Monte Carlo Optimization and Path Dependent Nonstationary Laws of Large Numbers
AbstractNew types of laws of large numbers are derived by using connections between estimation and stochastic optimization problems. They enable one to "track" time-and-path dependent functionals by using, in general, nonlinear estimators. Proofs are based on the new stochastic version of the Lyapunov's method. Applications to Monte Carlo optimization, stochastic branch and bounds method and minimization of risk functions are discussed.
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Bibliographic InfoPaper provided by International Institute for Applied Systems Analysis in its series Working Papers with number ir98009.
Date of creation: Mar 1998
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