  The Interior-Point Methods are a class for solving linear programming
problems that rely upon the solution of linear systems. At each iteration, it
becomes important to determine how to solve these linear systems when the
constraint matrix of the linear programming problem includes dense columns. In
this paper, we propose a preconditioner to handle linear programming problems
with dense columns, and we prove theoretically that the final linear system to
solve is uniformly bounded when the Interior-Point Method is converging to an
optimal solution. This result is illustrated through computational experiments,
which show that our proposed method is robust and competitive in terms of
running time and/or number of iterations compared with existing methods.
