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Olivier Fercoq(olivier.fercoqed.ac.uk) Abstract: We study the performance of a family of randomized parallel coordinate descent methods for minimizing the sum of a nonsmooth and separable convex functions. The problem class includes as a special case L1regularized L1 regression and the minimization of the exponential loss (``AdaBoost problem''). We assume the input data defining the loss function is contained in a sparse $m \times n$ matrix $A$ with at most $\omega$ nonzeros in each row. Our methods need $O(n \beta / \tau)$ iterations to find an approximate solution with high probability, where $\tau$ is the number of processors and $\beta = 1 + (\omega1)(\tau1)/(n1)$ for the fastest variant. The notation hides dependence on quantities such as the required accuracy and confidence levels and the distance of the starting iterate from an optimal point. Since $\beta / \tau$ is a decreasing function of $\tau$, the method needs fewer iterations when more processors are used. Certain variants of our algorithms perform on average only $O(\nnz(A) / n)$ arithmetic operations during a single iteration per processor and, because $\beta$ decreases when $\omega$ does, fewer iterations are needed for sparser problems. Keywords: parallel coordinate descent, parallel adaboost, convex optimization, big data, separability Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Category 2: Optimization Software and Modeling Systems (Parallel Algorithms ) Citation: Download: [PDF] Entry Submitted: 09/23/2013 Modify/Update this entry  
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