Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems. Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in fighting uncertainty with uncertainty. This book discusses theoretical aspects of many such algorithms and covers their application in various scientific fields
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We s...
http://deepblue.lib.umich.edu/bitstream/2027.42/3641/5/bbl3540.0001.001.pdfhttp://deepblue.lib.umich...
This book addresses stochastic optimization procedures in a broad manner. The first part offers an o...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
Optimization problems arising in practice involve random model parameters. This book features many i...
International audienceOptimization under uncertainty is a key problem in order to solve complex syst...
• Stochastic optimization refers to the minimization (or maximization) of a function in the presence...
The course covers a variety of topics in stochastic optimization. To begin with, some ap-proaches to...
Abstract Stochastic Programming (SP) was first introduced by George Dantzig in the 1950’s. Since tha...
The focus of the present volume is stochastic optimization of dynamical systems in discrete time whe...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
This book presents the main methodological and theoretical developments in stochastic global optimiz...
Optimizing decision problems under uncertainty can be done using a vari-ety of solution methods. Sof...
Uncertainty is a facet of many decision environments and might arise for various reasons, such as un...
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We s...
http://deepblue.lib.umich.edu/bitstream/2027.42/3641/5/bbl3540.0001.001.pdfhttp://deepblue.lib.umich...
This book addresses stochastic optimization procedures in a broad manner. The first part offers an o...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
Optimization problems arising in practice involve random model parameters. This book features many i...
International audienceOptimization under uncertainty is a key problem in order to solve complex syst...
• Stochastic optimization refers to the minimization (or maximization) of a function in the presence...
The course covers a variety of topics in stochastic optimization. To begin with, some ap-proaches to...
Abstract Stochastic Programming (SP) was first introduced by George Dantzig in the 1950’s. Since tha...
The focus of the present volume is stochastic optimization of dynamical systems in discrete time whe...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
This book presents the main methodological and theoretical developments in stochastic global optimiz...
Optimizing decision problems under uncertainty can be done using a vari-ety of solution methods. Sof...
Uncertainty is a facet of many decision environments and might arise for various reasons, such as un...
Stochastic methods are present in our daily lives, especially when we need to make a decision based ...
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We s...
http://deepblue.lib.umich.edu/bitstream/2027.42/3641/5/bbl3540.0001.001.pdfhttp://deepblue.lib.umich...
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