Final answer:
Shadow prices cannot be calculated without additional information from the solving process of the LP, such as simplex tableau or sensitivity analysis. Shadow prices indicate the potential increase in the objective function per unit increase in a constraint's right-hand side.
Step-by-step explanation:
To calculate the shadow prices for the constraints of the linear programming problem, we need to look at the constraints at the optimal solution. The shadow price, also known as the dual value, represents the change in the objective function per unit increase in the right-hand side of the constraint while keeping other constraints the same.
Since we don't have the complete output from a simplex or similar optimization method, we cannot calculate the exact shadow prices. Shadow prices are usually obtained from simplex tableau or sensitivity analysis that comes with solving LPs.
The general idea is that for each binding constraint at the optimal solution, the shadow price tells us how much the objective function value will increase if we relax that constraint by one unit. For non-binding constraints (those that are not met with equality at the optimal solution), the shadow price is typically zero as slack is available.