global numf numg

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

fprintf('Problem a\n');
[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 %8.4g.\n', sdparams.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));

fprintf('Problem b\n');
[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 %8.4g.\n', sdparams.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));

fprintf('Problem c\n');
[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 %8.4g.\n', sdparams.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));

fprintf('Problem d\n');
% x = struct('p',2*ones(2,1));
x = struct('p',2.1*ones(2,1));
[inform,xnew] = SteepDescent(@objd,x,sdparams);
if inform.status == 0
  fprintf('CONVERGENCE FAILURE: %d steps were taken without\n', inform.iter);
  fprintf('gradient size decreasing below %8.4g.\n', sdparams.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));