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We study the problem of minimizing the sum of three convex functions: a differentiable, twice-differentiable and a non-smooth term in a high dimensional setting.
Local convex conjugacy and Fenchel duality
Bertsekas, D. P · 1978
Earlier work this paper cites.
The modification of Newton’s method for unconstrained optimization by bounding cubic terms
Griewank, A · 1981
Earlier work this paper cites.
Convex analysis. Princeton landmarks in mathematics, 1997
Rockafellar, R. T · 1997
Earlier work this paper cites.
Trust region methods
Conn, A. R., Gould, N. I., and Toint, P. L · 2000
Earlier work this paper cites.
Introductory lectures on convex optimization., 2004
Nesterov, Y · 2004
Earlier work this paper cites.
Cubic regularization of Newton’s method and its global performance
Nesterov, Y. and Polyak, B. T · 2006
Earlier work this paper cites.
Modified gauss–newton scheme with worst case guarantees for global performance
Nesterov, Y · 2007
Earlier work this paper cites.
Accelerating the cubic regularization of Newton’s method on convex problems
Nesterov, Y · 2008
Earlier work this paper cites.
A coordinate gradient descent method for nonsmooth separable minimization
Tseng, P. and Yun, S · 2009
Earlier work this paper cites.
On solving trust-region and other regularised subproblems in optimization
Gould, N. I., Robinson, D. P., and Thorne, H. S · 2010
Cited alongside, same era.
Gradient methods for minimizing composite functions
Nesterov, Y · 2013
Cited alongside, same era.
Stochastic dual coordinate ascent methods for regularized loss minimization
Shalev-Shwartz, S. and Zhang, T · 2013
Cited alongside, same era.
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Richtárik, P. and Takáč, M · 2014
Cited alongside, same era.
Randomized sketches of convex programs with sharp guarantees
Pilanci, M. and Wainwright, M. J · 2015
Cited alongside, same era.
Finding approximate local minima for nonconvex optimization in linear time
SDNA: stochastic dual Newton ascent for empirical risk minimization
Qu, Z., Richtárik, P., Takáč, M., and Fercoq, O · 2016
Later among the works it cites.
Parallel coordinate descent methods for big data optimization
Richtárik, P. and Takáč, M · 2016
Later among the works it cites.
Second-order methods with cubic regularization under inexact information
Ghadimi, S., Liu, H., and Zhang, T · 2017
Later among the works it cites.
Regularized Newton methods for minimizing functions with Hölder continuous Hessians
Grapiglia, G. N. and Nesterov, Y · 2017
Later among the works it cites.
Sub-sampled cubic regularization for non-convex optimization
Kohler, J. M. and Lucchi, A · 2017
Later among the works it cites.
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Agarwal, N., Allen-Zhu, Z., Bullins, B., Hazan, E., and Ma, T · 2016
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Gradient descent efficiently finds the cubic-regularized non-convex newton step
Carmon, Y. and Duchi, J. C · 2016
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Coordinate descent with arbitrary sampling II: expected separable overapproximation
Qu, Z. and Richtárik, P · 2016
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Adaptive cubic regularisation methods for unconstrained optimization. part I: motivation, convergence and numerical results
Cartis, C., Gould, N. I., and Toint, P. L
Cited in the paper.
Adaptive cubic regularisation methods for unconstrained optimization. part II: worst-case function-and derivative-evaluation complexity
Cartis, C., Gould, N. I., and Toint, P. L
Cited in the paper.
Tripuraneni, N., Stern, M., Jin, C., Regier, J., and Jordan, M. I · 2017
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Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
Cartis, C. and Scheinberg, K · 2018
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Parallel stochastic newton method
Mutnỳ, M. and Richtárik, P · 2018
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