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nlsic

Non Linear Least Squares with Inequality Constraints

Installation

About

We solve non linear least squares problems with optional equality and/or inequality constraints. Non linear iterations are globalized with back-tracking method. Linear problems are solved by dense QR decomposition from 'LAPACK' which can limit the size of treated problems. On the other side, we avoid condition number degradation which happens in classical quadratic programming approach. Inequality constraints treatment on each non linear iteration is based on 'NNLS' method (by Lawson and Hanson). We provide an original function 'lsi_ln' for solving linear least squares problem with inequality constraints in least norm sens. Thus if Jacobian of the problem is rank deficient a solution still can be provided. However, truncation errors are probable in this case. Equality constraints are treated by using a basis of Null-space. User defined function calculating residuals must return a list having residual vector (not their squared sum) and Jacobian. If Jacobian is not in the returned list, package 'numDeriv' is used to calculated finite difference version of Jacobian. The 'NLSIC' method was fist published in Sokol et al. (2012) doi:10.1093/bioinformatics/btr716.

github.com/MathsCell/nlsic
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Key Metrics

Version 1.0.4
Published 2023-06-26 276 days ago
Needs compilation? no
License GPL-2
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Maintainer

Maintainer

Serguei Sokol

sokol@insa-toulouse.fr

Authors

Serguei Sokol

aut / cre

Material

NEWS
Reference manual
Package source

In Views

Optimization

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

nlsic archive

Depends

nnls

Suggests

numDeriv
RUnit
limSolve

Reverse Imports

bspline