Robust Knapsack: Informativeness of Optimization Solutions - Softcover
We approach the knapsack problem from a statistical learning perspective. We consider a stochastic setting with uncertainty about the description of the problem instances. As a consequence, uncertainty about the optimal solution arises. We present a characterization of different classes of knapsack problem instances based on their sensitivity to noise variations. We do so by calculating the informativeness as measured by the approximation set coding (ASC) principle. We also demonstrate experimentally that, depending on the problem instance class, the ability to reliably localize good knapsack solution sets may or may not be a requirement for good generalization performance. Furthermore, we present a parametrization of knapsack solutions based on the concept of a knapsack core. We show that this parametrization allows to regularize the model complexity of the knapsack learning problem. Algorithms based on the core concept may benefit from this parametrization to achieve better generalization performance at reduced running times. Finally, we consider a randomized approximation scheme for the counting knapsack problem proposed by Dyer. We employ the ASC principle to determine the maximally informative approximation ratio.
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was 25 years old when he attained his master's degree in computer science at ETH Zürich. He currently works as a software engineer at Ergon Informatik.
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- PublisherAV Akademikerverlag
- Publication date2013
- ISBN 10 3639474198
- ISBN 13 9783639474190
- BindingPaperback
- Number of pages88
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Robust Knapsack
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Book Description Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -We approach the knapsack problem from a statistical learning perspective. We consider a stochastic setting with uncertainty about the description of the problem instances. As a consequence, uncertainty about the optimal solution arises. We present a characterization of different classes of knapsack problem instances based on their sensitivity to noise variations. We do so by calculating the informativeness as measured by the approximation set coding (ASC) principle. We also demonstrate experimentally that, depending on the problem instance class, the ability to reliably localize good knapsack solution sets may or may not be a requirement for good generalization performance. Furthermore, we present a parametrization of knapsack solutions based on the concept of a knapsack core. We show that this parametrization allows to regularize the model complexity of the knapsack learning problem. Algorithms based on the core concept may benefit from this parametrization to achieve better generalization performance at reduced running times. Finally, we consider a randomized approximation scheme for the counting knapsack problem proposed by Dyer. We employ the ASC principle to determine the maximally informative approximation ratio. 88 pp. Englisch. Seller Inventory # 9783639474190
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Robust Knapsack : Informativeness of Optimization Solutions
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Book Description Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - We approach the knapsack problem from a statistical learning perspective. We consider a stochastic setting with uncertainty about the description of the problem instances. As a consequence, uncertainty about the optimal solution arises. We present a characterization of different classes of knapsack problem instances based on their sensitivity to noise variations. We do so by calculating the informativeness as measured by the approximation set coding (ASC) principle. We also demonstrate experimentally that, depending on the problem instance class, the ability to reliably localize good knapsack solution sets may or may not be a requirement for good generalization performance. Furthermore, we present a parametrization of knapsack solutions based on the concept of a knapsack core. We show that this parametrization allows to regularize the model complexity of the knapsack learning problem. Algorithms based on the core concept may benefit from this parametrization to achieve better generalization performance at reduced running times. Finally, we consider a randomized approximation scheme for the counting knapsack problem proposed by Dyer. We employ the ASC principle to determine the maximally informative approximation ratio. Seller Inventory # 9783639474190
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Robust Knapsack Informativeness of Optimization Solutions
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Book Description Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Stelling Simonwas 25 years old when he attained his master s degree in computer science at ETH Zuerich. He currently works as a software engineer at Ergon Informatik.We approach the knapsack problem from a statistical learning per. Seller Inventory # 4991274
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