In the context of linear programming and optimization, shadow prices (also known as dual prices or dual variables) are an essential concept associated with the constraints of the problem. When solving a linear programming problem, the shadow price of a particular constraint represents the rate of change in the optimal objective function value for a unit increase in the right-hand side (RHS) of that constraint, assuming all other constraints and variables remain unchanged.
The shadow price provides valuable information about the sensitivity of the optimal solution with respect to changes in the constraint coefficients. It helps answer questions such as:
- Resource Value: What is the marginal value of an additional unit of a particular resource (e.g., labor, material, etc.) in terms of the objective function?
- Resource Availability: How much the objective function value will change if more or less of a specific resource is available?
- Scarcity of Resources: How much would you be willing to pay to obtain additional units of a scarce resource?
The shadow price is particularly useful in situations where resources have limited availability, and decision-makers want to understand the impact of constraints on the objective function.
In a standard linear programming problem, after solving it using techniques like the simplex method, shadow prices can be found for each constraint in the final solution. If the problem is a maximization problem, the shadow price of a “≤” constraint is usually non-negative, while the shadow price of a “≥” constraint is usually non-positive. For a minimization problem, this relationship is reversed.
It’s important to note that the shadow price is only valid within a certain range of feasibility for the constraint (i.e., within the range of non-negativity or non-positivity). If a constraint becomes binding (active) at the optimal solution, its shadow price remains valid and provides meaningful information. However, if a constraint is not binding (slack or surplus), its shadow price becomes meaningless.
Shadow prices are a powerful tool for decision-makers to gain insights into the value and impact of resources on the optimal solution and to make informed decisions about resource allocation and management.
Identification of unique and Multiple optimal solutions
In linear programming, the presence of unique or multiple optimal solutions can have implications on the shadow prices associated with the constraints. Let’s explore how shadow prices are affected in these cases:
Unique Optimal Solution:
When a linear programming problem has a unique optimal solution, it means that there is only one feasible solution that optimizes the objective function. In such cases, each constraint will have a unique shadow price at the optimal solution. The shadow price of a constraint provides the rate of change in the optimal objective function value with respect to a one-unit increase in the constraint’s right-hand side.
Multiple Optimal Solutions:
When a linear programming problem has multiple optimal solutions, it means that there are two or more feasible solutions that achieve the same optimal objective function value. In this scenario, the shadow prices for certain constraints may have a range of valid values rather than a single unique value.
- Binding Constraints: Constraints that are active (binding) at all optimal solutions have unique shadow prices. These constraints determine the optimal solution and have non-zero shadow prices.
- Non-Binding Constraints: Constraints that are inactive (non-binding) at one or more optimal solutions have shadow prices that may take on a range of values, including zero. These constraints do not affect the optimal solution and can have zero shadow prices.
The concept of “Complementary slackness” is important in understanding shadow prices for multiple optimal solutions. Complementary slackness states that for any constraint in the problem, either its shadow price is zero (if the constraint is non-binding) or its slack (difference between the left-hand side and right-hand side) is zero (if the constraint is binding). This means that for non-binding constraints, the shadow price is zero, and for binding constraints, the slack is zero.
Unbounded Solution:
An unbounded solution occurs when a linear programming problem has no finite optimal solution, and the objective function can be made arbitrarily large (in maximization problems) or arbitrarily small (in minimization problems) without violating any of the constraints. This typically happens when the feasible region is unbounded, meaning it extends infinitely in one or more directions. In such cases, there is no optimal solution, and the objective function can be improved without bound.
Example of an unbounded solution in a maximization problem:
Maximize: 2x + y
Subject to:
x >= 0
y >= 0
x + y <= 1
The feasible region in this case is a triangle with vertices at (0, 0), (1, 0), and (0, 1). Since the objective function’s coefficients are positive, the solution can be improved indefinitely by increasing both x and y while staying within the feasible region.
Infeasibility:
Infeasibility occurs when there is no feasible solution that satisfies all the constraints of the linear programming problem. In other words, the constraints are mutually contradictory or incompatible, and it is impossible to find a point that simultaneously satisfies all of them. An infeasible problem has an empty feasible region.
Example of an infeasible problem:
Maximize: 3x + 2y
Subject to:
x + y <= 3
x + y >= 5
These constraints define two parallel lines with opposite directions. There is no feasible solution that satisfies both constraints, and thus, the problem is infeasible.
Degeneracy:
Degeneracy occurs in linear programming when the number of active constraints at an optimal solution is less than the number of variables in the problem. In such cases, the problem may have more than one optimal solution, and the basic variables may not be unique.
Degeneracy often happens when using the simplex method for solving linear programming problems. It can slow down the convergence of the algorithm and affect the efficiency of finding the optimal solution.
Example of a degenerate problem:
Maximize: 4x + 3y
Subject to:
2x + y <= 10
3x + 1.5y <= 15
4x + 2y <= 20
The feasible region in this case is a convex polygon with three vertices. The optimal solution lies at one of the vertices, and the problem is degenerate if the simplex method revisits the same basic solution multiple times.
Understanding these concepts is essential for successfully solving linear programming problems and interpreting the results correctly. Unboundedness, infeasibility, and degeneracy may require adjustments to the problem formulation or the use of specialized algorithms to handle these scenarios appropriately.