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» Nonlinear Stochastic Optimization by the Monte-Carlo Method
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DAC
1996
ACM
13 years 11 months ago
Computing Parametric Yield Adaptively Using Local Linear Models
Abstract A divide-and-conquer algorithm for computing the parametric yield of large analog circuits is presented. The algorithm targets applications whose performance spreads could...
Mien Li, Linda S. Milor
COR
2006
122views more  COR 2006»
13 years 7 months ago
Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms
A problem of assigning cooperating uninhabited aerial vehicles to perform multiple tasks on multiple targets is posed as a new combinatorial optimization problem. A genetic algori...
Tal Shima, Steven J. Rasmussen, Andrew G. Sparks, ...
NIPS
2001
13 years 8 months ago
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...
AE
2005
Springer
14 years 1 months ago
Algorithms (X, sigma, eta): Quasi-random Mutations for Evolution Strategies
Randomization is an efficient tool for global optimization. We here define a method which keeps : – the order 0 of evolutionary algorithms (no gradient) ; – the stochastic as...
Anne Auger, Mohamed Jebalia, Olivier Teytaud
DAC
2006
ACM
14 years 8 months ago
Stochastic variational analysis of large power grids considering intra-die correlations
For statistical timing and power analysis that are very important problems in the sub-100nm technologies, stochastic analysis of power grids that characterizes the voltage fluctua...
Praveen Ghanta, Sarma B. K. Vrudhula, Sarvesh Bhar...