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: minq.m
    path of file: ./repos/optimviz/utils/mcs/private
    
    the code of the file until where you have to start completion: 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% minq.m %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function [x,fct,ier,nsub]=minq(gam,c,G,xu,xo,prt,xx);
% minimizes an affine quadratic form subject to simple bounds,
% using coordinate searches and reduced subspace minimizations
% using LDL^T factorization updates
%    min    fct = gam + c^T x + 0.5 x^T G x 
%    s.t.   x in [xu,xo]    % xu<=xo is assumed
% where G is a symmetric n x n matrix, not necessarily definite
% (if G is indefinite, only a local minimum is found)
%
% if G is sparse, it is assumed that the ordering is such that
% a sparse modified Cholesky factorization is feasible
%
% prt	printlevel
% xx	initial guess (optional)
%
% x	minimizer (but unbounded direction if ier=1)
% fct	optimal function value
% ier	0  (local minimizer found)
% 	1  (unbounded below)
% 	99 (maxit exceeded)
% 	-1 (input error)
%
% calls getalp.m, ldl*.m, minqsub.m, pr01.m
%
function [x,fct,ier,nsub]=minq(gam,c,G,xu,xo,prt,xx);




%%% initialization %%%
%disp('###############################
    
function [x,fct,ier,nsub]=minq(gam,c,G,xu,xo,prt,xx);
% minimizes an affine quadratic form subject to simple bounds,
% using coordinate searches and reduced subspace minimizations
% using LDL^T factorization updates
%    min    fct = gam + c^T x + 0.5 x^T G x 
%    s.t.   x in [xu,xo]    % xu<=xo is assumed
% where G is a symmetric n x n matrix, not necessarily definite
% (if G is indefinite, only a local minimum is found)
%
% if G is sparse, it is assumed that the ordering is such that
% a sparse modified Cholesky factorization is feasible
%
% prt	printlevel
% xx	initial guess (optional)
%
% x	minimizer (but unbounded direction if ier=1)
% fct	optimal function value
% ier	0  (local minimizer found)
% 	1  (unbounded below)
% 	99 (maxit exceeded)
% 	-1 (input error)
%
% calls getalp.m, ldl*.m, minqsub.m, pr01.m
%
function [x,fct,ier,nsub]=minq(gam,c,G,xu,xo,prt,xx);




%%% initialization %%%
%disp('####################################################');
%disp('######################  minq  ######################');
%disp('####################################################');
convex=0;
n=size(G,1);

if prt>0, 
  % show printlevel
  printlevel=prt

  % check input data for consistency
  ier=0;
  if size(G,2)~=n, 
    ier=-1;disp('minq: Hessian has wrong dimension');
    x=NaN+zeros(n,1);fct=NaN;nsub=-1;
    return;
  end