ls_ln_svd

Least squares <code>a%*%x ~= b</code> of least norm ||x|| by using svd(a)

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>.

Serguei Sokol

nlsic

Non Linear Least Squares with Inequality Constraints

ls_ln_svd function

<dl><dt>a</dt>
<dd>dense matrix</dd>
<dt>b</dt>
<dd>right hand side vector</dd>
<dt>rcond</dt>
<dd>maximal condition number for determining rank deficient matrix</dd></dl>

Arguments

Linear Least Squares, least norm solution (by svd) — ls_ln_svd

<dl>

<dt>a</dt>
<dd>dense matrix</dd>


<dt>b</dt>
<dd>right hand side vector</dd>


<dt>rcond</dt>
<dd>maximal condition number for determining rank deficient matrix</dd>

</dl>

ls_ln_svd: Linear Least Squares, least norm solution (by svd)

Description

Usage

Value

Arguments