global numf numg numH

nparams = struct('maxit',1000,'toler',1.0e-6,'feastol',1e-6);

rand('state',0);
x = struct('p',rand(50,1));

fprintf('Starting value: %8.4g\n',objg(x.p,1));
A = [ones(1,50); rand(1,50)]; b = A*x.p;

tic, [inform,xnew] = TNewtonEq(@objg,x,A,b,nparams); toc,
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n\n', nparams.toler);
else
  fprintf('Success: %d steps taken\n\n', inform.iter);
end
fprintf('Ending value: %8.4g\n',xnew.f);
fprintf('No. function evaluations: %d\n',numf);
fprintf('No. gradient evaluations %d\n',numg);
fprintf('No. Hessian evaluations %d\n\n',numH);
[m,n] = size(A); [Q,R] = qr(A'); Z = Q(:,m+1:n);
fprintf('Norm of KKT violation: %8.4g\n', norm(Z'*xnew.g));
fprintf('Norm of feasibility error: %8.4g\n', norm(A*xnew.p-b));

load hwk9 A
b = ones(size(A,1),1);
x.p = A\b;
tic, [inform,xnew] = TNewtonEq(@objqp,x,A,b,nparams); toc,
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n\n', nparams.toler);
else
  fprintf('Success: %d steps taken\n\n', inform.iter);
end
fprintf('Ending value: %8.4g\n',xnew.f);
fprintf('No. function evaluations: %d\n',numf);
fprintf('No. gradient evaluations %d\n',numg);
fprintf('No. Hessian evaluations %d\n\n',numH);
[m,n] = size(A); [Q,R] = qr(A'); Z = Q(:,m+1:n);
fprintf('Norm of KKT violation: %8.4g\n', norm(Z'*xnew.g));
fprintf('Norm of feasibility error: %8.4g\n', norm(A*xnew.p-b));