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SIAMJO
2008
72views more  SIAMJO 2008»
13 years 4 months ago
A Sample Approximation Approach for Optimization with Probabilistic Constraints
We study approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical dis...
James Luedtke, Shabbir Ahmed
GECCO
2004
Springer
131views Optimization» more  GECCO 2004»
13 years 10 months ago
PolyEDA: Combining Estimation of Distribution Algorithms and Linear Inequality Constraints
Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models of good solutions to guide their search. When applied to co...
Jörn Grahl, Franz Rothlauf
ICRA
2010
IEEE
126views Robotics» more  ICRA 2010»
13 years 3 months ago
Simulation-based LQR-trees with input and state constraints
— We present an algorithm that probabilistically covers a bounded region of the state space of a nonlinear system with a sparse tree of feedback stabilized trajectories leading t...
Philipp Reist, Russ Tedrake
ICDE
2006
IEEE
163views Database» more  ICDE 2006»
14 years 6 months ago
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
Wireless sensor networks generate a vast amount of data. This data, however, must be sparingly extracted to conserve energy, usually the most precious resource in battery-powered ...
Adam Silberstein, Carla Schlatter Ellis, Jun Yang ...
ICDE
2010
IEEE
212views Database» more  ICDE 2010»
13 years 5 months ago
Cleansing uncertain databases leveraging aggregate constraints
— Emerging uncertain database applications often involve the cleansing (conditioning) of uncertain databases using additional information as new evidence for reducing the uncerta...
Haiquan Chen, Wei-Shinn Ku, Haixun Wang