
Step 1: Recall the vector calculus product rule.
For a scalar field $\phi$ and a vector field $\mathbf{u}$, the product rule for divergence is:
\[
\nabla \cdot (\phi \mathbf{u}) = \phi \, (\nabla \cdot \mathbf{u}) + (\nabla \phi) \cdot \mathbf{u}.
\]
Step 2: Interpret terms.
- The first term $\phi \, (\nabla \cdot \mathbf{u})$ scales the divergence of $\mathbf{u}$ by $\phi$.
- The second term $(\nabla \phi) \cdot \mathbf{u}$ is the dot product of the gradient of $\phi$ with $\mathbf{u}$.
Step 3: Compare with options.
This matches exactly with Option (A):
\[
\nabla \cdot (\phi \mathbf{u}) = \phi \, \nabla \cdot \mathbf{u} + \mathbf{u} \cdot \nabla \phi.
\]
\[
\boxed{\nabla \cdot (\phi \mathbf{u}) = \phi \, (\nabla \cdot \mathbf{u}) + \mathbf{u} \cdot \nabla \phi}
\]
Cholesky decomposition is carried out on the following square matrix [A]. \[ [A] = \begin{bmatrix} 8 & -5 \\ -5 & a_{22} \end{bmatrix} \] Let \( l_{ij} \) and \( a_{ij} \) be the (i,j)\textsuperscript{th elements of matrices [L] and [A], respectively. If the element \( l_{22} \) of the decomposed lower triangular matrix [L] is 1.968, what is the value (rounded off to the nearest integer) of the element \( a_{22} \)?}
| Point | Staff Readings Back side | Staff Readings Fore side | Remarks |
|---|---|---|---|
| P | -2.050 | - | 200.000 |
| Q | 1.050 | 0.95 | Change Point |
| R | - | -1.655 | - |