Renegar's Condition Number and Compressed Sensing Performance
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Alexandre d'Aspremont | Vincent Roulet | Vincent Roulet | Nicolas Boumal | Nicolas Boumal | A. d’Aspremont
[1] Bamdev Mishra,et al. Manopt, a matlab toolbox for optimization on manifolds , 2013, J. Mach. Learn. Res..
[2] James Renegar,et al. Incorporating Condition Measures into the Complexity Theory of Linear Programming , 1995, SIAM J. Optim..
[3] J. Renegar. Some perturbation theory for linear programming , 1994, Math. Program..
[4] R. DeVore,et al. Compressed sensing and best k-term approximation , 2008 .
[5] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[6] Yurii Nesterov,et al. Smooth minimization of non-smooth functions , 2005, Math. Program..
[7] Pablo A. Parrilo,et al. The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.
[8] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[9] James Renegar,et al. A mathematical view of interior-point methods in convex optimization , 2001, MPS-SIAM series on optimization.
[10] Santosh S. Vempala,et al. An Efficient Re-Scaled Perceptron Algorithm for Conic Systems , 2006, Math. Oper. Res..
[11] Emmanuel J. Candès,et al. Templates for convex cone problems with applications to sparse signal recovery , 2010, Math. Program. Comput..
[12] Robert M. Freund,et al. A geometric analysis of Renegar’s condition number, and its interplay with conic curvature , 2007, Math. Program..
[13] Andrea Montanari,et al. Cone-Constrained Principal Component Analysis , 2014, NIPS.
[14] V. Temlyakov,et al. A remark on Compressed Sensing , 2007 .
[15] D. Donoho,et al. Sparse nonnegative solution of underdetermined linear equations by linear programming. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[16] Javier Peña,et al. Understanding the Geometry of Infeasible Perturbations of a Conic Linear System , 1999, SIAM J. Optim..
[17] Joel A. Tropp,et al. Living on the edge: phase transitions in convex programs with random data , 2013, 1303.6672.
[18] Stephen P. Boyd,et al. Monotonicity and restart in fast gradient methods , 2014, 53rd IEEE Conference on Decision and Control.
[19] James Renegar,et al. Linear programming, complexity theory and elementary functional analysis , 1995, Math. Program..
[20] Stephen P. Boyd,et al. A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights , 2014, J. Mach. Learn. Res..
[21] Robert M. Freund,et al. Condition-Based Complexity of Convex Optimization in Conic Linear Form via the Ellipsoid Algorithm , 1999, SIAM J. Optim..
[22] Javier Peña,et al. A primal-dual symmetric relaxation for homogeneous conic systems , 2007, J. Complex..
[23] Justin K. Romberg,et al. Sparse Recovery of Streaming Signals Using $\ell_1$-Homotopy , 2013, IEEE Transactions on Signal Processing.
[24] Michael I. Jordan,et al. Computational and statistical tradeoffs via convex relaxation , 2012, Proceedings of the National Academy of Sciences.
[25] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[26] Robert M. Freund,et al. On the Complexity of Computing Estimates of Condition Measures of a Conic Linear System , 2003, Math. Oper. Res..
[27] Arkadi Nemirovski,et al. On unified view of nullspace-type conditions for recoveries associated with general sparsity structures , 2012, ArXiv.
[28] Yaakov Tsaig,et al. Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.
[29] Pradeep Ravikumar,et al. Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings , 2014, NIPS.
[30] Robert M. Freund,et al. Condition number complexity of an elementary algorithm for computing a reliable solution of a conic linear system , 2000, Math. Program..
[31] Robert M. Freund,et al. Some characterizations and properties of the “distance to ill-posedness” and the condition measure of a conic linear system , 1999, Math. Program..
[32] Fernando Ordóñez,et al. Computational Experience and the Explanatory Value of Condition Measures for Linear Optimization , 2003, SIAM J. Optim..
[33] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[34] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[35] Yurii Nesterov,et al. Generalized Power Method for Sparse Principal Component Analysis , 2008, J. Mach. Learn. Res..
[36] Dennis Amelunxen,et al. Gordon's inequality and condition numbers in conic optimization , 2014, 1408.3016.