
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):
\[
\nabla \cdot (\phi \mathbf{u}) = \phi \, \nabla \cdot \mathbf{u} + \mathbf{u} \cdot \nabla \phi.
\]
\[
\boxed{\nabla \cdot (\phi \mathbf{u}) = \phi \, (\nabla \cdot \mathbf{u}) + \mathbf{u} \cdot \nabla \phi}
\]
Consider designing a linear binary classifier \( f(x) = \text{sign}(w^T x + b), x \in \mathbb{R}^2 \) on the following training data: 
Class-2: \( \left\{ \left( \begin{array}{c} 0 \\ 0 \end{array} \right) \right\} \)
Hard-margin support vector machine (SVM) formulation is solved to obtain \( w \) and \( b \). Which of the following options is/are correct?



