Adaptive parameter estimation using interior point optimization techniques: convergence analysis

Interior point optimization techniques have emerged as a new tool for developing parameter estimation algorithms. These algorithms aim to take advantage of the fast convergence properties of interior point methods, to yield, in particular, fast transient performance. We develop a simple analytic center based algorithm, which updates estimates with a constant number of computation (independent of number of samples). The convergence analysis shows that the asymptotic performance of this algorithm matches that of the well-known least squares filter (provided some mild conditions are satisfied). Some numerical simulations are provided to demonstrate the fast transient performance of the interior point algorithm.