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How to Use Cutting Plane Method to Solve Integer Programming Problems

September 06, 2023
Dr. O'Donnell
Dr. O'Donnell
United States Of America
Discrete Math
Dr. O'Donnell has been captivated by the elegance and practicality of discrete mathematics and optimization. She has dedicated her research to developing innovative solutions for challenging combinatorial problems.

In the realm of optimization, integer programming (IP) is a powerful tool used to tackle a wide range of decision-making problems. Unlike linear programming, which deals with continuous variables, integer programming allows variables to take on only integer values, making it the ideal choice to solve your discrete math assignment. While this seemingly small restriction adds significant complexity to the optimization process, it also opens the door to solving problems that cannot be efficiently addressed through other techniques.

Solving integer programming problems can be challenging due to their combinatorial nature, where you're dealing with discrete decisions. Traditional methods often involve searching through an exponentially growing solution space, making them impractical for large-scale problems. Enter the "Cutting Plane Method," a sophisticated algorithm designed to help crack the toughest integer programming nuts.

In this blog, we'll delve deep into the Cutting Plane Method, exploring its fundamentals, applications, and some advanced variations. By the end, you'll have a comprehensive understanding of how this technique revolutionizes the way we approach complex optimization problems.

How to Use Cutting Plane Method to Solve Integer Programming Problems

Understanding Integer Programming

Before we dive into the Cutting Plane Method, let's first ensure we have a solid grasp of what integer programming is all about.

Integer Programming (IP): IP is a mathematical optimization technique where the decision variables are restricted to taking only integer values. This type of programming is commonly used when modeling real-world problems that involve discrete choices or quantities, such as selecting the number of items to produce or determining the assignment of tasks to workers.

Mathematically, an integer programming problem can be defined as follows:

Minimize (or Maximize):cTx

Subject to:Ax≤bxi∈Z, for i∈{1,2,…,n}


  • c is the vector of coefficients representing the objective function.
  • x is the vector of decision variables.
  • A is the constraint matrix.
  • b is the vector of constraint coefficients.
  • xi is an integer variable for
  • i in the set of decision variables.

Solving such problems can be incredibly challenging, as the discrete nature of the variables can lead to an astronomical number of possible solutions. This is where the Cutting Plane Method comes into play.

Introducing the Cutting Plane Method

The Cutting Plane Method is an optimization algorithm used primarily for solving integer programming problems. Developed as an extension of the branch-and-bound method, this technique aims to reduce the solution space by iteratively adding cutting planes, also known as "valid inequalities," to tighten the bounds on the optimal solution.

The fundamental idea behind the Cutting Plane Method is to start with a relaxation of the integer programming problem—essentially, a continuous version of the problem where variables can take on fractional values. The method then adds constraints to eliminate fractional solutions that are not part of the optimal integer solution.

The algorithm operates iteratively, with each iteration involving the following steps:

  1. Solve the relaxed problem:
  2. The first step in the Cutting Plane Method involves solving the "relaxed" version of the integer programming problem. This relaxed problem is essentially a linear programming (LP) problem where the integrality constraints (xi∈Z) are ignored, allowing the decision variables xi) to take on fractional values. Solving this relaxed problem provides us with a lower bound on the optimal integer solution.

    Why is this important? By solving the LP relaxation, we obtain a solution that is guaranteed to be at least as good as the optimal integer solution. In other words, this solution represents the best possible outcome if we allow the decision variables to take fractional values. This lower bound is crucial because it helps us understand the gap between the best fractional solution and the true integer optimum. It also serves as a benchmark against which we can compare subsequent iterations of the algorithm.

  3. Check for integer feasibility:
  4. After solving the relaxed problem, we need to determine whether the solution satisfies the integrality constraints. In other words, we check if all decision variables (xi) in the solution are integer values. If they are, then we have found an integer-feasible solution, and the algorithm can terminate. This is because an integer-feasible solution is also an optimal integer solution in this context.

    Why is this important? Confirming integer feasibility is crucial because it tells us whether we have reached the optimal integer solution. If the solution is already integer-feasible, there's no need to continue the algorithm, and we can confidently report the solution as the optimum.

  5. Identify a violated constraint:
  6. If the solution from the relaxed problem is not integer-feasible (i.e., it contains fractional values for some decision variables), we move on to the next step. Here, the goal is to identify a constraint that is violated by the fractional solution. In most cases, this violated constraint will be a valid inequality known as a "cutting plane."

    Why is this important? Identifying a violated constraint (cutting plane) is critical because it helps us tighten the feasible region of the problem. By adding this constraint to the problem, we effectively eliminate the fractional solution and restrict the search space to a more promising region. This is a key mechanism for progressively converging towards the true integer optimum.

  7. Add the cutting plane:
  8. Having identified a cutting plane (i.e., a constraint that was violated by the fractional solution), we add this constraint to the problem formulation. This has the effect of narrowing down the feasible region of the problem, making it more likely to contain the integer optimum.

    Why is this important? Adding the cutting plane is a strategic move in the algorithm. It improves the problem's formulation by incorporating new information that tightens the bounds on the solution space. Essentially, we're making the problem more reflective of the true nature of integer solutions.

  9. Repeat:
  10. After adding the cutting plane, we return to the first step and solve the relaxed problem with the updated set of constraints. We continue this iterative process until one of two conditions is met:

    • We find an integer-feasible solution. In this case, we can terminate the algorithm and report the solution as the optimal integer solution.
    • A termination condition is met. This condition could be based on time, computational resources, or a predetermined number of iterations. If this condition is met without finding an integer-feasible solution, we may conclude that the problem is infeasible or that further optimization is currently impractical.

Why is this important? The iterative nature of the Cutting Plane Method is what allows it to progressively refine the solution space and converge towards the optimal integer solution. By repeatedly solving the relaxed problem, identifying violated constraints, and adding cutting planes, we systematically approach the true solution while leveraging the lower bounds obtained in each iteration.

In summary, the Cutting Plane Method is a systematic and powerful approach for solving integer programming problems. It starts by relaxing the integrality constraints to obtain a lower bound, then iteratively tightens the problem formulation by identifying and adding violated constraints (cutting planes). This process continues until an integer-feasible solution is found or a termination condition is met. Through this series of steps, the algorithm efficiently navigates the complex solution space of integer programming problems, ultimately delivering optimal solutions to challenging decision-making scenarios.

Applications of the Cutting Plane Method

The Cutting Plane Method has a wide range of applications across various industries and fields. Here are some notable areas where it is frequently employed:

  1. Production Planning and Scheduling
  2. In manufacturing, optimizing production schedules often involves discrete decisions, such as determining production quantities and scheduling order releases. The Cutting Plane Method can help find optimal schedules while considering constraints like resource availability and production capacity.

  3. Network Design
  4. When designing networks, such as telecommunication or transportation networks, decisions regarding the placement of facilities, routing, and capacity allocation are often discrete in nature. The Cutting Plane Method aids in solving network design problems efficiently.

  5. Vehicle Routing
  6. For logistics and delivery companies, the Vehicle Routing Problem (VRP) is a classic example of an integer programming problem. The goal is to determine optimal routes for a fleet of vehicles to serve a set of customers while minimizing costs. The Cutting Plane Method can be applied to tackle VRP variations.

  7. Portfolio Optimization
  8. In finance, portfolio optimization involves selecting a mix of assets to maximize returns while managing risk. Integer programming models can handle constraints on asset allocations, and the Cutting Plane Method can be used to find the optimal portfolio composition.

  9. Airline Crew Scheduling
  10. Airlines face the complex task of scheduling flight crews while adhering to various rules and regulations. The Cutting Plane Method helps find solutions that meet these constraints while minimizing costs.

  11. Cutting Stock Problems
  12. In industries like paper manufacturing and metal cutting, the Cutting Stock Problem involves finding the most efficient way to cut raw materials into smaller pieces to fulfill customer orders. This optimization problem is a natural fit for the Cutting Plane Method.

    These are just a few examples, but the Cutting Plane Method's versatility allows it to be applied to a broad spectrum of integer programming problems in diverse domains.

Advanced Variations and Enhancements

The basic Cutting Plane Method we've discussed is a powerful technique on its own, but over the years, researchers and practitioners have developed several advanced variations and enhancements to further improve its performance. Let's explore a few of these:

  1. Branch-and-Cut
  2. The Branch-and-Cut method combines the Cutting Plane Method with the branch-and-bound technique. In each iteration, it divides the problem into smaller subproblems (branching) and uses cutting planes to tighten the bounds on these subproblems. This combination often leads to more efficient convergence to the optimal solution.

  3. Integer Cuts
  4. Integer cuts are special cutting planes added to the problem to eliminate fractional solutions. These cuts are generated based on properties specific to integer programming problems. Examples include Gomory cuts and mixed-integer rounding cuts, which target integer-feasible solutions.

  5. Lift-and-Project
  6. The Lift-and-Project method is a more advanced approach that generates cutting planes by lifting fractional solutions to higher-dimensional spaces. This can lead to tighter bounds and faster convergence for some integer programming problems.

  7. Dynamic Cutting Planes
  8. Dynamic cutting plane techniques adaptively generate cutting planes during the solution process based on the current state of the problem. This can significantly reduce the number of iterations required to reach an optimal solution.

  9. Heuristic Methods
  10. In cases where finding the exact optimal solution is computationally infeasible, heuristic methods can be combined with the Cutting Plane Method to quickly obtain near-optimal solutions. Metaheuristics like genetic algorithms or simulated annealing are often employed for this purpose.

Challenges and Considerations

While the Cutting Plane Method is a powerful tool for solving integer programming problems, it's not without its challenges and considerations:

  1. Computational Complexity: The method can still be computationally intensive, especially for large-scale problems. Finding the optimal solution may require a significant amount of computing resources and time.
  2. Model Formulation: Careful model formulation is crucial. The choice of variables, constraints, and objective functions can greatly impact the efficiency and effectiveness of the Cutting Plane Method.
  3. Solver Selection: The choice of optimization software or solver can impact the performance of the method. Different solvers may have varying capabilities and strengths for different types of integer programming problems.
  4. Termination Criteria: Deciding when to terminate the algorithm is important. Setting termination criteria that balance optimality with computation time is a challenging task.


The Cutting Plane Method is a powerful algorithmic technique for solving integer programming problems. By iteratively adding cutting planes to tighten the bounds on the optimal solution, this method has the capacity to address complex optimization problems in various domains, from production planning to network design and beyond.

Understanding the fundamentals of the Cutting Plane Method, its applications, and the advanced variations and enhancements available can empower practitioners and researchers to tackle challenging decision-making problems efficiently. However, it's essential to remember that while the Cutting Plane Method is a valuable tool, it's not a one-size-fits-all solution, and careful problem formulation and solver selection are critical to achieving success.

As technology continues to advance and computational resources become more accessible, the Cutting Plane Method, along with its variations, will likely play an increasingly pivotal role in solving real-world optimization challenges, helping us make better decisions in complex, discrete scenarios.

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