Decision Variable

Definition

A decision variable is a key element in optimization problems that represents the choices available to the decision-maker. These variables are the unknowns that are determined within the model to optimize an objective function, while also satisfying certain constraints. Decision variables play a critical role in defining the feasible region of solutions and are directly tied to the outcomes of the optimization process.

5 Must Know Facts For Your Next Test

1. Decision variables can be continuous, integer, or binary, affecting how optimization methods are applied.
2. In linear programming, decision variables are typically represented as 'x' values in equations and inequalities.
3. The number of decision variables directly influences the complexity of the optimization model and its solution process.
4. Choosing appropriate decision variables is crucial because they need to capture all relevant aspects of the problem for effective modeling.
5. Sensitivity analysis can be used to understand how changes in decision variables impact the optimal solution and objective function.

Review Questions

• How do decision variables influence the formulation of an optimization problem?
  • Decision variables directly shape the structure of an optimization problem by defining what choices can be made. They determine the potential solutions within the feasible region and ultimately influence the value of the objective function. The selection and definition of these variables are critical because they must accurately reflect the real-world decisions that need to be optimized while adhering to any constraints present.
• Discuss how basic and non-basic variables relate to decision variables in the context of linear programming.
  • In linear programming, decision variables can be classified as basic or non-basic based on their roles in forming a solution at a vertex of the feasible region. Basic variables correspond to dimensions where constraints intersect, contributing actively to forming a basic feasible solution. Non-basic variables remain at their bounds (often set to zero) during this process. Understanding this distinction helps in identifying which decision variables will lead to optimal solutions during iterations of the simplex method.
• Evaluate the importance of decision variables in relation to the principle of optimality in dynamic programming.
  • Decision variables are fundamental to applying the principle of optimality in dynamic programming because they represent choices at each stage of a multi-stage decision process. The principle states that an optimal policy has the property that whatever the initial state and decision, subsequent decisions must also be optimal. By properly defining decision variables for each stage, it becomes possible to construct recursive equations that lead to optimal solutions, ensuring that each choice made at one stage is aligned with future optimal outcomes.

Related terms

Objective Function:

The function that needs to be maximized or minimized in an optimization problem, often dependent on the decision variables.

Constraints:

Conditions or limitations that must be satisfied within an optimization problem, often expressed as inequalities involving decision variables.

Feasible Region:

The set of all possible points that satisfy the constraints of an optimization problem, determining which values of decision variables are permissible.