You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: plsqr_b.m
    path of file: ./repos/HRTF-Individualization/model/matlabcode/functions/library/regu
    
    the code of the file until where you have to start completion: function [X,rho,eta,F] = plsqr_b(A,L,W,b,k,reorth,sm)
%PLSQR_B "Precond." version of the LSQR Lanczos bidiagonalization algorithm.
%
% [X,rho,eta,F] = plsqr_b(A,L,W,b,k,reorth,sm)
%
% Performs k steps of the `preconditioned' LSQR Lanczos
% bidiagonalization algorithm applied to the system
%    min || (A*L_p) x - b || ,
% where L_p is the A-weighted generalized inverse of L.  Notice
% that the matrix W holding a basis for the null space of L must
% also be specified.
%
% The routine returns all k solutions, stored as columns of
% the matrix X.  The solution seminorm and the residual norm are
% returned in eta and rho, respectively.
%
% If the generalized singular values sm of (A,L) are also provided,
% then glsqr computes the filter factors associated with each step
% and stores them columnwise in the matrix F.
%
% Reorthogonalization is controlled by means of reorth:
%    reorth = 0 : no reorthogonalization (default),
%    reorth = 1 : reorthogonalization by means of MGS

% References: C. C. Paige & M. A. Saunders, "LSQR: an algorithm for
% sparse linear equations and sparse least squares", ACM Trans.
% Math. Software 8 (1982), 43-71.
% P. C. Hansen, "Rank-Deficient and Discrete Ill-Posed Problems.

    
function [X,rho,eta,F] = plsqr_b(A,L,W,b,k,reorth,sm)
%PLSQR_B "Precond." version of the LSQR Lanczos bidiagonalization algorithm.
%
% [X,rho,eta,F] = plsqr_b(A,L,W,b,k,reorth,sm)
%
% Performs k steps of the `preconditioned' LSQR Lanczos
% bidiagonalization algorithm applied to the system
%    min || (A*L_p) x - b || ,
% where L_p is the A-weighted generalized inverse of L.  Notice
% that the matrix W holding a basis for the null space of L must
% also be specified.
%
% The routine returns all k solutions, stored as columns of
% the matrix X.  The solution seminorm and the residual norm are
% returned in eta and rho, respectively.
%
% If the generalized singular values sm of (A,L) are also provided,
% then glsqr computes the filter factors associated with each step
% and stores them columnwise in the matrix F.
%
% Reorthogonalization is controlled by means of reorth:
%    reorth = 0 : no reorthogonalization (default),
%    reorth = 1 : reorthogonalization by means of MGS

% References: C. C. Paige & M. A. Saunders, "LSQR: an algorithm for
% sparse linear equations and sparse least squares", ACM Trans.
% Math. Software 8 (1982), 43-71.
% P. C. Hansen, "Rank-Deficient and Discrete Ill-Posed Problems.
% Numerical Aspects of Linear Inversion", SIAM, Philadelphia, 1997.

% Per Christian Hansen, IMM, Sept. 13, 2001.

% The fudge threshold is used to prevent filter factors from exploding.
fudge_thr = 1e-4;

% Initialization
if (k < 1), error('Number of steps k must be positive'), end