High-order evaluation complexity for convexly-constrained optimization with non-Lipschitzian group sparsity terms
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[1] Boris Polyak,et al. Accuracy guaranties for ` 1 recovery of block-sparse signals , 2012 .
[2] Philippe L. Toint,et al. WORST-CASE EVALUATION COMPLEXITY AND OPTIMALITY OF SECOND-ORDER METHODS FOR NONCONVEX SMOOTH OPTIMIZATION , 2017, Proceedings of the International Congress of Mathematicians (ICM 2018).
[3] Guoan Bi,et al. The Group Lasso for Stable Recovery of Block-Sparse Signal Representations , 2011, IEEE Transactions on Signal Processing.
[4] Xiaojun Chen,et al. Optimality Conditions and a Smoothing Trust Region Newton Method for NonLipschitz Optimization , 2013, SIAM J. Optim..
[5] Cun-Hui Zhang,et al. A group bridge approach for variable selection , 2009, Biometrika.
[6] Y. Ye,et al. Lower Bound Theory of Nonzero Entries in Solutions of ℓ2-ℓp Minimization , 2010, SIAM J. Sci. Comput..
[7] Nicholas I. M. Gould,et al. Trust Region Methods , 2000, MOS-SIAM Series on Optimization.
[8] Andreas Griewank,et al. On the unconstrained optimization of partially separable functions , 1982 .
[9] Donald Goldfarb,et al. Partial-Update Newton Methods for Unary, Factorable, and Partially Separable Optimization , 1993, SIAM J. Optim..
[10] Boris Polyak,et al. Accuracy guaranties for $\ell_1$ recovery of block-sparse signals , 2011, 1111.2546.
[11] P. Toint,et al. Lancelot: A FORTRAN Package for Large-Scale Nonlinear Optimization (Release A) , 1992 .
[12] Yu Guang Wang,et al. Isotropic sparse regularization for spherical harmonic representations of random fields on the sphere , 2020 .
[13] Xiaojun Chen,et al. Worst-Case Complexity of Smoothing Quadratic Regularization Methods for Non-Lipschitzian Optimization , 2013, SIAM J. Optim..
[14] P. Toint,et al. Global convergence of a class of trust region algorithms for optimization with simple bounds , 1988 .
[15] Yongdai Kim,et al. Sparse optimization for nonconvex group penalized estimation , 2016 .
[16] Xiaojun Chen,et al. Complexity analysis of interior point algorithms for non-Lipschitz and nonconvex minimization , 2015, Math. Program..
[17] Yoram Bresler,et al. Subspace Methods for Joint Sparse Recovery , 2010, IEEE Transactions on Information Theory.
[18] P. Toint,et al. An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity , 2012 .
[19] M. Vidyasagar,et al. Error Bounds for Compressed Sensing Algorithms With Group Sparsity: A Unified Approach , 2015, 1512.08673.
[20] Nicholas I. M. Gould,et al. FILTRANE, a Fortran 95 filter-trust-region package for solving nonlinear least-squares and nonlinear feasibility problems , 2007, TOMS.
[21] Nadav Hallak,et al. Optimization problems involving group sparsity terms , 2019, Math. Program..
[22] N. Deng,et al. Modified Partial-Update Newton-Type Algorithms for Unary Optimization , 1998 .
[23] S. Bellavia,et al. Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization , 2018, SIAM J. Optim..
[24] Xiaojun Chen,et al. Spherical Designs and Nonconvex Minimization for Recovery of Sparse Signals on the Sphere , 2018, SIAM J. Imaging Sci..
[25] D. Gay. Automatically Finding and Exploiting Partially Separable Structure in Nonlinear Programming Problems , 1996 .
[26] Brian W. Kernighan,et al. AMPL: a mathematical programming language , 1989 .
[27] Xiaojun Chen,et al. Complexity of unconstrained $$L_2-L_p$$ minimization , 2011, Math. Program..
[28] Stefania Bellavia,et al. Deterministic and stochastic inexact regularization algorithms for nonconvex optimization with optimal complexity , 2018, ArXiv.
[29] Nicholas I. M. Gould,et al. Second-Order Optimality and Beyond: Characterization and Evaluation Complexity in Convexly Constrained Nonlinear Optimization , 2018, Found. Comput. Math..
[30] Murray Hill. Automatically Finding and Exploiting Partially Separable Structure in Nonlinear Programming Problems , 1996 .
[31] Yu Guang Wang,et al. Sparse Isotropic Regularization for Spherical Harmonic Representations of Random Fields on the Sphere , 2018, 1801.03212.
[32] Junzhou Huang,et al. The Benefit of Group Sparsity , 2009 .
[33] Michael I. Jordan,et al. Union support recovery in high-dimensional multivariate regression , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.
[34] Yonina C. Eldar,et al. Block-Sparse Signals: Uncertainty Relations and Efficient Recovery , 2009, IEEE Transactions on Signal Processing.
[35] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[36] Nicholas I. M. Gould,et al. Sharp worst-case evaluation complexity bounds for arbitrary-order nonconvex optimization with inexpensive constraints , 2018, SIAM J. Optim..
[37] José Mario Martínez,et al. Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models , 2017, Math. Program..
[38] Jian Huang,et al. Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors , 2012, Statistics and Computing.
[39] Xiaojun Chen,et al. Complexity of Partially Separable Convexly Constrained Optimization with Non-Lipschitzian Singularities , 2019, SIAM J. Optim..
[40] Nicholas I. M. Gould,et al. CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization , 2013, Computational Optimization and Applications.
[41] Peter Richtárik,et al. Distributed Block Coordinate Descent for Minimizing Partially Separable Functions , 2014, 1406.0238.
[42] Nicholas I. M. Gould,et al. Convergence Properties of Minimization Algorithms for Convex Constraints Using a Structured Trust Region , 1996, SIAM J. Optim..
[43] Jian Huang,et al. A Concave Pairwise Fusion Approach to Subgroup Analysis , 2015, 1508.07045.
[44] Volkan Cevher,et al. Group-Sparse Model Selection: Hardness and Relaxations , 2013, IEEE Transactions on Information Theory.