Geometric median in nearly linear time
暂无分享,去创建一个
Jakub W. Pachocki | Gary L. Miller | Yin Tat Lee | Michael B. Cohen | Aaron Sidford | Michael B. Cohen | Aaron Sidford | G. Miller | Y. Lee | J. Pachocki
[1] J. Jewkes,et al. Theory of Location of Industries. , 1933 .
[2] Harold W. Kuhn,et al. A note on Fermat's problem , 1973, Math. Program..
[3] Lawrence M. Ostresh. On the Convergence of a Class of Iterative Methods for Solving the Weber Location Problem , 1978, Oper. Res..
[4] Richard A. Kronmal,et al. The alias and alias-rejection-mixture methods for generating random variables from probability distributions , 1979, WSC '79.
[5] L. Cooper,et al. The Weber problem revisited , 1981 .
[6] E. Balas,et al. A Note on the Weiszfeld-Kuhn Algorithm for the General Fermat Problem. , 1982 .
[7] Chandrajit L. Bajaj,et al. The algebraic degree of geometric optimization problems , 1988, Discret. Comput. Geom..
[8] James Renegar,et al. A polynomial-time algorithm, based on Newton's method, for linear programming , 1988, Math. Program..
[9] Arie Tamir,et al. Open questions concerning Weiszfeld's algorithm for the Fermat-Weber location problem , 1989, Math. Program..
[10] M. Shirosaki. Another proof of the defect relation for moving targets , 1991 .
[11] P. Rousseeuw,et al. Breakdown Points of Affine Equivariant Estimators of Multivariate Location and Covariance Matrices , 1991 .
[12] Clóvis C. Gonzaga,et al. Path-Following Methods for Linear Programming , 1992, SIAM Rev..
[13] Yurii Nesterov,et al. Interior-point polynomial algorithms in convex programming , 1994, Siam studies in applied mathematics.
[14] Jakob Krarup,et al. On Torricelli's geometrical solution to a problem of Fermat , 1997 .
[15] Yinyu Ye,et al. An Efficient Algorithm for Minimizing a Sum of Euclidean Norms with Applications , 1997, SIAM J. Optim..
[16] Yinyu Ye,et al. Interior point algorithms: theory and analysis , 1997 .
[17] R. Motwani,et al. High-Dimensional Computational Geometry , 2000 .
[18] Cun-Hui Zhang,et al. The multivariate L1-median and associated data depth. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[19] Pablo A. Parrilo,et al. Minimizing Polynomial Functions , 2001, Algorithmic and Quantitative Aspects of Real Algebraic Geometry in Mathematics and Computer Science.
[20] Piotr Indyk,et al. Approximate clustering via core-sets , 2002, STOC '02.
[21] Zvi Drezner,et al. The Weber Problem , 2002 .
[22] P. Bose,et al. Fast approximations for sums of distances, clustering and the Fermat-Weber problem , 2003, Comput. Geom..
[23] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[24] Sariel Har-Peled,et al. Smaller Coresets for k-Median and k-Means Clustering , 2005, SCG.
[25] F. Plastria,et al. On the convergence of the Weiszfeld algorithm for continuous single facility location–allocation problems , 2008 .
[26] Michael Langberg,et al. A unified framework for approximating and clustering data , 2011, STOC '11.
[27] Gary L. Miller,et al. Runtime guarantees for regression problems , 2011, ITCS '13.
[28] Aleksander Madry,et al. Navigating Central Path with Electrical Flows: From Flows to Matchings, and Back , 2013, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.
[29] Sébastien Bubeck,et al. Theory of Convex Optimization for Machine Learning , 2014, ArXiv.
[30] Yin Tat Lee,et al. Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow , 2014, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.
[31] Sébastien Bubeck,et al. Convex Optimization: Algorithms and Complexity , 2014, Found. Trends Mach. Learn..
[32] Alexandr Andoni,et al. High-Dimensional Computational Geometry , 2016, Handbook of Big Data.