Fetching the paper…
Reading the bibliography…
The filtering-clustering models, including trend filtering and convex clustering, have become an important source of ideas and modeling tools in machine learning and related fields.
A simple method of trend construction
C. Leser · 1961
Earlier work this paper cites.
Two-metric projection methods for constrained optimization
E. M. Gafni and D. P. Bertsekas · 1984
Earlier work this paper cites.
A posteriori error bounds for the linearly-constrained variational inequality problem
J-S. Pang · 1987
Earlier work this paper cites.
Applications of a splitting algorithm to decomposition in convex programming and variational inequalities
P. Tseng · 1991
Earlier work this paper cites.
On the linear convergence of descent methods for convex essentially smooth minimization
Z-Q. Luo and P. Tseng · 1992
Earlier work this paper cites.
Nonlinear total variation based noise removal algorithms
L. I. Rudin, S. Osher, and E. Faterni · 1992
Earlier work this paper cites.
Error bounds and convergence analysis of feasible descent methods: a general approach
Z-Q. Luo and P. Tseng · 1993
Earlier work this paper cites.
On projection algorithms for solving convex feasibility problems
H. H. Bauschke and J. M. Borwein · 1996
Earlier work this paper cites.
Convergence rates in forward–backward splitting
G. H. G. Chen and R. T. Rockafellar · 1997
Earlier work this paper cites.
Error bounds in mathematical programming
J-S. Pang · 1997
Earlier work this paper cites.
Hoffman’s error bound, local controllability, and sensitivity analysis
A. Jourani · 2000
Earlier work this paper cites.
A modified forward-backward splitting method for maximal monotone mappings
P. Tseng · 2000
Earlier work this paper cites.
On the Barzilai-Borwein method
R. Fletcher · 2005
Earlier work this paper cites.
Sparsity and smoothness via the fused lasso
R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight · 2005
Earlier work this paper cites.
ℓ 1 \ell_{1} trend filtering
S-J. Kim, K. Koh, S. Boyd, and D. Gorinevsky · 2009
Earlier work this paper cites.
Approximation accuracy, gradient methods, and error bound for structured convex optimization
P. Tseng · 2010
Earlier work this paper cites.
Clusterpath: An algorithm for clustering using convex fusion penalties
T. Hocking, J.-P. Vert, F. Bach, and A. Joulin · 2011
Earlier work this paper cites.
Convergence rates of inexact proximal-gradient methods for convex optimization
M. Schmidt, N. L. Roux, and F. Bach · 2011
Earlier work this paper cites.
The solution path of the generalized lasso
R. J. Tibshirani and J. Taylor · 2011
Cited alongside, same era.
Fast global convergence of gradient methods for high-dimensional statistical recovery
A. Agarwal, S. Negahban, and M. J. Wainwright · 2012
Cited alongside, same era.
Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
S. Negahban and M. J. Wainwright · 2012
Cited alongside, same era.
A unified framework for high-dimensional analysis of m m -estimators with decomposable regularizers
S. Negahban, P. Ravikumar, M. J. Wainwright, and B. Yu · 2012
Cited alongside, same era.
Making gradient descent optimal for strongly convex stochastic optimization
A. Rakhlin, O. Shamir, and K. Sridharan · 2012
Cited alongside, same era.
Proximal algorithms
N. Parikh, S. Boyd, et al · 2014
A new algorithm and theory for penalized regression-based clustering
C. Wu, S. Kwon, X. Shen, and W. Pan · 2016
Later among the works it cites.
Katyusha: The first direct acceleration of stochastic gradient methods
Z. Allen-Zhu · 2017
Later among the works it cites.
Linearly convergent away-step conditional gradient for non-strongly convex functions
A. Beck and S. Shtern · 2017
Later among the works it cites.
On the linear convergence of the alternating direction method of multipliers
M. Hong and Z-Q. Luo · 2017
Later among the works it cites.
An optimal randomized incremental gradient method
G. Lan and Y. Zhou · 2017
Later among the works it cites.
A sharp error analysis for the fused Lasso, with application to approximate changepoint screening
K. Lin, J. L. Sharpnack, A. Rinaldo, and R. J. Tibshirani · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Adaptive piecewise polynomial estimation via trend filtering
R. J. Tibshirani · 2014
Cited alongside, same era.
Iteration complexity of feasible descent methods for convex optimization
P-W. Wang and C-J. Lin · 2014
Cited alongside, same era.
Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
Z. Wang, H. Liu, and T. Zhang · 2014
Cited alongside, same era.
Convex optimization procedure for clustering: Theoretical revisit
C. Zhu, H. Xu, C. Leng, and S. Yan · 2014
Cited alongside, same era.
Splitting methods for convex clustering
E. C. Chi and K. Lange · 2015
Cited alongside, same era.
Regularized m m -estimators with nonconvexity: Statistical and algorithmic theory for local optima
P-L. Loh and M. J. Wainwright · 2015
Cited alongside, same era.
Later among the works it cites.
Convex clustering via ℓ 1 \ell_{1} fusion penalization
P. Radchenko and G. Mukherjee · 2017
Later among the works it cites.
Exploiting strong convexity from data with primal-dual first-order algorithms
J. Wang and L. Xiao · 2017
Later among the works it cites.
Stochastic primal-dual coordinate method for regularized empirical risk minimization
Y. Zhang and L. Xiao · 2017
Later among the works it cites.
A unified approach to error bounds for structured convex optimization problems
Z. Zhou and A. M-C. So · 2017
Later among the works it cites.
Error bounds, quadratic growth, and linear convergence of proximal methods
D. Drusvyatskiy and A. S. Lewis · 2018
Later among the works it cites.
Lectures on Convex Optimization , volume 137
Y. Nesterov · 2018
Later among the works it cites.
An algorithm to compute the hoffman constant of a system of linear constraints
J. Pena, J. Vera, and L. Zuluaga · 2018
Later among the works it cites.
Adaptive risk bounds in univariate total variation denoising and trend filtering
A. Guntuboyina, D. Lieu, S. Chatterjee, and B. Sen · 2020
Closest in time.
First-order and Stochastic Optimization Methods for Machine Learning
G. Lan · 2020
Closest in time.
Variational analysis perspective on linear convergence of some first order methods for nonsmooth convex optimization problems
J. Y. Jane, X. Yuan, S. Zeng, and J. Zhang · 2021
Closest in time.
Convex clustering: Model, theoretical guarantee and efficient algorithm
D. Sun, K-C. Toh, and Y. Yuan · 2021
Closest in time.