% l1qc_logbarrier.m
%
% Solve quadratically constrained l1 minimization:
% min ||x||_1   s.t.  ||Ax - b||_2 <= \epsilon
%
% Reformulate as the second-order cone program
% min_{x,u}  sum(u)   s.t.    x - u <= 0,
%                            -x - u <= 0,
%      1/2(||Ax-b||^2 - \epsilon^2) <= 0
% and use a log barrier algorithm.
%
% Usage:  xp = l1qc_logbarrier(x0, A, At, b, epsilon, lbtol, mu, cgtol, cgmaxiter)
%
% x0 - Nx1 vector, initial point.
%
% A - Either a handle to a function that takes a N vector and returns a K 
%     vector , or a KxN matrix.  If A is a function handle, the algorithm
%     operates in "largescale" mode, solving the Newton systems via the
%     Conjugate Gradients algorithm.
%
% At - Handle to a function that takes a K vector and returns an N vector.
%      If A is a KxN matrix, At is ignored.
%
% b - Kx1 vector of observations.
%
% epsilon - scalar, constraint relaxation parameter
%
% lbtol - The log barrier algorithm terminates when the duality gap <= lbtol.
%         Also, the number of log barrier iterations is completely
%         determined by lbtol.
%         Default = 1e-3.
%
% mu - Factor by which to increase the barrier constant at each iteration.
%      Default = 10.
%
% cgtol - Tolerance for Conjugate Gradients; ignored if A is a matrix.
%     Default = 1e-8.
%
% cgmaxiter - Maximum number of iterations for Conjugate Gradients; ignored
%     if A is a matrix.
%     Default = 200.
%
% Written by: Justin Romberg, Caltech
% Email: jrom@acm.caltech.edu
% Created: October 2005
%

function xp = l1qc_logbarrier(x0, A, At, b, epsilon, lbtol, mu, cgtol, cgmaxiter)  

largescale = isa(A,'function_handle');

if (nargin < 6), lbtol = 1e-3; end
if (nargin < 7), mu = 10; end
if (nargin < 8), cgtol = 1e-8; end
if (nargin < 9), cgmaxiter = 200; end

newtontol = lbtol;
newtonmaxiter = 50;

N = length(x0);

% starting point --- make sure that it is feasible
if (largescale)
  if (norm(A(x0)-b) > epsilon)
    disp('Starting point infeasible; using x0 = At*inv(AAt)*y.');
    AAt = @(z) A(At(z));
    [w, cgres] = cgsolve(AAt, b, cgtol, cgmaxiter, 0);
    if (cgres > 1/2)
      disp('A*At is ill-conditioned: cannot find starting point');
      xp = x0;
      return;
    end
    x0 = At(w);
  end
else
  if (norm(A*x0-b) > epsilon)
    disp('Starting point infeasible; using x0 = At*inv(AAt)*y.');
    opts.POSDEF = true; opts.SYM = true;
    [w, hcond] = linsolve(A*A', b, opts);
    if (hcond < 1e-14)
      disp('A*At is ill-conditioned: cannot find starting point');
      xp = x0;
      return;
    end
    x0 = A'*w;
  end  
end
x = x0;
u = (0.95)*abs(x0) + (0.10)*max(abs(x0));

disp(sprintf('Original l1 norm = %.3f, original functional = %.3f', sum(abs(x0)), sum(u)));

% choose initial value of tau so that the duality gap after the first
% step will be about the origial norm
tau = max((2*N+1)/sum(abs(x0)), 1);
                                                                                                                          
lbiter = ceil((log(2*N+1)-log(lbtol)-log(tau))/log(mu));
disp(sprintf('Number of log barrier iterations = %d\n', lbiter));

totaliter = 0;

for ii = 1:lbiter

  [xp, up, ntiter] = l1qc_newton(x, u, A, At, b, epsilon, tau, newtontol, newtonmaxiter, cgtol, cgmaxiter);
  totaliter = totaliter + ntiter;
  
  disp(sprintf('\nLog barrier iter = %d, l1 = %.3f, functional = %8.3f, tau = %8.3e, total newton iter = %d\n', ...
    ii, sum(abs(xp)), sum(up), tau, totaliter));
  
  x = xp;
  u = up;
 
  tau = mu*tau;
  
end