Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficul...
Abstract. Combinatorial Auctions are an attractive application of intelligent agents; their applications are countless and are shown to provide good revenues. On the other hand, on...
Marco Alberti, Federico Chesani, Alessio Guerri, M...
We present an efficient optimization scheme for gate sizing in the presence of process variations. Our method is a worst-case design scheme, but it reduces the pessimism involved i...
Jaskirat Singh, Zhi-Quan Luo, Sachin S. Sapatnekar
Simulated annealing has been one of the most popular stochastic optimization methods used in the VLSI CAD field in the past two decades for handling NP-hard optimization problems...
Jason Cong, Tianming Kong, Faming Liang, Jun S. Li...
Estimation of distribution algorithms (EDAs) are widely used in stochastic optimization. Impressive experimental results have been reported in the literature. However, little work ...