A fast convergence algorithm for L2-sensitivity minimization of 2-D separable-denominator state-space digital filters

This paper proposes a fast convergence algorithm for L-2-sensitivity minimization problem of two-dimensional (2-D) separable-denominator state-space digital filters subject to L-2-scaling constraints. The proposed algorithm reduces a constrained optimization problem to an unconstrained optimization problem by appropriate variable transformation, and minimizes the L-2-sensitivity by iterative calculation called successive substitution method. Our novel algorithm can achieve the L-2-sensitivity minimization with quite fast convergence behavior.