  Based on smoothing techniques, we propose two new methods to solve linear
complementarity problems (LCP) called TLCP and Soft-Max. The idea of these two
new methods takes inspiration from interior-point methods in optimization. The
technique that we propose avoids any parameter management while ensuring good
theoretical convergence results. In our approach we do not need any complicated
strategy to update the smoothing parameter r since we will consider it as a new
variable. Our methods are validated by extensive numerical tests, in which we
compare our methods to several other classical methods.
