n = 10;
for i=1:n
  for j=1:n
    H(i,j) = 1/(i+j-1);
  end;
end
[L,d,p,e] = mchol(H);
L,d,p,e,pause,

global numf numg

x = struct('p',[-1.2; 1]);
nparams = struct('maxit',1000,'toler',1.0e-4,'method','chol');

[inform,xnew] = Newton(@obja,x,nparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n', nparams.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\n\n\n', norm(xnew.g));

[inform,xnew] = Newton(@objb,x,nparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n', nparams.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\n\n\n', norm(xnew.g));

[inform,xnew] = Newton(@objc,x,nparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n', nparams.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\n\n\n', norm(xnew.g));

[inform,xnew] = Newton(@obje,x,nparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n', nparams.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\n\n\n', norm(xnew.g));