Efficient Monte Carlo Optimization with ATMathCoreLib

スポンサーリンク

概要

• 論文の詳細を見る

• In this paper, we discuss a deterministic optimization method for stochastic simulation with unknown distribution. The problem to solve is the following: there is a parameter t to which a stochastic cost function f(t, z) is associated, where z is the nuisance parameter. We want to estimate t with which the average of f(t, z) is minimum, with as small number of evaluations of f(t, z) as possible. Our method is based on Bayesian formulation, and utilizes the information given through the prior distribution.

• 2012-03-19

著者

• Reiji Suda

  Presently With Crest Jst

• Reiji Suda

  Graduate School of Information Science, the University of Tokyo|CREST, JST

関連論文

• An execution time prediction analytical model for GPU with instruction-level and thread-level parallelism awareness
• A precise measurement tool for power dissipation of CUDA kernels
• A Three-Step Performance Automatic Tuning Strategy using Statistical Model for OpenCL Implementation of Krylov Subspace Methods
• Efficient Monte Carlo Optimization with ATMathCoreLib
• Evaluation of Impact of Noise on Collective Algorithms in Repeated Computation Cycle
• The Future of Accelerator Programming: Abstraction, Performance or Can We Have Both?

スポンサーリンク