By analyzing the constitution of the optimal adaptive weight

we find that the optimal adaptive weight only lies in a low-dimension subspace spanned by the desired signal steering vector and the interferences subspace.Generally

the number of interferences designed to suppress is much smaller than that of the array sensors.Consequently

once the interference-plus-signal subspace (IPSS) is estimated

only a low-dimension combination vector is needed to compute

which leads to a reduction of the computation complexity.First

we construct a complete IPSS.And then the sparse constraint is imposed on the combination vector to select the least number of column vectors of the complete IPSS to form the adaptive weight.Simulation results validate the effectiveness and robustness of the proposed algorithm.