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

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% minqdef.m %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function [x,y,ier]=minqdef(c,G,A,b,eq,prt,xx);
% minimizes a definite quadratic form subject to linear constraints
%    min    fct = c^T x + 0.5 x^T G x 
%    s.t.   A x >= b, with equality at indices with eq=1
% where G is a definite symmetric n x n matrix
%
% if A is sparse, it is assumed that the ordering is such that
% sparse Cholesky factorization of G and AG^(-1)A^T are feasible
%
% eq    characteristic vector of equalities
% prt	printlevel
% xx	guess (optional)
%
% x	minimizer (but unbounded direction if ier=1)
% y     Lagrange multiplier satisfying the KKT conditions
%       Gx=A^Ty-c, inf(y,Ax-b)=0 at indices with eq=0
% ier	0  (global minimizer found)
% 	1  (approximate solution; feasible set probably empty)
% 	99 (approximate solution; maxit exceeded)
%
% Method: apply minq.m to the dual
%    min  0.5*(A^
    
function [x,y,ier]=minqdef(c,G,A,b,eq,prt,xx);
% minimizes a definite quadratic form subject to linear constraints
%    min    fct = c^T x + 0.5 x^T G x 
%    s.t.   A x >= b, with equality at indices with eq=1
% where G is a definite symmetric n x n matrix
%
% if A is sparse, it is assumed that the ordering is such that
% sparse Cholesky factorization of G and AG^(-1)A^T are feasible
%
% eq    characteristic vector of equalities
% prt	printlevel
% xx	guess (optional)
%
% x	minimizer (but unbounded direction if ier=1)
% y     Lagrange multiplier satisfying the KKT conditions
%       Gx=A^Ty-c, inf(y,Ax-b)=0 at indices with eq=0
% ier	0  (global minimizer found)
% 	1  (approximate solution; feasible set probably empty)
% 	99 (approximate solution; maxit exceeded)
%
% Method: apply minq.m to the dual
%    min  0.5*(A^Ty-c)^TG^(-1)(A^Ty-c)-b^Ty 
%    s.t. y(~eq)>=0
% x is recovered as x=G^(-1)(A^Ty-c)
%
function [x,y,ier]=minqdef(c,G,A,b,eq,prt,xx);


R=chol(G);

[m,n]=size(A);
A0=A/R;
GG=A0*A0';
c0=R'\c;
cc=-b-A0*c0;
yo=inf+zeros(m,1);
yu=zeros(m,1);yu(eq)=-yo(eq);

[y,fct,ier]=minq(0,cc,GG,yu,yo,prt);
x=R\(A0'*y-c0);
if ier==99, return; end