سوال ۲۹ کنکور ارشد علوم کامپیوتر ۱۴۰۰

Read the following passage and answer the question.

PASSAGE 3: Structure, Sparsity, and Black Boxes

Large matrices, perhaps in excess of $10^{5}$, arise routinely in scientific problems. Most large matrices of computational interest are simpler than their vast numbers of individual entries might suggest, exhibiting some kind of structure. The most obvious structure that a large matrix may have is a preponderance of zero entries — sparsity. (The opposite of sparse is dense.) A matrix arising from the finite-difference discretization of a partial differential equation may have dimension $m = 10^{5}$ with only $\approx 10$ nonzero entries per row. This kind of sparsity is readily exploited by the iterative methods, for they only need to manipulate the matrix in the form of a black box:

$x \longrightarrow \boxed{\text{BLACK BOX}} \longrightarrow Ax.$

The iterative algorithm requires nothing more than the ability to determine $Ax$ for any $x$. For a sparse matrix $A$, computing $Ax$ takes only $O(\nu m)$ rather than $O(m^{2})$ operations, and a sparse solve can be made far cheaper than the $O(m^{3})$ cost of classic direct methods. This is in contrast to the algorithms of direct linear algebra, such as Gaussian elimination, which explicitly manipulate matrix entries and in the process generally destroy sparsity.

Question: According to the passage:

The $O(m^{3})$ bottleneck ----------.