global numf numg

x = struct('p',[-1.2; 1]);
sdparams = struct('maxit',10000,'toler',1.0e-4);

numf=0; numg=0;
[inform,xnew] = SteepDescent(@obja,x,sdparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %10.6g.\n', sdparams.toler);
else
  fprintf('Success: %d steps taken\n', inform.iter);
end
fprintf('  Ending point: '); fprintf('%10.6g ',xnew.p);
fprintf('\n  Ending function value: %10.6g\n', xnew.f);
fprintf('  No. function evaluations: %d, No. gradient evaluations %d\n',...
    numf, numg);
fprintf('  Norm of ending gradient: %10.6g\n\n\n', norm(xnew.g));

numf=0; numg=0;
[inform,xnew] = SteepDescent(@objb,x,sdparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %10.6g.\n', sdparams.toler);
else
  fprintf('Success: %d steps taken\n', inform.iter);
end
fprintf('  Ending point: '); fprintf('%10.6g ',xnew.p);
fprintf('\n  Ending function value: %10.6g\n', xnew.f);
fprintf('  No. function evaluations: %d, No. gradient evaluations %d\n',...
    numf, numg);
fprintf('  Norm of ending gradient: %10.6g\n\n\n', norm(xnew.g));

numf=0; numg=0;
[inform,xnew] = SteepDescent(@objc,x,sdparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %10.6g.\n', sdparams.toler);
else
  fprintf('Success: %d steps taken\n', inform.iter);
end
fprintf('  Ending point: '); fprintf('%10.6g ',xnew.p);
fprintf('\n  Ending function value: %10.6g\n', xnew.f);
fprintf('  No. function evaluations: %d, No. gradient evaluations %d\n',...
    numf, numg);
fprintf('  Norm of ending gradient: %10.6g\n\n\n', norm(xnew.g));