Computation of the vertices of the convex hull of a set $S$ of $n$ points in $mathbb{R} ^m$ is a fundamental problem in computational geometry, optimization, machine learning and more. We present All Vertex Triangle Algorithm (AVTA), a robust and efficient algorithm for computing the subset $overline S$ of all $K$ vertices of $conv(S)$, the convex hull of $S$. If $Gamma_*$ is the minimum of the distances from each vertex to the convex hull of the remaining vertices, given any $gamma leq gamma_* = Gamma_*/R$, $R$ the diameter of $S$, $AVTA$ computes $overline S$ in $O(nK(m+ gamma^{-2}))$ operations. If $gamma_*$ is unknown but $K$ is known, AVTA computes $overline S$ in $O(nK(m+ gamma_*^{-2})) log(gamma_*^{-1})$ operations. More generally, given $t in (0,1)$, AVTA computes a subset $overline S^t$ of $overline S$ in $O(n |overline S^t|(m+ t^{-2}))$ operations, where the distance between any $p in conv(S)$ to $conv(overline S^t)$ is at most $t R$. Next we consider AVTA where input is $S_varepsilon$, an $varepsilon$ perturbation of $S$. Assuming a bound on $varepsilon$ in terms of the minimum of the distances of vertices of $conv(S)$ to the convex hull of the remaining point of $S$, we derive analogous complexity bounds for computing $overline S_varepsilon$. We also analyze AVTA under random projections of $S$ or $S_varepsilon$. Finally, via AVTA we design new practical algorithms for two popular machine learning problems: topic modeling and non-negative matrix factorization. For topic models AVTA leads to significantly better reconstruction of the topic-word matrix than state of the art approaches~cite{arora2013practical, bansal2014provable}. For non-negative matrix AVTA is competitive with existing methods~cite{arora2012computing}. Empirically AVTA is robust and can handle larger amounts of noise than existing methods.
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