Fetching the paper…
Reading the bibliography…
Sharpness is an almost generic assumption in continuous optimization that bounds the distance from minima by objective function suboptimality.
On approximate solutions of systems of linear inequalities
A. J. Hoffman · 1952
Earlier work this paper cites.
Une propriété topologique des sous-ensembles analytiques réels
S. Lojasiewicz · 1963
Earlier work this paper cites.
An application of error bounds for convex programming in a linear space
S. M. Robinson · 1975
Earlier work this paper cites.
Problem complexity and method efficiency in optimization
A. S. Nemirovskij and D. B. Yudin · 1983
Earlier work this paper cites.
A method for solving the convex programming problem with convergence rate O ( 1 / k 2 ) O(1/k^{2})
Y. E. Nesterov · 1983
Earlier work this paper cites.
A condition number for differentiable convex inequalities
O. L. Mangasarian · 1985
Earlier work this paper cites.
Optimal methods of smooth convex minimization
A. S. Nemirovskii and Y. E. Nesterov · 1985
Earlier work this paper cites.
Global regularity theorems
A. Auslender and J.-P. Crouzeix · 1988
Earlier work this paper cites.
Weak sharp minima in mathematical programming
J. V. Burke and M. C. Ferris · 1993
Earlier work this paper cites.
Weak sharp minima revisited Part I: basic theory
J. Burke and S. Deng · 2002
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course
Y. Nesterov · 2003
Earlier work this paper cites.
Smooth minimization of non-smooth functions
Y. Nesterov · 2005
Earlier work this paper cites.
The łojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems
J. Bolte, A. Daniilidis, and A. Lewis · 2007
Earlier work this paper cites.
Graph implementations for nonsmooth convex programs
M. Grant and S. Boyd · 2008
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
Earlier work this paper cites.
Wine Quality
P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis · 2009
Earlier work this paper cites.
An algorithm for minimizing the Mumford–Shah functional
T. Pock, D. Cremers, H. Bischof, and A. Chambolle · 2009
Earlier work this paper cites.
Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the Kurdyka-łojasiewicz inequality
H. Attouch, J. Bolte, P. Redont, and A. Soubeyran · 2010
Earlier work this paper cites.
A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science
E. Esser, X. Zhang, and T. F. Chan · 2010
Earlier work this paper cites.
NESTA: A fast and accurate first-order method for sparse recovery
S. Becker, J. Bobin, and E. J. Candès · 2011
Earlier work this paper cites.
Templates for convex cone problems with applications to sparse signal recovery
S. Becker, E. J. Candès, and M. C. Grant · 2011
Earlier work this paper cites.
Square-root LASSO: pivotal recovery of sparse signals via conic programming
A. Belloni, V. Chernozhukov, and L. Wang · 2011
Earlier work this paper cites.
A first-order primal-dual algorithm for convex problems with applications to imaging
A. Chambolle and T. Pock · 2011
Earlier work this paper cites.
Libsvm: a library for support vector machines
C.-C. Chang and C.-J. Lin · 2011
Earlier work this paper cites.
Realistic analytical phantoms for parallel Magnetic Resonance Imaging
M. Guerquin-Kern, L. Lejeune, K. P. Pruessmann, and M. Unser · 2012
Earlier work this paper cites.
A mathematical introduction to compressive sensing
S. Foucart and H. Rauhut · 2013
Cited alongside, same era.
Gradient methods for minimizing composite functions
Y. Nesterov · 2013
Cited alongside, same era.
Pivotal estimation via square-root LASSO in nonparametric regression
A. Belloni, V. Chernozhukov, and L. Wang · 2014
Cited alongside, same era.
Proximal alternating linearized minimization for nonconvex and nonsmooth problems
J. Bolte, S. Sabach, and M. Teboulle · 2014
Cited alongside, same era.
Monotonicity and restart in fast gradient methods
P. Giselsson and S. Boyd · 2014
Cited alongside, same era.
CVX: Matlab software for disciplined convex programming, version 2.1
M. Grant and S. Boyd · 2014
Cited alongside, same era.
Lectures on convex optimization
Y. Nesterov et al · 2018
Later among the works it cites.
Adaptive restart of accelerated gradient methods under local quadratic growth condition
O. Fercoq and Z. Qu · 2019
Later among the works it cites.
Randomized primal–dual proximal block coordinate updates
X. Gao, Y.-Y. Xu, and S.-Z. Zhang · 2019
Later among the works it cites.
Restarting Frank-Wolfe
T. Kerdreux, A. d’Aspremont, and S. Pokutta · 2019
Later among the works it cites.
Linear convergence of first order methods for non-strongly convex optimization
I. Necoara, Y. Nesterov, and F. Glineur · 2019
Later among the works it cites.
Accelerated first-order methods for hyperbolic programming
J. Renegar · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Iouditski and Y. Nesterov · 2014
Cited alongside, same era.
An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization
Q. Lin and L. Xiao · 2014
Cited alongside, same era.
A differential equation for modeling Nesterov’s accelerated gradient method: theory and insights
W. Su, S. Boyd, and E. Candes · 2014
Cited alongside, same era.
Splitting methods with variable metric for Kurdyka–łojasiewicz functions and general convergence rates
P. Frankel, G. Garrigos, and J. Peypouquet · 2015
Cited alongside, same era.
Universal gradient methods for convex optimization problems
Y. Nesterov · 2015
Cited alongside, same era.
Adaptive restart for accelerated gradient schemes
B. O’donoghue and E. Candes · 2015
Cited alongside, same era.
Computational complexity versus statistical performance on sparse recovery problems
V. Roulet, N. Boumal, and A. d’Aspremont · 2020
Later among the works it cites.
Sharpness, restart, and acceleration
V. Roulet and A. d’Aspremont · 2020
Later among the works it cites.
Lower bounds and primal-dual methods for affinely constrained convex optimization under metric subregularity, 2020
O. Rynkiewicz · 2020
Later among the works it cites.
Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging
B. Adcock, N. Dexter, and Q. Xu · 2021
Later among the works it cites.
Compressive Imaging: Structure, Sampling, Learning
B. Adcock and A. Hansen · 2021
Later among the works it cites.
The extended Smale’s 9th problem
A. Bastounis, A. C. Hansen, and V. Vlačić · 2021
Later among the works it cites.
Lectures on modern convex optimization
A. Ben-Tal and A. Nemirovski · 2021
Later among the works it cites.
Acceleration methods
A. d’Aspremont, D. Scieur, A. Taylor, et al · 2021
Later among the works it cites.
A simple nearly optimal restart scheme for speeding up first-order methods
J. Renegar and B. Grimmer · 2021
Later among the works it cites.
B. Adcock, S. Brugiapaglia, N. Dexter, and S. Moraga · 2022
Later among the works it cites.
Sparse Polynomial Approximation of High-Dimensional Functions
B. Adcock, S. Brugiapaglia, and C. G. Webster · 2022
Later among the works it cites.
Faster first-order primal-dual methods for linear programming using restarts and sharpness
D. Applegate, O. Hinder, H. Lu, and M. Lubin · 2022
Later among the works it cites.
WARPd: A linearly convergent first-order primal-dual algorithm for inverse problems with approximate sharpness conditions
M. J. Colbrook · 2022
Later among the works it cites.
The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and smale’s 18th problem
M. J. Colbrook, V. Antun, and A. C. Hansen · 2022
Later among the works it cites.
Quadratic error bound of the smoothed gap and the restarted averaged primal-dual hybrid gradient
O. Fercoq · 2022
Later among the works it cites.
Restarting frank–wolfe: Faster rates under hölderian error bounds
T. Kerdreux, A. d’Aspremont, and S. Pokutta · 2022
Later among the works it cites.
NESTANets: Stable, accurate and efficient neural networks for analysis-sparse inverse problems
M. Neyra-Nesterenko and B. Adcock · 2022
Later among the works it cites.
Unrolled NESTA: constructing stable, accurate and efficient neural networks for gradient-sparse imaging problems
M. Neyra-Nesterenko · 2023
Closest in time.