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» Hedging Uncertainty: Approximation Algorithms for Stochastic...
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EMO
2005
Springer
68views Optimization» more  EMO 2005»
15 years 3 months ago
Multi-objective Optimization of Problems with Epistemic Uncertainty
Abstract. Multi-objective evolutionary algorithms (MOEAs) have proven to be a powerful tool for global optimization purposes of deterministic problem functions. Yet, in many real-w...
Philipp Limbourg
CORR
2012
Springer
235views Education» more  CORR 2012»
13 years 5 months ago
An Incremental Sampling-based Algorithm for Stochastic Optimal Control
Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation ...
Vu Anh Huynh, Sertac Karaman, Emilio Frazzoli
CCE
2007
14 years 9 months ago
Water networks security: A two-stage mixed-integer stochastic program for sensor placement under uncertainty
This work describes a stochastic approach for the optimal placement of sensors in municipal water networks to detect maliciously injected contaminants. The model minimizes the exp...
Vicente Rico-Ramírez, Sergio Frausto-Hern&a...
APPROX
2010
Springer
188views Algorithms» more  APPROX 2010»
14 years 11 months ago
Approximation Algorithms for Reliable Stochastic Combinatorial Optimization
We consider optimization problems that can be formulated as minimizing the cost of a feasible solution wT x over an arbitrary combinatorial feasible set F {0, 1}n . For these pro...
Evdokia Nikolova
STOC
2004
ACM
118views Algorithms» more  STOC 2004»
15 years 9 months ago
Boosted sampling: approximation algorithms for stochastic optimization
Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the STEINER T...
Anupam Gupta, Martin Pál, R. Ravi, Amitabh ...