Convergence of a Regularized Euclidean Residual Algorithm for Nonlinear Least-Squares
نویسندگان
چکیده
منابع مشابه
Convergence of a Regularized Euclidean Residual Algorithm for Nonlinear Least-Squares
The convergence properties of the new Regularized Euclidean Residual method for solving general nonlinear least-squares and nonlinear equations problems are investigated. This method, derived from a proposal by Nesterov (2007), uses a model of the objective function consisting of the unsquared Euclidean linearized residual regularized by a quadratic term. At variance with previous analysis, its...
متن کاملOptimal Rates for Regularized Least-squares Algorithm
We develop a theoretical analysis of the generalization performances of regularized least-squares algorithm on a reproducing kernel Hilbert space in the supervised learning setting. The presented results hold in the general framework of vector-valued functions, therefore they can be applied to multi-task problems. In particular we observe that the concept of effective dimension plays a central ...
متن کاملFast Rates for Regularized Least-squares Algorithm
We develop a theoretical analysis of generalization performances of regularized leastsquares on reproducing kernel Hilbert spaces for supervised learning. We show that the concept of effective dimension of an integral operator plays a central role in the definition of a criterion for the choice of the regularization parameter as a function of the number of samples. In fact a minimax analysis is...
متن کاملA Regularized Total Least Squares Algorithm
Error-contaminated systems Ax ≈ b, for which A is ill-conditioned, are considered. Such systems may be solved using Tikhonov-like regularized total least squares (R-TLS) methods. Golub et al, 1999, presented a direct algorithm for the solution of the Lagrange multiplier formulation for the R-TLS problem. Here we present a parameter independent algorithm for the approximate R-TLS solution. The a...
متن کاملConvergence of Common Proximal Methods for L1-Regularized Least Squares
We compare the convergence behavior of ADMM (alternating direction method of multipliers), [F]ISTA ([fast] iterative shrinkage and thresholding algorithm) and CD (coordinate descent) methods on the model `1-regularized least squares problem (aka LASSO). We use an eigenanalysis of the operators to compare their local convergence rates when close to the solution. We find that, when applicable, CD...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Numerical Analysis
سال: 2010
ISSN: 0036-1429,1095-7170
DOI: 10.1137/080732432