global numf numg numH

mNparams = struct('maxit',10000,'toler',1.0e-5, 'eta', 1.0e-4, ...
		  'DeltaMax', 100, 'Delta0',1.0)  ;

x = struct('p',[-1.2; 1]);
numf=0; numg=0; numH=0;
fprintf(' \n** Rosenbrock with first starting point\n');
[inform,xnew] = modNewtonDogleg(@objc,x,mNparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n', mNparams.toler);
else
  fprintf('Success: %d steps taken\n', inform.iter);
end
fprintf('  Ending point: '); fprintf('%8.4g ',xnew.p);
fprintf('\n  Ending value: '); fprintf('%8.4g ',xnew.f);
fprintf('; No. function evaluations: %d',numf);
% fprintf('\n  Ending gradient: '); fprintf('%8.4g ',xnew.g);
fprintf('; No. gradient evaluations %d',numg);
fprintf('\n  Norm of ending gradient: %8.4g', norm(xnew.g));
fprintf('\n  No. Hessian evaluations %d',numH);
fprintf('\n Final Hessian condition number %8.2e\n\n', cond(xnew.h));

x = struct('p',[-10.; 1.]);
numf=0; numg=0; numH=0;
fprintf(' \n** Rosenbrock with second starting point\n');
[inform,xnew] = modNewtonDogleg(@objc,x,mNparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n', mNparams.toler);
else
  fprintf('Success: %d steps taken\n', inform.iter);
end
fprintf('  Ending point: '); fprintf('%8.4g ',xnew.p);
fprintf('\n  Ending value: '); fprintf('%8.4g ',xnew.f);
fprintf('; No. function evaluations: %d',numf);
% fprintf('\n  Ending gradient: '); fprintf('%8.4g ',xnew.g);
fprintf('; No. gradient evaluations %d',numg);
fprintf('\n  Norm of ending gradient: %8.4g', norm(xnew.g));
fprintf('\n  No. Hessian evaluations %d',numH);
fprintf('\n Final Hessian condition number %8.2e\n\n', cond(xnew.h));

x = struct('p',[0;1]);
numf=0; numg=0; numH=0;
fprintf(' \n** Powell badly scaled\n');
[inform,xnew] = modNewtonDogleg(@objd,x,mNparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n', mNparams.toler);
else
  fprintf('Success: %d steps taken\n', inform.iter);
end
fprintf('  Ending point: '); fprintf('%8.4g ',xnew.p);
fprintf('\n  Ending value: '); fprintf('%8.4g ',xnew.f);
fprintf('; No. function evaluations: %d',numf);
% fprintf('\n  Ending gradient: '); fprintf('%8.4g ',xnew.g);
fprintf('; No. gradient evaluations %d',numg);
fprintf('\n  Norm of ending gradient: %8.4g', norm(xnew.g));
fprintf('\n  No. Hessian evaluations %d',numH);
fprintf('\n Final Hessian condition number %8.2e\n\n', cond(xnew.h));