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COAP 2020 best paper prize

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References

  1. Boyd, S., Ghosh, A., Prabhakar, B., Shah, D.: Randomized gossip algorithms. IEEE Trans. Inf. Theory 14(SI), 2508–2530 (2006)

    Article  MathSciNet  Google Scholar 

  2. Can, B., Gurbuzbalaban, M., Zhu, L.: Accelerated linear convergence of stochastic momentum methods in wasserstein distances. In ICML, pages 891–901 (2019)

  3. Cauchy, A.: Méthode générale pour la résolution des systemes d’équations simultanées. Comp. Rend. Sci. Paris 25(1847), 536–538 (1847)

    Google Scholar 

  4. Ghadimi, E., Feyzmahdavian, H.R., Johansson, M.: Global convergence of the heavy-ball method for convex optimization. In ECC, pages 310–315. IEEE (2015)

  5. Gower, R.M., Richtárik, P.: Randomized iterative methods for linear systems. SIAM J. Matrix Anal. Appl. 36(4), 1660–1690 (2015)

    Article  MathSciNet  Google Scholar 

  6. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In NeurIPS, pages 1097–1105, (2012)

  7. Lessard, L., Recht, B., Packard, A.: Analysis and design of optimization algorithms via integral quadratic constraints. SIAM J. Optim. 26(1), 57–95 (2016)

    Article  MathSciNet  Google Scholar 

  8. Leventhal, D., Lewis, A.S.: Randomized methods for linear constraints: convergence rates and conditioning. Math. Oper. Res. 35(3), 641–654 (2010)

    Article  MathSciNet  Google Scholar 

  9. Liu, Y., Gao, Y., Yin, W.: An improved analysis of stochastic gradient descent with momentum. NeurIPS 33 (2020)

  10. Loizou, Nicolas, Richtárik, Peter: Momentum and stochastic momentum for stochastic gradient, newton, proximal point and subspace descent methods. Comput. Optim. Appl. 77(3), 653–710 (2020)

    Article  MathSciNet  Google Scholar 

  11. Loizou, N., Richtárik, P.: Revisiting randomized gossip algorithms: General framework, convergence rates and novel block and accelerated protocols. IEEE Trans. Inf. Theory (2021)

  12. Nesterov, Y.: Introductory Lectures on Convex Optimization: A Basic Course, vol. 87. Springer (2013)

  13. Ochs, P., Brox, T., Pock, T.: iPiasco: Inertial proximal algorithm for strongly convex optimization. J. Math. Imaging Vis. 53(2), 171–181 (2015)

    Article  MathSciNet  Google Scholar 

  14. Polyak, B.T.: Some methods of speeding up the convergence of iteration methods. USSR Comput. Math. Math. Phys. 4(5), 1–17 (1964)

    Article  Google Scholar 

  15. Polyak, B.T.: Introduction to optimization. translations series in mathematics and engineering. Optim. Softw. (1987)

  16. Richtárik, Peter, Takác, Martin: Stochastic reformulations of linear systems: algorithms and convergence theory. SIAM J. Matrix Anal. Appl. 41(2), 487–524 (2020)

    Article  MathSciNet  Google Scholar 

  17. Sebbouh, O., Gower, R.M., Defazio, A.: Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball. In COLT, pages 3935–3971. PMLR (2021)

  18. Strohmer, T., Vershynin, R.: A randomized Kaczmarz algorithm with exponential convergence. J. Fourier Anal. Appl. 15(2), 262–278 (2009)

    Article  MathSciNet  Google Scholar 

  19. Sutskever, I., Martens, J., Dahl, G.E., Hinton, G.E.: On the importance of initialization and momentum in deep learning. ICML 28, 1139–1147 (2013)

    Google Scholar 

  20. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In CVPR, pages 1–9 (2015)

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COAP 2020 best paper prize. Comput Optim Appl 80, 681–685 (2021). https://doi.org/10.1007/s10589-021-00327-x

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