  This paper develops and analyzes a stochastic derivative-free optimization
strategy. A key feature is the state-dependent adaptive variance. We prove
global convergence in probability with algebraic rate and give the quantitative
results in numerical examples. A striking fact is that convergence is achieved
without explicit information of the gradient and even without comparing
different objective function values as in established methods such as the
simplex method and simulated annealing. It can otherwise be compared to
annealing with state-dependent temperature.
