Logging
Logging related parameters and functions are essential for users to control the display of solving logs of COPT. This chapter provides an interpretation of loggings for different algorithms, including the following sections:
Parameters and Functions for Logging
Users can control the display of logs by setting related parameters.
Logging
Integer parameter.
Whether to print optimization logs.
Default: 1
Possible values:
0: No optimization logs.
1: Print optimization logs.
LogToConsole
Integer parameter.
Whether to print optimization logs to console.
Default: 1
Possible values:
0: No optimization logs to console.
1: Print optimization logs to console.
COPT provides operations related to logs, such as setting the logging file.
COPT provides functions to set the logging file, write the optimization logs into the specified file (with the file name suffix .log
), allowing users to save and review the logs. The functions for different programming interfaces are shown in Table 27:
Programming Interface 
Functions for setting logging files 

C 

C++ 

C# 

Java 

Python 

Note: When calling these functions, users should specify the file name for saving logs using the logfile
parameter.
Basic Information
COPT outputs basic information before starting the solving process, depending on the problem types. The following information is typically displayed:
Model fingerprint: 2c27ab28
Hardware has 64 cores and 128 threads. Using instruction set X86_AVX512_E1 (14)
Minimizing a MIP problem
Here, Model fingerprint
represents the unique code for the current model.
The next output provides information about the hardware used, including the number of CPU cores (cores
) and threads (threads
).
Finally, the problem type and optimization sense are displayed, such as:
Minimizing an LP problem
, Minimizing a MIP problem
, and Minimizing an SDP problem
, etc.
Simplex Method Logging
Based on different stages of the optimization process, the logs for Simplex Method can be divided into three parts:
Presolve
Simplex iteration process
Summary of optimization results
This section uses the logs for example problem afiro.mps to interpret the information in the Simplex Method logging.
Presolve
By default, before starting the Simplex Method, the COPT performs presolve on the model to improve its quality. A preprocessed model will be transmitted to the solver.
The presolve part of the log outputs changes in the model size before and after presolve:
The original problem has:
27 rows, 32 columns and 83 nonzero elements
The presolved problem has:
7 rows, 10 columns and 28 nonzero elements
The description of the LP problem size includes the following information:
Number of constraints (
rows
)Number of variables (
columns
)Number of nonzero elements in the coefficient matrix (
nonzero elements
)
In the example logs, after presolve, the number of constraints, variables, and nonzero elements in the coefficient matrix are all reduced.
Simplex iteration process
This part of the logs provides relevant information about the iteration process using the Simplex method.
Starting the simplex solver using up to 8 threads
Method Iteration Objective Primal.NInf Dual.NInf Time
Dual 0 4.8553789460e+02 3 0 0.00s
Dual 3 4.6476735494e+02 0 0 0.00s
Postsolving
Dual 3 4.6475314286e+02 0 0 0.00s
Here, the first line indicates that the current optimization algorithm is the Simplex method, and it uses 8 threads (threads
) for computation.
The subsequent lines represent the simplex iteration process with 6 columns:
Method
: The optimization algorithm used, where"Dual"
represents the dual simplex method.Iteration
: The number of iterations.Objective
: The objective function value.Primal.NInf
: The number of primal infeasibilities in the primal problem.Dual.NInf
: The number of dual infeasibilities in the dual problem.Time
: The time taken for solving (in seconds).
Summary of Optimization Results
This part of the logs summarizes the results and the iteration process of the Simplex method after completing the solving process.
Solving finished
Status: Optimal Objective: 4.6475314286e+02 Iterations: 3 Time: 0.00s
The included information consists of:
Solving status (
Status
): If the model has an optimal solution, it isOptimal
.Objective function value (
Objective
): If the model has an optimal solution,Objective
displays the optimal objective function value.Total number of iterations (
Iterations
).Total solving time (
Time
).
If the model is infeasible, the corresponding log output is as follows:
Solving finished
Status: Infeasible Objective:  Iterations: 2 Time: 0.00s
Barrier Method Logging
Based on different stages of the optimization process, the solving logs for the Barrier Method can be divided into three parts:
Presolve
Barrier optimization process
Summary of Optimization Results
Note: By setting the optimization parameter "LpMethod = 2"
, you can choose the Barrier method as the algorithm for solving linear programming problems.
Similarly, using the example problem afiro.mps, this section interprets the information in the logs for solving LP problems with the Barrier method.
Presolve
By default, before starting the Barrier Method, the COPT performs presolve to reduce the model size. A preprocessed model will be transmitted to the solver.
The presolve part of the log outputs changes in the model size before and after presolve:
The original problem has:
27 rows, 32 columns and 83 nonzero elements
The presolved problem has:
7 rows, 10 columns and 28 nonzero elements
Model Information
This part of the log presents relevant numerical information about the model:
Starting barrier solver using 64 threads
Problem info:
Dualized in presolve: No
Range of matrix coefficients: [4e01,4e+00]
Range of rhs coefficients: [8e+01,3e+02]
Range of bound coefficients: [4e+01,1e+02]
Range of cost coefficients: [2e01,2e+00]
Factor info:
Number of free columns: 0
Number of dense columns: 0
Number of matrix entries: 2.800e+01
Number of factor entries: 2.800e+01
Number of factor flops: 1.140e+02
Here, the first line indicates that the current optimization algorithm is Barrier Method, and it uses 64 threads (threads
). The information includes:
Problem info
output, which includes: whether the dualized model is solved, the range of matrix coefficients, the range of RHS coefficients, the range of bound coefficients for constraint variables, and the range of cost coefficients for the objective function.Factor info
output, which includes: the number of free columns, the number of dense columns, information related to the linear system matrix factorization.
BranchandCut Method Logging
Based on different stages of the optimization process, the BranchandCut logging can be divided into three parts:
Presolve
BranchandCut Search Process
Summary of Results
Here, we’ll use the example of cutstock.mps.gz in the “/examples/data” directory of COPT installation package to interpret the information of the MIP log,
Presolve
To simplify the model, the COPT solver performs presolve on the MIP model to eliminate redundant constraints or variable ranges. The information presented in the Presolve log includes the changes in model size before and after presolving.
The original problem has:
404 rows, 1200 columns and 2598 nonzero elements
200 binaries and 1000 integers
Presolving the problem
The presolved problem has:
373 rows, 1169 columns and 2505 nonzero elements
369 binaries and 800 integers
The description of MIP problem size includes
the number of constraints (rows).
the number of variables (columns).
nonzero elements in the coefficient matrix (nonzero elements).
the number of binary variables (binaries).
the number of integer variables (integers).
Notes
The variable count (cols) here includes all types of variables in the model, including continuous variables, integer variables, and 01 variables.
In theory, 01 variables are a special case of integer variables, but they are counted separately in this context.
In the provided log example, the problem size (number of constraints and variables) is reduced after presolving.
BranchandCut Search Process
This section of the log provides information about the BranchandCut search process.
Starting the MIP solver with 8 threads and 32 tasks
Nodes Active LPit/n IntInf BestBound BestSolution Gap Time
0 1  0 3.100000e+01  Inf 0.05s
H 0 1  0 3.100000e+01 6.800000e+01 54.4% 0.70s
H 0 1  0 3.100000e+01 6.600000e+01 53.0% 0.70s
H 0 1  0 3.100000e+01 6.500000e+01 52.3% 0.71s
0 1  86 5.591304e+01 6.500000e+01 14.0% 0.72s
H 0 1  86 5.591304e+01 6.200000e+01 9.82% 0.74s
1 2 0.0 86 5.591304e+01 6.200000e+01 9.82% 0.76s
H 1 1 2129 6 6.000000e+01 6.000000e+01 0.00% 0.87s
2 0 1064 6 6.000000e+01 6.000000e+01 0.00% 0.87s
2 0 1064 6 6.000000e+01 6.000000e+01 0.00% 0.87s
Note: The presented log content is a partial representation of the optimization process due to space limitation, aiming to facilitating the interpretation of log content.
The BranchandCut search log can be divided into three parts based on the meaning of each information item, and we’ll interpret each part separately:
Node search information (columns 14)
Feasible solution interval information (columns 57)
Solving time information (column 8)
Node search information (columns 14):
Nodes
: Number of nodes searched.Active
: Number of leaf nodes yet to be searched.LPit/n
: Average number of Simplex iterations per node.IntInf
: Number of integer variables not taking integer values in the current linear relaxation (LP relaxation) solution.
Feasible solution interval information (columns 57):
BestBound
: Current best objective bound.BestSolution
: Current best objective function value.Gap
: Relative gap between upper and lower bounds. If less than the specified RelGap parameter, the solving process will stop.
Solving time information (column 8):
Time
: Time taken for solving.
Notes
The markers (
H/*
) beforeNodes
in the first column indicate the discovery of a new feasible solution.H
: Found through heuristics.*
: Found by solving subproblems through branching.
Sometimes,
Nodes
stays 0 for an extended period, indicating that COPT is processing the root node. At the root node, COPT generates cutting planes and attempts various heuristic methods to obtain the optimal feasible solution, aiming to reduce the subsequent search scope.
Summary of Results
This part summarizes the final solving status of the MIP problem and the search process of the BranchandCut method, including the model’s solution and the workload of the solving process.
Best solution : 60.000000000
Best bound : 60.000000000
Best gap : 0.0000%
Solve time : 0.87
Solve node : 2
MIP status : solved
Solution status : integer optimal (relative gap limit 0.0001)
Violations : absolute relative
bounds : 0 0
rows : 0 0
integrality : 0
Results:
Best objective function value (
Best solution
).Best lower bound (
Best bound
).Best gap (
Best gap
).Solving status (
Solution status
).
Solving Workload:
Solving time (
Solve time
).Number of nodes searched (
Solve node
).
The section Violations
indicates the extent to which the optimal solution satisfies the model’s constraints and variable ranges. This includes conflict values for variables (bounds
) and constraints (rows
), as well as conflicts for integer solutions (integrality).
Logging for Firstorder method (PDLP) of GPU solver
For Linear Programming Problems, if the PDLP method is selected (setting the parameter LpMethod=6
), GPU solver can be enabled. (The machine needs the compatible GPU and the necessary CUDA library needs to be configured).
The logging for GPU solver can be divided into the following sections, which are slightly different from the CPU solver, with the main distinction in the second section:
Presolve section
GPU hardware information of the machine
Firstorder method PULP iteration process
Crossover section
Summary of optimization results
Taking the "thk_63"
instance from public LP benchmark as an example, the following is the solving log for the GPU solver.
GPU hardware information of the machine
This section outputs information about the current machine’s GPU information and CUDA version.
Hardware has 1 supported GPU device with CUDA 12.3
GPU 0: NVIDIA GeForce RTX 4090 (CUDA capability 8.9)
Notes
The
"CUDA 12.3"
mentioned in the log refers to the highest version of the CUDA Toolkit supported by the currently installed CUDA driver.COPT’s GPU solver requires a minimum CUDA Toolkit version of 11.7 (for Linux systems, the corresponding minimum CUDA Driver version is 525.60.13; for Windows systems, the corresponding minimum CUDA Driver version is 527.41). It is recommended to directly install CUDA version 12.0 or higher. If you need to use CUDA Toolkit 11.7, please install CUDA Toolkit and CUDA Driver separately. For details, please refer to the FAQ section: GPU Usage.
3. Firstorder method PULP iteration process
This section outputs the solving iteration process of the PDLP method, and the summary after the completion, including the iteration count of PDLP, and the optimal objective values, dual gaps for primal and dual problems.
Starting PDLP solver on GPU 0
Iterations Primal.Obj Dual.Obj Gap Primal.Inf Dual.Inf Time
0 +6.00000000e+00 +6.00000000e+00 +0.00e+00 7.87e+00 0.00e+00 21.63s
4000 +1.95436674e+03 1.77166004e+03 +3.73e+03 2.09e02 0.00e+00 33.37s
8000 +1.90433201e+03 +1.55817851e+03 +3.46e+02 1.51e02 0.00e+00 44.75s
12000 +1.87801607e+03 +1.85689627e+03 +2.11e+01 1.74e02 0.00e+00 56.27s
16000 +1.86810632e+03 +1.86897715e+03 +8.71e01 4.92e03 0.00e+00 67.72s
20000 +1.87022842e+03 +1.86994685e+03 +2.82e01 3.42e03 0.00e+00 79.18s
23640 +1.87099459e+03 +1.87099144e+03 +3.15e03 4.69e05 0.00e+00 89.68s
PDLP status: OPTIMAL
PDLP iterations: 23640
Primal objective: 1.87099459e+03
Dual objective: 1.87099144e+03
Primal infeasibility (abs/rel): 4.69e05 / 6.20e07
Dual infeasibility (abs/rel): 0.00e+00 / 0.00e+00
Duality gap (abs/rel): 3.15e03 / 8.43e07
Experimental: using crossover to find a basic solution after PDLP
Please set parameter Crossover to 0 if the basic solution is not required
Please set parameter PDLPTol to a smaller value if the crossover cleanup takes too long
Starting crossover using up to 8 threads
50320 primal pushes remaining 92.09s
12495 primal pushes remaining 97.71s
4124 primal pushes remaining 102s
202 primal pushes remaining 104s
0 primal pushes remaining 104s
1480858 dual pushes remaining 104s
589582 dual pushes remaining 106s
0 dual pushes remaining 107s
Method Iteration Objective Primal.NInf Dual.NInf Time
Dual 986011 1.8710000000e+03 0 0 107.97s
Postsolving
Dual 986011 1.8710000000e+03 0 0 110.56s
Unfolding
Dual 1036742 1.8710000000e+03 0 0 157.25s
Solving finished
Status: Optimal Objective: 1.8710000000e+03 Iterations: 1036742 Time: 157.69s
Note: After solving with a Firstorder method (PDLP), if the solution reaches optimality (Status: Optimal
), the default behavior is to perform the Crossover process to the basic solution. This process can also be disabled by setting the parameter Crossover
to 0.