Two important topics in equalizer design are its complexity and its training. We present a family of blind equalizers which, by incorporating a decomposition finite-impulse response filtering technique, can reduce the complexity of the convolution operation therein by about one half. The prototype algorithm in this equalizer family employs the prevalent Godard cost function. Several simplified algorithms are proposed, including a sign algorithm which eliminates multiplications in coefficient adaptation and a few delayed versions. We also study the convergence properties of the algorithms. For the prototype algorithm, we show that, in the limit of an infinitely long equalizer and under mild conditions on signal constellations and channel characteristics, there are only two sets of local minima on the performance surface. One of the sets is undesirable and is characterized by a null equalized channel response. The other corresponds to perfect equalization, which can be reached with proper equalizer initialization. For the simplified algorithms, corresponding cost functions may not exist. Some understanding of their convergence behaviors are obtained via examination of their adaptation equations. Simulation results are presented to demonstrate the performance of the algorithms.