Adaptive filters play an important role in the fields related to digital signal processing and communication‎,‎ such as system identification‎,‎ noise cancellation‎,‎ channel equalization‎,‎ and beamforming‎.‎ In practical applications‎,‎ the computational complexity of an adaptive filter is an important consideration‎.‎ The Least Mean Square ‎(‎LMS‎)‎ algorithm is widely used because of its low computational complexity ‎(‎$O‎(‎N‎)‎$‎)‎ and simplicity in implementation‎.‎ The least squares algorithms‎,‎ such as Recursive Least Squares ‎(‎RLS‎)‎‎,‎ Conjugate Gradient ‎(‎CG‎)‎‎,‎ and Euclidean Direction Search ‎(‎EDS‎)‎‎,‎ can converge faster and have lower steady‎-state mean square error ‎(‎MSE‎)‎ than LMS‎.‎ However‎,‎ their high computational complexity ‎(‎$O‎(‎N^2‎)‎$‎)‎ makes them unsuitable for many real‎-time applications‎.‎ A well‎-known approach to controlling computational complexity is applying partial update ‎(‎PU‎)‎ method to adaptive filters‎.‎ A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the‎.‎‎.‎‎.‎