Fix emphasis vs definitions in odu and opt_transport

Changes per #721:
- reservation wage (odu.md)
- reservation wage functional equation (odu.md)
- matrix (opt_transport.md)
- vector (opt_transport.md)

All terms changed from italic to bold as they are definitions per style guide.

Author: mmcky

lectures/odu.md

@@ -111,7 +111,7 @@ v(w)
 ```
 
 The optimal policy has the form $\mathbf{1}\{w \geq \bar w\}$, where
-$\bar w$ is a constant called the *reservation wage*.
+$\bar w$ is a constant called the **reservation wage**.
 
 ### Offer Distribution Unknown
 
@@ -545,7 +545,7 @@ and using $\circ$ for composition of functions yields
 
 Equation {eq}`odu_mvf4` can be understood as a functional equation, where $\bar w$ is the unknown function.
 
-* Let's call it the *reservation wage functional equation* (RWFE).
+* Let's call it the **reservation wage functional equation** (RWFE).
 * The solution $\bar w$ to the RWFE is the object that we wish to compute.
 
 ## Solving the RWFE

lectures/opt_transport.md

@@ -136,12 +136,12 @@ tackle the optimal transport problem.
 
 ### Vectorizing a Matrix of Decision Variables
 
-A *matrix* of decision variables $x_{ij}$ appears in problem {eq}`plannerproblem`.
+A **matrix** of decision variables $x_{ij}$ appears in problem {eq}`plannerproblem`.
 
-The SciPy function `linprog` expects to see a *vector* of decision variables.
+The SciPy function `linprog` expects to see a **vector** of decision variables.
 
 This situation impels us to rewrite our problem in terms of a
-*vector* of decision variables.
+**vector** of decision variables.
 
 Let