# Pegasos: primal estimated sub-gradient solver for SVM

@article{ShalevShwartz2007PegasosPE, title={Pegasos: primal estimated sub-gradient solver for SVM}, author={Shai Shalev-Shwartz and Yoram Singer and Nathan Srebro and Andrew Cotter}, journal={Mathematical Programming}, year={2007}, volume={127}, pages={3-30} }

We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy $${\epsilon}$$ is $${\tilde{O}(1 / \epsilon)}$$, where each iteration operates on a single training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require $${\Omega(1 / \epsilon^2)}$$ iterations. As in previously…

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