Derive closed-form expressions for _1, _2, . . . , _p where for j = 1, 2, . . . , p: _j = arg minjL(_1, . . . , _(j1), _j , _(j+1), . . . , _p). What to submit: a closed-form expression along with your working. Hint: Be careful, this is not as straightforward as it might seem at first. It is recommended to choose a value for p, e.g. p = 8 and first write out the expression in terms of summations. Then take derivatives to get the closed-form expressions. the loss model is L() = 1/(2*p) * norm(y - , ord = '2') ^2 + lambda * norm(W*, ord = '2')^2 where W and is a matrix, p is 8, y is also a matrix