Debugging is the process of finding and fixing errors or bugs in a computer program. In other words, it is the process of identifying and resolving issues that prevent the program from functioning as intended. Debugging is the process of identifying and resolving errors, bugs, or defects in computer software. Debugging is an important part of the software development life cycle, as it helps to ensure that the software functions correctly and meets the requirements of the end-user. Debugging can involve a range of techniques, including code reviews, unit testing, integration testing, and system testing. When an error or bug is found, the programmer must investigate the cause of the problem and then make the necessary changes to fix it. Debugging can be a time-consuming process, but it is essential for producing high-quality software that is reliable and error-free.

Debugging in mathematical optimization involves identifying and resolving errors or issues in the optimization model or algorithm that prevent it from finding a solution or producing unexpected or incorrect results.

During the optimization process, it’s common for errors or issues to arise due to mistakes in the model formulation or constraints, programming errors, or numerical issues. These issues can cause the solver to terminate prematurely or produce incorrect results, making it difficult to find the optimal solution.

Debugging techniques for optimization typically involve examining the optimization model and solver output to identify the source of the issue, and then modifying the model or solver parameters to address the issue. This may involve adjusting the optimization model to correct errors or remove redundancies, checking the data input to ensure it’s correct, and adjusting solver parameters to improve convergence or performance.

Debugging is an important part of the optimization process, as it helps to ensure the accuracy and reliability of the results. Without proper debugging, the optimization model may produce incorrect or unexpected results, leading to incorrect decisions and potentially costly errors.

Debug in Pulp

Pulp is a popular Python library used for linear optimization. It can be used to solve a wide range of optimization problems, including linear programming, integer programming, and mixed-integer programming. Here’s an example of debugging a pulp optimization problem:

Let’s say we want to optimize the production of two products, A and B, subject to certain constraints. We can use the pulp library to set up and solve the optimization problem. Here’s the code:

import pulp

# Create the optimization problem
prob = pulp.LpProblem("Product Optimization", pulp.LpMaximize)

# Define the decision variables
x1 = pulp.LpVariable("x1", lowBound=0, cat='Integer')
x2 = pulp.LpVariable("x2", lowBound=0, cat='Integer')

# Define the objective function
prob += 5*x1 + 4*x2, "Total Profit"

# Define the constraints
prob += 3*x1 + 4*x2 <= 25, "Constraint 1"
prob += 2*x1 + 3*x2 <= 18, "Constraint 2"
prob += x1 <= 5, "Constraint 3"

# Debugging print statements
print("Decision variables:")
print("x1 =", x1)
print("x2 =", x2)

print("Constraints:")
print(prob.constraints)

# Solve the optimization problem
prob.solve()

# Print the results
print("Status:", pulp.LpStatus[prob.status])
print("Total Profit = ", pulp.value(prob.objective))
print("x1 = ", pulp.value(x1))
print("x2 = ", pulp.value(x2))

= 25, "Constraint 1"
prob

When we run the program with this debugging code, we can see that the value of Constraint 1 is not being calculated correctly. This suggests that there is a problem with how we defined the constraint.

Gurobi solver debug

Compute the Ireducible Inconsistent Subsystem (IIS). IIS is a subset of constraint and variable bounds with the following properties:

• It is still impossible and
• Once one constraint or bound is removed, the subsystem becomes executable.

Note that there may be multiple IISs in the non-executable model. What Gurobi returns is not necessarily the smallest. There may be others with fewer constraints or bounds.

IIS results are returned in many of the attributes IISConstr, IISLB, IISUB, IIOS, IISQConstr, and IISGenConstr. Each indicates whether the corresponding model element is a computed IIS member.

IIS logs provide information about the progress of the algorithm, including final IIS size estimates.

If the IIS computation is interrupted before completion, Gurobi will return the smallest non-executing subsystem found at that time.

To use the debug in the gurobi solver we can use ilp file which is gurobi computeIIS file, here below is example code:

def debug(self) -> None:
    path = str(self.debug_dir / Path('Debug/'))
    model.write(path+'/debug.lp', io_options={'symbolic_solver_labels': True})
    model = gurobipy.read(path+'/debug.lp')
    model.params.NonConvex = 2
    model.computeIIS()
    model.write(path+'/debug.ilp')

The main process of debugging in mathematical optimization involves the following steps:

1. Examining the optimization model and solver output: The first step in debugging is to examine the optimization model and the output produced by the solver to identify any issues or errors. This includes checking the formulation of the model, the constraints, and the objective function, as well as examining the solver output to see if it terminates prematurely or produces unexpected results.
2. Identifying the source of the issue: Once an issue has been identified, the next step is to determine its source. This may involve analyzing the data input, checking for programming errors, or identifying numerical issues such as ill-conditioned matrices or numerical instability.
3. Modifying the model or solver parameters: After the source of the issue has been identified, the next step is to modify the optimization model or the solver parameters to address the issue. This may involve adjusting the objective function, adding or removing constraints, or changing the solver settings to improve convergence or performance.
4. Testing the modified model: Once the model or solver has been modified, it’s important to test the modified model to ensure that the issue has been resolved and that the results are accurate and reliable. This may involve running the optimization multiple times with different inputs or verifying the results against known solutions.
5. Repeating the process: If further issues are identified, the debugging process may need to be repeated, starting again with step 1 to identify and resolve the issue.

Overall, the process of debugging in mathematical optimization requires careful analysis and attention to detail, as well as a deep understanding of the optimization model and the solver algorithms. Through a systematic approach to debugging, it’s possible to identify and resolve issues, ensuring the accuracy and reliability of the optimization results.

Here’s an example Gurobi code for saving the solution of a model to a file:

import gurobipy as gp

# Create a Gurobi model
model = gp.Model()

# ... add variables and constraints to the model ...

# Optimize the model
model.optimize()

# Check if the model is optimized to optimality
if model.status == gp.GRB.OPTIMAL:
    # Save the solution to a file
    model.write("solution.sol")
else:
    print("No optimal solution found.")

Here’s an example Gurobi code for saving the log file of a model to a file:

import gurobipy as gp

# Create a Gurobi model
model = gp.Model()

# ... add variables and constraints to the model ...

# Set the output flag to 1 to generate a log file
model.setParam("OutputFlag", 1)

# Set the log file name
model.setParam("LogFile", "example.log")

# Optimize the model
model.optimize()

In this code, after the model is created, the setParam() method is used to set the OutputFlag parameter to 1, which generates a log file. The setParam() method is then used again to set the LogFile parameter to the name of the log file, “example.log”. Finally, the model is optimized and the log file is created with the specified name.

Note that the OutputFlag parameter can be set to 0 to turn off log generation, and the LogFile parameter can be set to an empty string to write the log to the console instead of a file.

Here’s an example Gurobi code for generating an MPS file:

import gurobipy as gp

# Create a Gurobi model
model = gp.Model()

# ... add variables and constraints to the model ...

# Set the output flag to 0 to turn off log generation
model.setParam("OutputFlag", 0)

# Set the method to use for generating the MPS file
model.setParam("Method", 0)

# Set the name of the MPS file
model.write("example.mps")

In this code, after the model is created, the setParam() method is used to set the OutputFlag parameter to 0, which turns off log generation. The setParam() method is also used to set the Method parameter to 0, which specifies the primal simplex method for generating the MPS file. Finally, the write() method is used to generate the MPS file with the specified name, “example.mps”.

Note that you can choose a different method for generating the MPS file by setting the Method parameter to a different value. You can also set additional parameters to customize the output format of the MPS file.

Here’s an example Gurobi code for generating an IIS (irreducible infeasible set) file:

import gurobipy as gp

# Create a Gurobi model
model = gp.Model()

# ... add variables and constraints to the model ...

# Optimize the model to find infeasibility
model.optimize()

# Check if the model is infeasible
if model.status == gp.GRB.Status.INFEASIBLE:
    # Generate the IIS file
    model.computeIIS()
    model.write("example.ilp")