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This volume contains selected papers presented at the "International Workshop on Computationally Intensive Methods in Simulation and Op­ th th timization" held from 23 to 25 August 1990 at the International Institute for Applied Systems Analysis (nASA) in La~enburg, Austria. The purpose of this workshop was to evaluate and to compare recently developed methods dealing with optimization in uncertain environments. It is one of the nASA's activities to study optimal decisions for uncertain systems and to make the result usable in economic, financial, ecological and resource planning. Over 40 participants from 12 different countries contributed to the success of the workshop, 12 papers were selected for this volume. Prof. A. Kurzhanskii Chairman of the Systems and Decision Sciences Program nASA Preface Optimization in an random environment has become an important branch of Applied Mathematics and Operations Research. It deals with optimal de­ cisions when only incomplete information of t.he future is available. Consider the following example: you have to make the decision about the amount of production although the future demand is unknown. If the size of the de­ mand can be described by a probability distribution, the problem is called a stochastic optimization problem.

This volume contains selected papers presented at the "International Workshop on Computationally Intensive Methods in Simulation and Op­ th th timization" held from 23 to 25 August 1990 at the International Institute for Applied Systems Analysis (nASA) in La~enburg, Austria. The purpose of this workshop was to evaluate and to compare recently developed methods dealing with optimization in uncertain environments. It is one of the nASA's activities to study optimal decisions for uncertain systems and to make the result usable in economic, financial, ecological and resource planning. Over 40 participants from 12 different countries contributed to the success of the workshop, 12 papers were selected for this volume. Prof. A. Kurzhanskii Chairman of the Systems and Decision Sciences Program nASA Preface Optimization in an random environment has become an important branch of Applied Mathematics and Operations Research. It deals with optimal de­ cisions when only incomplete information of t.he future is available. Consider the following example: you have to make the decision about the amount of production although the future demand is unknown. If the size of the de­ mand can be described by a probability distribution, the problem is called a stochastic optimization problem.

정보제공 :

I: Optimization of Simulated Systems.- Performance evaluation for the score function method in sensitivity analysis and stochastic optimization.- Experimental results for gradient estimation and optimization of a markov chain in steady-staten.- Optimization of stochastic discrete event dynamic systems.- Sensitivity analysis of simulation experiments: Regression analysis and statistical design.- II: Optimization and Stochastic Optimization.- A stochastic optimization approach for training the parameters in neural networks.- Integrated stochastic approximation program system.- Lexicographic duality in linear optimization.- Dual optimization of dynamic systems.- Stochastic approximation via averaging: The Polyak’s approach revisited.- III: Random Numbers.- Nonuniform random numbers: A sensitivity analysis for transformation methods.- Nonlinear methods for pseudorandom number and vector generation.- Sampling from discrete and continuous distributions with c-rand.

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