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On the boundedness problem of stochastic adaptive algorithms

The analysis of stochastic adaptive algorithms has been dogged by a boundedness problem from the beginning. While it has been overcome in specific cases it continues to be the achilles heel of general proof methods. Here we provide a new and simple projection method which overcomes these problems. We demonstrate this by finite time averaging analysis for both single time-scale and mixed time- scale, fixed gain, nonlinear algorithms.