ML IALGO LINREG: Difference between revisions

Revision as of 18:35, 12 October 2021

ML_IALGO_LINREG = [integer]
Default: ML_IALGO_LINREG = 1

Description: This tag determines with which algorithm to solve the system of linear equations in the ridge regression method for machine learning.

In the ridge regression method for machine learning one needs to solve for the unknown weights $\mathbf{w}$ within the following equations

${\displaystyle \mathbf{Y} = \mathbf{\Phi} \mathbf{w}. }$

For more details please see here.

The following options are available to solve for $\mathbf{w}$ :

ML_IALGO_LINREG=1: Bayesian linear regression (see here). Usable with NSW $\ge$ 1.
ML_IALGO_LINREG=2: QR factorization. Usable with NSW=0,1.
ML_IALGO_LINREG=3: Singular value decomposition. Usable with NSW=0,1.
ML_IALGO_LINREG=4: Singular value decomposition with Tikhonov regularization. Usable with NSW=0,1.

@@ Line 10: / Line 10: @@
 </math>
-The following options are available:
+For more details please see [[Machine learning force field: Theory#Matrix vector form of linear equations|here]].
+The following options are available to solve for <math>\mathbf{w}</math>:
 *{{TAG|ML_IALGO_LINREG}}=1: Bayesian linear regression (see [[Machine learning force field: Theory#Bayesian error estimation|here]]). Usable with {{TAG|NSW}}<math>\ge</math>1.
 *{{TAG|ML_IALGO_LINREG}}=2: QR factorization. Usable with {{TAG|NSW}}=0,1.