A Newton Method for Convex Regression, Data Smoothing, and Quadratic Programming with Bounded Constraints

DOI10.1137/0803022zbMath0793.90048OpenAlexW2047391904MaRDI QIDQ3139997

Wu Li, John J. Swetits

Publication date: 16 August 1994

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/0803022

zbMATH Keywords

unconstrained minimizationNewton methodsconstrained convex optimizationdata smoothingleast-distance problemsymmetric monotone linear complementaritypiecewise linear equationsconvex regression problemmultivariate quadratic spline

Mathematics Subject Classification ID

Parametric inference under constraints (62F30) Convex programming (90C25) Applications of mathematical programming (90C90) Quadratic programming (90C20) Newton-type methods (49M15) Sensitivity, stability, parametric optimization (90C31) Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33) Approximation with constraints (41A29) Conditioning of matrices (15A12)

Related Items (7)

Linearly convergent descent methods for the unconstrained minimization of convex quadratic splines ⋮ Exact penalty functions for constrained minimization problems via regularized gap function for variational inequalities ⋮ A conjugate gradient method for the unconstrained minimization of strictly convex quadratic splines ⋮ A finite newton method for classification ⋮ On the characterization of quadratic splines ⋮ Linear Convergence of Descent Methods for the Unconstrained Minimization of Restricted Strongly Convex Functions ⋮ Newton's method for linear inequality systems

This page was built for publication: A Newton Method for Convex Regression, Data Smoothing, and Quadratic Programming with Bounded Constraints