Algorithm 676: ODRPACK: software for weighted orthogonal distance regression

Authors:
Paul T. Boggs;Janet R. Donaldson;Richaard h. Byrd;Robert B. Schnabel
Affiliations:
National Institute of Standards and Technology, Gaithersburg, MD;National Institute of Standards and Technology, Boulder, CO;Univ. of Colorado, Boulder;Univ. of Colorado, Boulder
Venue:
ACM Transactions on Mathematical Software (TOMS)
Year:
1989

Citing 6
Cited 3

Measurement error models

Measurement error models
A stable and efficient algorithm for nonlinear orthogonal distance regression

SIAM Journal on Scientific and Statistical Computing
Computational experience with confidence intervals for nonlinear least squares

Technometrics
Basic Linear Algebra Subprograms for Fortran Usage

ACM Transactions on Mathematical Software (TOMS)
Testing Unconstrained Optimization Software

ACM Transactions on Mathematical Software (TOMS)
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

Next generation modeling II - applications: improving the development process for eukaryotic cell cycle models with a modeling support environment

Proceedings of the 35th conference on Winter simulation: driving innovation
Algorithm 869: ODRPACK95: A weighted orthogonal distance regression code with bound constraints

ACM Transactions on Mathematical Software (TOMS)
Stratified Generalized Procrustes Analysis

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we describe ODRPACK, a software package for the weighted orthogonal distance regression problem. This software is an implementation of the algorithm described in [2] for finding the parameters that minimize the sum of the squared weighted orthogonal distances from a set of observations to a curve or surface determined by the parameters. It can also be used to solve the ordinary nonlinear least squares problem. The weighted orthogonal distance regression procedure application to curve and surface fitting and to measurement error models in statistics. The algorithm implemented is an efficient and stable trust region (Levenberg-Marquardt) procedure that exploits the structure of the problem so that the computational cost per iteration is equal to that for the same type of algorithm applied to the ordinary nonlinear least squares problem. The package allows a general weighting scheme, provides for finite difference derivatives, and contains extensive error checking and report generating facilities.