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linprog.m
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linprog.m
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function [z, history] = linprog(c, A, b, rho, alpha)
% linprog Solve standard form LP via ADMM
%
% [x, history] = linprog(c, A, b, rho, alpha);
%
% Solves the following problem via ADMM:
%
% minimize c'*x
% subject to Ax = b, x >= 0
%
% The solution is returned in the vector x.
%
% history is a structure that contains the objective value, the primal and
% dual residual norms, and the tolerances for the primal and dual residual
% norms at each iteration.
%
% rho is the augmented Lagrangian parameter.
%
% alpha is the over-relaxation parameter (typical values for alpha are
% between 1.0 and 1.8).
%
%
% More information can be found in the paper linked at:
% http://www.stanford.edu/~boyd/papers/distr_opt_stat_learning_admm.html
%
t1 = cputime;
QUIET = 0;
MAX_ITER = 1000;
ABSTOL = 1e-4;
RELTOL = 1e-2;
[m n] = size(A);
x = zeros(n,1);
z = zeros(n,1);
u = zeros(n,1);
if ~QUIET
fprintf('%3s\t%10s\t%10s\t%10s\t%10s\t%10s\n', 'iter', ...
'r norm', 'eps pri', 's norm', 'eps dual', 'objective');
end
for k = 1:MAX_ITER
% x-update
tmp = [ rho*eye(n), A'; A, zeros(m) ] \ [ rho*(z - u) - c; b ];
x = tmp(1:n);
% z-update with relaxation
zold = z;
x_hat = alpha*x + (1 - alpha)*zold;
z = max(x_hat + u, 0);
u = u + (x_hat - z);
% diagnostics, reporting, termination checks
history.objval(k) = objective(c, x);
history.r_norm(k) = norm(x - z);
history.s_norm(k) = norm(-rho*(z - zold));
history.eps_pri(k) = sqrt(n)*ABSTOL + RELTOL*max(norm(x), norm(-z));
history.eps_dual(k)= sqrt(n)*ABSTOL + RELTOL*norm(rho*u);
t2 = cputime;
history.time(k) = t2 - t1;
if ~QUIET
fprintf('%3d\t%10.4f\t%10.4f\t%10.4f\t%10.4f\t%10.2f\n', k, ...
history.r_norm(k), history.eps_pri(k), ...
history.s_norm(k), history.eps_dual(k), history.objval(k));
end
if (history.r_norm(k) < history.eps_pri(k) && ...
history.s_norm(k) < history.eps_dual(k))
break;
end
end
if ~QUIET
t3 = cputime;
excutetime = t3 - t1
end
end
function obj = objective(c, x)
obj = c'*x;
end