comparison matlab/BP/l1qc_logbarrier.m @ 2:735a0e24575c

Organized folders: added tests, apps, matlab, docs folders. Added __init__.py files

+% 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: [e-mail]
+% 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 = 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;
+
+% Added by Nic
+if lbiter == 0
+    xp = zeros(size(x0));
+end
+
+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
+