文件名称:stoch_opt.zip
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Stochastic optimization: Operations research has been particularly successful in two areas of decision analysis: (i)
optimization of problems involving many variables when the outcome of the decisions can
be predicted with certainty, and (ii) the analysis of situations involving a few variables
when the outcome of the decisions cannot be predicted with certainty. The earlier chapters
of this book have identified this dichotomy as deterministic optimization versus stochastic
analysis. The optimization of problems in which the outcomes are uncertain, the subject of
this chapter, is still in its infancy so there is still much room for improvement in
computational methods.,Stochastic optimization: Operations research has been particularly successful in two areas of decision analysis: (i)
optimization of problems involving many variables when the outcome of the decisions can
be predicted with certainty, and (ii) the analysis of situations involving a few variables
when the outcome of the decisions cannot be predicted with certainty. The earlier chapters
of this book have identified this dichotomy as deterministic optimization versus stochastic
analysis. The optimization of problems in which the outcomes are uncertain, the subject of
this chapter, is still in its infancy so there is still much room for improvement in
computational methods.
optimization of problems involving many variables when the outcome of the decisions can
be predicted with certainty, and (ii) the analysis of situations involving a few variables
when the outcome of the decisions cannot be predicted with certainty. The earlier chapters
of this book have identified this dichotomy as deterministic optimization versus stochastic
analysis. The optimization of problems in which the outcomes are uncertain, the subject of
this chapter, is still in its infancy so there is still much room for improvement in
computational methods.,Stochastic optimization: Operations research has been particularly successful in two areas of decision analysis: (i)
optimization of problems involving many variables when the outcome of the decisions can
be predicted with certainty, and (ii) the analysis of situations involving a few variables
when the outcome of the decisions cannot be predicted with certainty. The earlier chapters
of this book have identified this dichotomy as deterministic optimization versus stochastic
analysis. The optimization of problems in which the outcomes are uncertain, the subject of
this chapter, is still in its infancy so there is still much room for improvement in
computational methods.
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