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In this paper we show how to accelerate randomized coordinate descent methods and achieve faster convergence rates without paying per-iteration costs in asymptotic running time.
Angenäherte auflösung von systemen linearer gleichungen
Stefan Kaczmarz · 1937
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Nonlinear Programming: A Unified Approach
W Zangwill · 1969
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A method for solving a convex programming problem with convergence rate 1/kˆ2
Yu. Nesterov · 1983
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Iterative methods for optimization
Carl T Kelley · 1987
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On the convergence of the coordinate descent method for convex differentiable minimization
Z. Q. Luo and P. Tseng · 1992
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On projection algorithms for solving convex feasibility problems
Heinz H. Bauschke and Jonathan M. Borwein · 1996
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The mathematics of computerized tomography
Frank Natterer · 2001
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Introductory Lectures on Convex Optimization: A Basic Course
Yu Nesterov · 2003
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Solving fractional packing problems in oast(1/ε) iterations
D. Bienstock and G. Iyengar · 2004
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Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Combinatorial preconditioners and multilevel solvers for problems in computer vision and image processing
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Iteration Complexity of Randomized Block-Coordinate Descent Methods for Minimizing a Composite Function
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T Goldstein, BRENDAN OÕDonoghue, and Simon Setzer · 2012
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Efficiency of coordinate descent methods on huge-scale optimization problems
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