Initialization: Choose an initial point $($x$\_(0),\backslash$mu $\_(0))$ and a learning ratc $\backslash$alpha $.$ Gradient Computation: Compute the gradient of the function gradf$($x$,$y$)=(($delf$)/($delx$),($delf$)/($dely$)).$ For f$($x$,$y$)=($x$^(2)-1)^(2)+($y$-1)^(2)$; $($delf$)/($delx$)=4$x$($x$^(2)-1)($delf$)/($dely$)=2($y$-1)$ Update Rule: Update the current point using the gradient: x$\_($n$+1)=$x$\_($n$)-\backslash$alpha $($delf$)/($delx$)$ y$\_($n$+1)=$y$\_($n$)-\backslash$alpha $($delf$)/($dely$)$ Convergence Check: Repeat steps $2-3$ until convergence, i$.$c$.,$ until the change in the function value between iterations is less than a small threshold $($e$.$g$.,\backslash$epsi $=1\backslash$times $10^(-6))$ or for a fixed number of iterations. Tasks Implement the above vanilla gradient descent algorithm to minimize f$($x$,$y$).$ Start from an initial point $($x$\_(0),$y$\_(0))$ chosen arbitrarily $($e$.$g$.,($x