Generation of binary vectors that optimize a given weight function with application to soft-decision decoding

Many decoding algorithms need to compute some lists of binary vectors that minimize a given weight function. Furthermore, it is often desirable that these vectors are generated by increasing weight. The considered weight function is usually decreasing in the a priori likelihood that the vector yields correct decoding. We present a new technique to generate candidates for error patterns from the most a priori likely to the least, that proves significantly more efficient than any other known method.