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We study the problem of solving linear program in the streaming model. Givena 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 pointmethod that optimizes solely on the dual program. To this end, we obtainefficient algorithms for various different problems:  * For general linear programs, we can solve them in $\widetilde O(\sqrtn\log(1/\epsilon))$ passes and $\widetilde O(n^2)$ space for an$\epsilon$-approximate solution. To the best of our knowledge, this is the mostefficient LP solver in streaming with no polynomial dependence on $m$ for bothspace and passes.  * For bipartite graphs, we can solve the minimum vertex cover and maximumweight matching problem in $\widetilde O(\sqrt{m})$ passes and $\widetildeO(n)$ space.  In addition to our space-efficient IPM, we also give algorithms for solvingSDD systems and isolation lemma in $\widetilde O(n)$ spaces, which are thecornerstones for our graph results.

Space-Efficient Interior Point Method, with applications to Linear Programming and Maximum Weight Bipartite Matching