Let f be a function from R$^$n to R $.$ Consider the application of the gradient descent method to minimize f$($x$),$x belongs to R$^$n$,$ versus an application of the method to minimize

F$($x$)=(||$gradient f$($x$)||^2)\_2($square of $2$norm of delta f$($x$)).$

In which of the two cases, do you think the convergence would be faster $?$