When working with optimization problems in Julia, it is often necessary to determine the shadow price of constraints. The shadow price represents the change in the objective function value for a small change in the right-hand side of a constraint. In this article, we will explore three different ways to calculate the shadow price of constraints in Julia using the Jump package.
Option 1: Dual values
The first option to calculate the shadow price of constraints is by using the dual values. The dual values represent the marginal value of each constraint in the optimization problem. To obtain the dual values, we need to solve the optimization problem and access the dual values of the constraints.
using JuMP, GLPK
# Create a model
model = Model(GLPK.Optimizer)
# Define variables and constraints
@variable(model, x >= 0)
@variable(model, y >= 0)
@constraint(model, 2x + 3y <= 10)
@constraint(model, x + y <= 5)
# Define objective function
@objective(model, Max, 4x + 5y)
# Solve the optimization problem
optimize!(model)
# Access dual values
shadow_price_1 = dual.(model.constraints)
In this code snippet, we create a model using the GLPK optimizer and define variables, constraints, and the objective function. After solving the optimization problem, we can access the dual values of the constraints using the `dual` function. The `shadow_price_1` variable will contain the shadow prices of the constraints.
Option 2: Reduced costs
The second option to calculate the shadow price of constraints is by using the reduced costs. The reduced costs represent the change in the objective function value for a small increase in the right-hand side of a constraint. To obtain the reduced costs, we need to solve the optimization problem and access the reduced costs of the variables.
using JuMP, GLPK
# Create a model
model = Model(GLPK.Optimizer)
# Define variables and constraints
@variable(model, x >= 0)
@variable(model, y >= 0)
@constraint(model, 2x + 3y <= 10)
@constraint(model, x + y <= 5)
# Define objective function
@objective(model, Max, 4x + 5y)
# Solve the optimization problem
optimize!(model)
# Access reduced costs
shadow_price_2 = -reduced_costs(model)
In this code snippet, we create a model using the GLPK optimizer and define variables, constraints, and the objective function. After solving the optimization problem, we can access the reduced costs of the variables using the `reduced_costs` function. The `shadow_price_2` variable will contain the shadow prices of the constraints.
Option 3: Sensitivity analysis
The third option to calculate the shadow price of constraints is by performing a sensitivity analysis. The sensitivity analysis allows us to determine the impact of changes in the right-hand side of a constraint on the optimal solution. To perform a sensitivity analysis, we need to solve the optimization problem and access the sensitivity information.
using JuMP, GLPK
# Create a model
model = Model(GLPK.Optimizer)
# Define variables and constraints
@variable(model, x >= 0)
@variable(model, y >= 0)
@constraint(model, 2x + 3y <= 10)
@constraint(model, x + y <= 5)
# Define objective function
@objective(model, Max, 4x + 5y)
# Solve the optimization problem
optimize!(model)
# Perform sensitivity analysis
shadow_price_3 = shadow_prices(model)
In this code snippet, we create a model using the GLPK optimizer and define variables, constraints, and the objective function. After solving the optimization problem, we can perform a sensitivity analysis using the `shadow_prices` function. The `shadow_price_3` variable will contain the shadow prices of the constraints.
After exploring these three options, it is clear that the best option to calculate the shadow price of constraints in Julia using the Jump package is Option 1: Dual values. This option directly provides the dual values, which represent the marginal value of each constraint. The other options require additional calculations or sensitivity analysis. Therefore, Option 1 is the most straightforward and efficient way to obtain the shadow prices of constraints in Julia.
