Variable projection without smoothness
暂无分享,去创建一个
Dmitriy Drusvyatskiy | Aleksandr Y. Aravkin | Tristan van Leeuwen | T. Leeuwen | D. Drusvyatskiy | A. Aravkin
[1] George Biros,et al. Inexactness Issues in the Lagrange-Newton-Krylov-Schur Method for PDE-constrained Optimization , 2003 .
[2] Saburou Saitoh,et al. Theory of Reproducing Kernels and Its Applications , 1988 .
[3] Mark W. Schmidt,et al. Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization , 2011, NIPS.
[4] M. G. Delgado,et al. Optimal control and partial differential equations , 2004 .
[5] Dmitriy Drusvyatskiy,et al. Efficient Quadratic Penalization Through the Partial Minimization Technique , 2018, IEEE Transactions on Automatic Control.
[6] Sabine Van Huffel,et al. Overview of total least-squares methods , 2007, Signal Process..
[7] P. Rousseeuw. Least Median of Squares Regression , 1984 .
[8] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[9] James V. Burke,et al. Algorithmic Differentiation of Implicit Functions and Optimal Values , 2008 .
[10] Sabine Van Huffel,et al. Exponential Data Fitting and Its Applications , 2018 .
[11] Bernhard Schölkopf,et al. A Generalized Representer Theorem , 2001, COLT/EuroCOLT.
[12] G. Golub,et al. Separable nonlinear least squares: the variable projection method and its applications , 2003 .
[13] Gene H. Golub,et al. An analysis of the total least squares problem , 1980, Milestones in Matrix Computation.
[15] Miguel Á. Carreira-Perpiñán,et al. Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application , 2013, ArXiv.
[16] Yurii Nesterov,et al. Gradient methods for minimizing composite functions , 2012, Mathematical Programming.
[17] Yurii Nesterov,et al. First-order methods of smooth convex optimization with inexact oracle , 2013, Mathematical Programming.
[18] Dmitriy Drusvyatskiy,et al. Quadratic Penalization Through the Variable Projection Technique , 2016 .
[19] Aleksandr Y. Aravkin,et al. A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning , 2016, ArXiv.
[20] Gene H. Golub,et al. The differentiation of pseudo-inverses and non-linear least squares problems whose variables separate , 1972, Milestones in Matrix Computation.
[21] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[22] Aleksandr Y. Aravkin,et al. Sparse/robust estimation and Kalman smoothing with nonsmooth log-concave densities: modeling, computation, and theory , 2013, J. Mach. Learn. Res..
[23] A. Hielscher,et al. Optical tomography as a PDE-constrained optimization problem , 2005 .
[24] Laurent Condat. Fast projection onto the simplex and the l1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pmb {l}_\mathbf {1}$$\end{ , 2015, Mathematical Programming.
[25] R. Plessix. A review of the adjoint-state method for computing the gradient of a functional with geophysical applications , 2006 .
[26] F. Herrmann,et al. Robust estimation of primaries by sparse inversion via one-norm minimization , 2013 .
[27] R. O. Schmidt,et al. Multiple emitter location and signal Parameter estimation , 1986 .
[28] R. Koenker,et al. Regression Quantiles , 2007 .
[29] S. Hanasoge. FULL WAVEFORM INVERSION OF SOLAR INTERIOR FLOWS , 2014, 1410.1981.
[30] Dianne P. O'Leary,et al. Variable projection for nonlinear least squares problems , 2012, Computational Optimization and Applications.
[31] D. Ruppert,et al. Trimmed Least Squares Estimation in the Linear Model , 1980 .
[32] E. Haber,et al. On optimization techniques for solving nonlinear inverse problems , 2000 .
[33] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[34] Yves Grandvalet,et al. More efficiency in multiple kernel learning , 2007, ICML '07.
[35] 齋藤 三郎,et al. Theory of reproducing kernels and its applications , 1988 .
[36] Laurent Condat,et al. A Fast Projection onto the Simplex and the l 1 Ball , 2015 .
[37] Axel Ruhe,et al. Algorithms for separable nonlinear least squares problems , 1980 .
[38] Margaret Cheney,et al. The Linear Sampling Method and the MUSIC Algorithm , 2001 .
[39] D K Smith,et al. Numerical Optimization , 2001, J. Oper. Res. Soc..
[40] L. N. Vicente,et al. Trust-Region Interior-Point SQP Algorithms for a Class of Nonlinear Programming Problems , 1998 .
[41] Marc Teboulle,et al. Proximal alternating linearized minimization for nonconvex and nonsmooth problems , 2013, Mathematical Programming.
[42] E. Haber,et al. Blind deconvolution of seismograms regularized via minimum support , 2010 .
[43] T. Leeuwen,et al. A penalty method for PDE-constrained optimization in inverse problems , 2015, 1504.02249.
[44] Michael R. Osborne,et al. SEPARABLE LEAST SQUARES, VARIABLE PROJECTION, AND THE GAUSS-NEWTON ALGORITHM ∗ , 2007 .
[45] Aleksandr Y. Aravkin,et al. Estimating nuisance parameters in inverse problems , 2012, 1206.6532.
[46] Eunho Yang,et al. Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso , 2015, NIPS.
[47] A. Gilbert,et al. A generalization of variable elimination for separable inverse problems beyond least squares , 2013, 1302.0441.
[48] Adrian S. Lewis,et al. Convex Analysis And Nonlinear Optimization , 2000 .
[49] Alfred O. Hero,et al. Semi-Blind Sparse Image Reconstruction With Application to MRFM , 2012, IEEE Transactions on Image Processing.
[50] I.,et al. Optimal Control and Partial Differential Equations , 2004 .
[51] Mário A. T. Figueiredo,et al. Parameter Estimation for Blind and Non-Blind Deblurring Using Residual Whiteness Measures , 2013, IEEE Transactions on Image Processing.