Sequential quadratic programming (SQP) algorithms are widely recognized to be among the most successful algorithms for nonconvex optimization. This paper attempts to develop an SQP-based method for frequency-response-masking (FRM) filters. We explain how the complementarity conditions in the SQP algorithm help reduce the amount of computation required to update the Lagrange multipliers in a significant manner. Simulation results are presented to demonstrate the algorithm's performance that compares favorably with several existing design methods.
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