RES: Regularized Stochastic BFGS Algorithm

M. J. D. Powell, Some global convergence properties of a variable metric algorithm for minimization without exact line search , 2nd ed. London, UK: Academic Press, 1971

1971

Earlier work this paper cites.

C. G. Broyden, J. E. D. Jr., Wang, and J. J. More, “On the local and superlinear convergence of quasi-newton methods,” IMA J. Appl. Math , vol. 12, no. 3, pp. 223–245, June 1973

1973

Earlier work this paper cites.

J. J. E. Dennis and J. J. More, “A characterization of super linear convergence and its application to quasi-newton methods,” Mathematics of computation , vol. 28, no. 126, pp. 549–560, 1974

1974

Earlier work this paper cites.

R. H. Byrd, J. Nocedal, and Y. Yuan, “Global convergence of a class of quasi-newton methods on convex problems,” SIAM J. Numer. Anal. , vol. 24, no. 5, pp. 1171–1190, October 1987

1987

Earlier work this paper cites.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in Proceedings of the fifth annual workshop on Computational learning theory , ACM, 1992

1992

Earlier work this paper cites.

J. R. Birge, X. Chen, L. Qi, and Z. Wei, “A stochastic newton method for stochastic quadratic programs with resource,” Technical report , University of Michigan, Ann Arbor, MI 1995

1995

Earlier work this paper cites.

V. Solo and X. Kong, Adaptive Signal Processing Algorithms: Stability and Performance . Englewood Cliffs: NJ: Prentice-Hall, 1995

1995

Earlier work this paper cites.

N. L. Johnson, S. Kotz, and N. Balakrishnan, Continuous Univariate Distributions, vol. 2 , 2nd ed. Wiley-Interscience, 1995

1995

Earlier work this paper cites.

J. Nocedal and S. J. Wright, Numerical optimization , 2nd ed. New York, NY: Springer-Verlag, 1999

1999

Earlier work this paper cites.

V. Vapnik, The nature of statistical learning theory , 2nd ed. springer, 1999

1999

Cited alongside, same era.

T. Zhang, “Solving large scale linear prediction problems using stochastic gradient descent algorithms,” In Proceedings of the twenty-first international conference on Machine learning , p. 919Ð926, ACM, 2004

2004

Cited alongside, same era.

S. Boyd and L. Vandenberghe, Convex Optimization , 1st ed. Cambridge, U.K: Cambridge Univ. Press, 2004

2004

Cited alongside, same era.

L. Bottou and Y. L. Cun, “On-line learning for very large datasets,” in Applied Stochastic Models in Business and Industry , vol. 21. pp. 137-151, 2005

2005

Cited alongside, same era.

S. Shalev-Shwartz, Y. Singer, and N. Srebro, “Pegasos: Primal estimated sub-gradient solver for svm,” In Proceedings of the 24th international conference on Machine learning , pp. 807–814, ACM, 2007

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” In Proceedings of COMPSTAT’2010 , pp. 177–186, Physica-Verlag HD, 2010

2010

Later among the works it cites.

A. Ribeiro, “Ergodic stochastic optimization algorithms for wireless communication and networking,” IEEE Trans. Signal Process.. , vol. 58, no. 12, pp. 6369–6386, December 2010

2010

Later among the works it cites.

——, “Optimal resource allocation in wireless communication and networking,” EURASIP J. Wireless commun. , vol. 2012, no. 272, pp. 3727–3741, August 2012

2012

Later among the works it cites.

N. LeRoux, M. Schmidt, and F. Bach, “A stochastic gradient method with an exponential convergence rate for strongly-convex optimization with finite training sets,” arXiv preprint arXiv , 1202.6258, 2012

2012

Later among the works it cites.