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2013 IEEE 54th Annual Symposium on Foundations of Computer Science (2013)
Berkeley, CA USA
Oct. 26, 2013 to Oct. 29, 2013
ISSN: 0272-5428
pp: 147-156
Yin Tat Lee , Math. Dept., Massachusetts Inst. of Technol., Cambridge, MA, USA
Aaron Sidford , Electr. Eng. & Comput. Sci. Dept., Massachusetts Inst. of Technol., Cambridge, MA, USA
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. In particular, we show how to generalize and efficiently implement a method proposed by Nesterov, giving faster asymptotic running times for various algorithms that use standard coordinate descent as a black box. In addition to providing a proof of convergence for this new general method, we show that it is numerically stable, efficiently implementable, and in certain regimes, asymptotically optimal. To highlight the power of this algorithm, we show how it can used to create faster linear system solvers in several regimes: - We show how this method achieves a faster asymptotic runtime than conjugate gradient for solving a broad class of symmetric positive definite systems of equations. - We improve the convergence guarantees for Kaczmarz methods, a popular technique for image reconstruction and solving over determined systems of equations, by accelerating an algorithm of Strohmer and Vershynin. - We achieve the best known running time for solving Symmetric Diagonally Dominant (SDD) system of equations in the unit-cost RAM model, obtaining a running time of O(m log3/2n (log log n)1/2 log((log n)/eps)) by accelerating a recent solver by Kelner et al. Beyond the independent interest of these solvers, we believe they highlight the versatility of the approach of this paper and we hope that they will open the door for further algorithmic improvements in the future.
Acceleration, Convergence, Iterative methods, Probabilistic logic, Linear systems, Algorithm design and analysis, Standards

Yin Tat Lee and A. Sidford, "Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems," 2013 IEEE 54th Annual Symposium on Foundations of Computer Science(FOCS), Berkeley, CA USA, 2014, pp. 147-156.
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