  This paper presents fast first-order methods for solving linear programs (LP)
approximately. We adapt online linear programming algorithms to offline LPs and
obtain algorithms that avoid any matrix multiplication. We also introduce a
variable-duplication technique that copies each variable $K$ times and reduces
optimality gap and constraint violation by a factor of $\sqrt{K}$. Furthermore,
we show how online algorithms can be effectively integrated into sifting, a
column generation scheme for large-scale LPs. Numerical experiments demonstrate
that our methods can serve as either an approximate direct solver, or an
initialization subroutine for exact LP solving.
