problems. If successful this research would lead to a better understanding of the difficulty involved in solving stochastic dynamic programming problems and to efficient approximations for stochastic optimization problems. The project is built in a modular way, exploring independently various practical topics in stochastic optimization such as stochastic models, structure of optimal policies, applied probability, dependencies of the stochastic variable, hardness results and practical performance.'
Introduzione (Teaser)
Most real-world 'problems' are stochastic or random in nature. Scientists developed novel optimisation algorithms for solving these complex problems and used them to address issues of inventory control and finance.