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Motion pictures --- Religious aspects --- Catholic Church. --- History. --- #SBIB:316.331H537 --- #SBIB:309H1313 --- Cinema --- Feature films --- Films --- Movies --- Moving-pictures --- Audio-visual materials --- Mass media --- Performing arts --- Catholic Church and motion pictures --- Motion pictures and Catholic Church --- Godsdienstige praktijken: massamedia --- Geschiedenis en/of organisatie van het filmwezen: algemeen en per land (met inbegrip van de rol van het filmwezen in de ontwikkelingsproblematiek) --- History and criticism --- Religious aspects&delete& --- Catholic Church --- History --- Motion pictures - Religious aspects - Catholic Church. --- Motion pictures - Germany - Bavaria - History.
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Stochastic programming provides a framework for modelling, analyzing, and solving optimization problems with some parameters being not known up to a probability distribution. Such problems arise in a variety of applications, such as inventory control, financial planning and portfolio optimization, airline revenue management, scheduling and operation of power systems, and supply chain management. Christian Küchler studies various aspects of the stability of stochastic optimization problems as well as approximation and decomposition methods in stochastic programming. In particular, the author presents an extension of the Nested Benders decomposition algorithm related to the concept of recombining scenario trees. The approach combines the concept of cut sharing with a specific aggregation procedure and prevents an exponentially growing number of subproblem evaluations. Convergence results and numerical properties are discussed.
Decision-making. --- Stochastic programming. --- Uncertainty. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Theory --- Mathematics. --- Mathematical models. --- Probabilities. --- Mathematical Modeling and Industrial Mathematics. --- Probability Theory and Stochastic Processes. --- Mathematics, general. --- Linear programming --- Distribution (Probability theory. --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Models, Mathematical --- Simulation methods
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Stochastic programming provides a framework for modelling, analyzing, and solving optimization problems with some parameters being not known up to a probability distribution. Such problems arise in a variety of applications, such as inventory control, financial planning and portfolio optimization, airline revenue management, scheduling and operation of power systems, and supply chain management. Christian Küchler studies various aspects of the stability of stochastic optimization problems as well as approximation and decomposition methods in stochastic programming. In particular, the author presents an extension of the Nested Benders decomposition algorithm related to the concept of recombining scenario trees. The approach combines the concept of cut sharing with a specific aggregation procedure and prevents an exponentially growing number of subproblem evaluations. Convergence results and numerical properties are discussed.
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