  The so-called constrained least mean-square algorithm is one of the most
commonly used linear-equality-constrained adaptive filtering algorithms. Its
main advantages are adaptability and relative simplicity. In order to gain
analytical insights into the performance of this algorithm, we examine its
mean-square performance and derive theoretical expressions for its transient
and steady-state mean-square deviation. Our methodology is inspired by the
principle of energy conservation in adaptive filters. Simulation results
corroborate the accuracy of the derived formula.
