  We study the problem of solving linear program in the streaming model. Given
a constraint matrix $A\in \mathbb{R}^{m\times n}$ and vectors $b\in
\mathbb{R}^m, c\in \mathbb{R}^n$, we develop a space-efficient interior point
method that optimizes solely on the dual program. To this end, we obtain
efficient algorithms for various different problems:
  * For general linear programs, we can solve them in $\widetilde O(\sqrt
n\log(1/\epsilon))$ passes and $\widetilde O(n^2)$ space for an
$\epsilon$-approximate solution. To the best of our knowledge, this is the most
efficient LP solver in streaming with no polynomial dependence on $m$ for both
space and passes.
  * For bipartite graphs, we can solve the minimum vertex cover and maximum
weight matching problem in $\widetilde O(\sqrt{m})$ passes and $\widetilde
O(n)$ space.
  In addition to our space-efficient IPM, we also give algorithms for solving
SDD systems and isolation lemma in $\widetilde O(n)$ spaces, which are the
cornerstones for our graph results.
