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limSolve (version 2.0.2)

Solving Linear Inverse Models

Description

Functions that (1) find the minimum/maximum of a linear or quadratic function: min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i) subject to equality constraints Ex=f and/or inequality constraints Gx>=h, (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b, (3) solve a linear system Ax=B for the unknown x. It includes banded and tridiagonal linear systems.

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Version

Install

install.packages('limSolve')

Monthly Downloads

5,371

Version

2.0.2

License

GPL

Maintainer

Karline Soetaert

Last Published

June 3rd, 2026

Functions in limSolve (2.0.2)

E_coli

An underdetermined linear inverse problem: the Escherichia Coli Core Metabolism Model.
RigaWeb

An underdetermined linear inverse problem: the Gulf of Riga *spring* planktonic food web
ldei

Weighted Least Distance Programming with equality and inequality constraints.
Solve

Generalised inverse solution of Ax = B
Chemtax

An overdetermined linear inverse problem: estimating algal composition based on pigment biomarkers.
Solve.banded

Solution of a banded system of linear equations
Blending

A linear inverse blending problem
Solve.block

Solution of an almost block diagonal system of linear equations
Solve.tridiag

Solution of a tridiagonal system of linear equations
resolution

Row and column resolution of a matrix.
Minkdiet

An underdetermined linear inverse problem: estimating diet composition of Southeast Alaskan Mink.
varsample

Samples the probability density function of variables of linear inverse problems.
lsei

Least Squares with Equalities and Inequalities
limSolve-package

Solving Linear Inverse Models
varranges

Calculates ranges of inverse variables in a linear inverse problem
nnls

Nonnegative Least Squares
xranges

Calculates ranges of the unknowns of a linear inverse problem
ldp

Least Distance Programming
xsample

Randomly samples an underdetermined problem with linear equality and inequality constraints
linp

Linear Programming.