- Referenced in 921 articles
- Kernel smoothing refers to a general methodology for recovery of the underlying structure in data ... required for a comprehensive understanding of kernel smoothing, and hence smoothing problems in general ... given to the problem of choosing the smoothing parameter.par For the study of the book ... research in the field of kernel smoothing. But the bibliographical notes...
- Referenced in 610 articles
- pages, is devoted to the optimization of smooth functions. The methods studied in this first ... treatment of both, optimization methods for smooth and for noisy functions is a unique feature...
- Referenced in 518 articles
- effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints ... discuss an SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit...
- Referenced in 697 articles
- interest even to experts on smoothing, and they are a third possible audience. This book...
- Referenced in 277 articles
- Smoothing spline ANOVA models Nonparametric function estimation with stochastic data, otherwise known as smoothing ... ample desktop and laptop computing power, smoothing methods are now finding their ways into everyday ... methods have proved successful for univariate smoothing, ones practical in multivariate settings number far less ... Smoothing spline ANOVA models are a versatile family of smoothing methods derived through roughness penalties...
- Referenced in 442 articles
- both. The nonlinear functions must be smooth. Stable numerical methods are employed throughout. Features include...
- Referenced in 320 articles
- choose various multigrid cycles, transfer operators, smoothing methods, and nested iteration, and defect correction. Cell...
- Referenced in 290 articles
- accessible and the author used a fairly smooth way even in the case of advanced...
- Referenced in 147 articles
- Fortran 77 subroutines for minimizing a smooth function subject to constraints, which may include simple ... bounds on the variables, linear constraints, and smooth nonlinear constraints. The user provides subroutines...
- Referenced in 179 articles
- effective for solving linear, quadratic, and nonlinear smooth optimization problems, both convex and nonconvex...
- Referenced in 151 articles
- combination. They are designed to interact very smoothly. Computer algebra system (CAS). Education...
- Referenced in 144 articles
- consider the problem of minimizing a smooth objective function f of n real variables...
- Referenced in 134 articles
- package of Fortran subroutines for calculating smoothing splines for various kinds of data and geometries...
- Referenced in 131 articles
- statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated...
- Referenced in 123 articles
- already quite immense. This paper applies a smoothing technique and an accelerated first-order algorithm...
- Referenced in 123 articles
- black-box classes to construct highly-scalable smoothed aggregation preconditioners. ML preconditioners have been used...
- Referenced in 121 articles
- appropriate homotopy map and then tracking some smooth curve in the zero set of this...
- Referenced in 117 articles
- user to write their own wavelet smoothing routine making use of the function ebayesthresh...
- Referenced in 84 articles
- mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation. Routines for GAMs and other generalized ... ridge regression with multiple smoothing parameter selection by GCV, REML or UBRE/AIC. Also GAMMs. Includes...
- Referenced in 73 articles
- Additive Modelling. We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models ... combination of sparsity and smoothness is crucial for mathematical theory as well as performance ... Finally, an adaptive version of our sparsity-smoothness penalized approach yields large additional performance gains...