The adaptive multiple input/output inverse theorem (A-MINT) algorithm has been applied to equalize acoustic channels. To further increase its convergence rate, we suppress any undesirable non-zero coefficients in the estimated Kronecker delta function at each iteration. We achieve this by introducing the concept of sparseness measure of the estimated Kronecker delta function and using this as an additional constraint to A-MINT. Simulation and experimental results illustrate that the proposed algorithm can achieve faster convergence than A-MINT.