  In view of the KS-tensor complementarity problem, the sparse solution of this
problem is studied. Due to the nonconvexity and noncontinuity of the l_0-norm,
it is a NP hard problem to find the sparse solution of the KS-tensor
complementarity problem. In order to solve this problem, we transform it into a
polynomial programming problem with constraints. Then we use the sequential
quadratic programming (SQP) algorithm to solve this transformed problem.
Numerical results show that the SQP algorithm can find the sparse solutions of
the KS-tensor complementarity problem effectively.
