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    Metadata details:
    filename: corner.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 [k_corner,info] = corner(rho,eta,fig)
%CORNER Find corner of discrete L-curve via adaptive pruning algorithm.
%
% [k_corner,info] = corner(rho,eta,fig)
%
% Returns the integer k_corner such that the corner of the log-log
% L-curve is located at ( log(rho(k_corner)) , log(eta(k_corner)) ).
%
% The vectors rho and eta must contain corresponding values of the
% residual norm || A x - b || and the solution's (semi)norm || x ||
% or || L x || for a sequence of regularized solutions, ordered such
% that rho and eta are monotonic and such that the amount of
% regularization decreases as k increases.
%
% The second output argument describes possible warnings.
% Any combination of zeros and ones is possible.
%   info = 000 : No warnings - rho and eta describe a discrete 
%                   L-curve with a corner.
%   info = 001 : Bad data - some elements of rho and/or eta are
%                   Inf, NaN, or zero.
%   info = 010 : Lack of monotonicity - rho and/or eta are not
%                   strictly monotonic.
%   info = 100 : Lack of convexity - the L-curve described by rho
%                   and eta is concave and has no corner.
%
% The warnings described above will also result in text warnings on the
% command line. Type 'warning off Corner:warnings' to disable all
% command line warnings from this function.

    
function [k_corner,info] = corner(rho,eta,fig)
%CORNER Find corner of discrete L-curve via adaptive pruning algorithm.
%
% [k_corner,info] = corner(rho,eta,fig)
%
% Returns the integer k_corner such that the corner of the log-log
% L-curve is located at ( log(rho(k_corner)) , log(eta(k_corner)) ).
%
% The vectors rho and eta must contain corresponding values of the
% residual norm || A x - b || and the solution's (semi)norm || x ||
% or || L x || for a sequence of regularized solutions, ordered such
% that rho and eta are monotonic and such that the amount of
% regularization decreases as k increases.
%
% The second output argument describes possible warnings.
% Any combination of zeros and ones is possible.
%   info = 000 : No warnings - rho and eta describe a discrete 
%                   L-curve with a corner.
%   info = 001 : Bad data - some elements of rho and/or eta are
%                   Inf, NaN, or zero.
%   info = 010 : Lack of monotonicity - rho and/or eta are not
%                   strictly monotonic.
%   info = 100 : Lack of convexity - the L-curve described by rho
%                   and eta is concave and has no corner.
%
% The warnings described above will also result in text warnings on the
% command line. Type 'warning off Corner:warnings' to disable all
% command line warnings from this function.
%
% If a third input argument is present, then a figure will show the discrete
% L-curve in log-log scale and also indicate the found corner.

% Reference: P. C. Hansen, T. K. Jensen and G. Rodriguez, "An adaptive
% pruning algorithm for the discrete L-curve criterion," J. Comp. Appl.
% Math., 198 (2007), 483-492.
  
% Per Christian Hansen and Toke Koldborg Jensen, IMM, DTU;
% Giuseppe Rodriguez, University of Cagliari, Italy; March 22, 2006.

% Initialization of data
rho = rho(:);       % Make rho and eta column vectors.
eta = eta(:);

if (nargin < 3) | isempty(fig)
    fig = 0;        % Default is no figure.
elseif fig < 0,
    fig = 0;
end