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Python API Expression Class Precision

Quang Ngu
Gurobi-versary
First Comment
First Question

I have been creating a Python version of an application we have here and trying to match the results. I figured out a way to compare the .mps files generated from Python and MATLAB. I am absolutely sure I am giving both languages the same problem. The only differences in the .mps are these random "offset" terms.

For example, in Python, I see the following line:
UP BND1 XXXXXX-E 2.3390000000000004e-03

But in MATLAB, I see the following line:
UP BND1 XXXXXX-E 0.002339

These differences are randomly peppered through out the files. Some times its MATLAB that has the small perturbation and sometimes its Python.

Is this due to the difference in API? For Python, I use the expression class to build my constraints and objective function but in MATLAB, the constraints and objective function is passed as a sparse matrix. Are there floating point precision differences between using the expression class vs. the sparse matrix in MATLAB?

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  • Official comment
    Simranjit Kaur
    Gurobi Staff Gurobi Staff
    This post is more than three years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum. Or why not try our AI Gurobot?.
  • Quang Ngu
    Gurobi-versary
    First Comment
    First Question

    Hopefully this may provide some clues...I have the gurobi outputs from both Python and MATLAB below. Comparing output lines, I think I am giving the exact same problem.

    Python:

    Gurobi 8.1.1 (win64) logging started 12/03/19 15:34:42

    Changed value of parameter LogFile to python_gurobi.log
    Prev: gurobi.log Default:
    Parameter MIPFocus unchanged
    Value: 0 Min: 0 Max: 3 Default: 0
    Changed value of parameter MIPGap to 0.01
    Prev: 0.0001 Min: 0.0 Max: 1e+100 Default: 0.0001
    Changed value of parameter MIPGapAbs to 1e-06
    Prev: 1e-10 Min: 0.0 Max: 1e+100 Default: 1e-10
    Changed value of parameter TimeLimit to 300.0
    Prev: 1e+100 Min: 0.0 Max: 1e+100 Default: 1e+100
    Parameter NumericFocus unchanged
    Value: 0 Min: 0 Max: 3 Default: 0
    Optimize a model with 54127 rows, 53740 columns and 199891 nonzeros
    Model has 1 quadratic constraint
    Variable types: 33613 continuous, 20127 integer (20127 binary)
    Coefficient statistics:
    Matrix range [2e-05, 2e+00]
    QMatrix range [1e-03, 2e+04]
    QLMatrix range [3e-04, 3e+02]
    Objective range [1e+00, 1e+00]
    Bounds range [2e-03, 1e+00]
    RHS range [2e-03, 2e+02]
    QRHS range [2e+02, 2e+02]
    Presolve removed 51786 rows and 51543 columns
    Presolve time: 0.19s
    Presolved: 2341 rows, 2197 columns, 22964 nonzeros
    Variable types: 1123 continuous, 1074 integer (1074 binary)

    Root relaxation: objective 1.970000e+00, 973 iterations, 0.02 seconds

    Nodes | Current Node | Objective Bounds | Work
    Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time

    0 0 1.97000 0 4 - 1.97000 - - 0s
    0 0 1.97000 0 4 - 1.97000 - - 0s
    0 0 1.97000 0 2 - 1.97000 - - 0s
    0 0 1.97000 0 2 - 1.97000 - - 0s
    0 2 1.97000 0 2 - 1.97000 - - 0s
    H 75 7 1.9699989 1.97000 0.00% 102 1s
    * 75 7 2 1.9699989 1.97000 0.00% 102 1s

    Cutting planes:
    Gomory: 1
    Cover: 1
    MIR: 1
    Flow cover: 1

    Explored 84 nodes (9567 simplex iterations) in 1.43 seconds
    Thread count was 8 (of 8 available processors)

    Solution count 1: 1.97

    Optimal solution found (tolerance 1.00e-02)
    Warning: max constraint violation (1.1856e-06) exceeds tolerance
    Best objective 1.969998894384e+00, best bound 1.969998894384e+00, gap 0.0000%
    Optimize a model with 54127 rows, 53740 columns and 199891 nonzeros
    Model has 6106 quadratic objective terms
    Model has 1 quadratic constraint
    Variable types: 33613 continuous, 20127 integer (20127 binary)
    Coefficient statistics:
    Matrix range [2e-05, 2e+00]
    QMatrix range [1e-03, 2e+04]
    QLMatrix range [3e-04, 3e+02]
    Objective range [2e-07, 2e-01]
    QObjective range [1e-06, 2e+01]
    Bounds range [2e-03, 1e+00]
    RHS range [2e-03, 2e+02]
    QRHS range [2e+02, 2e+02]

    Loaded MIP start with objective -0.0872508

    Presolve removed 51782 rows and 51539 columns
    Presolve time: 0.19s
    Presolved: 2345 rows, 2201 columns, 22972 nonzeros
    Presolved model has 2452 quadratic objective terms
    Variable types: 1127 continuous, 1074 integer (1074 binary)

    Root relaxation: objective -1.317051e-01, 3141 iterations, 0.21 seconds

    Nodes | Current Node | Objective Bounds | Work
    Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time

    0 0 -0.13171 0 19 -0.08725 -0.13171 51.0% - 0s
    H 0 0 -0.1316187 -0.13171 0.07% - 0s

    Explored 1 nodes (3141 simplex iterations) in 0.52 seconds
    Thread count was 8 (of 8 available processors)

    Solution count 2: -0.131619 -0.0872508

    Optimal solution found (tolerance 1.00e-02)
    Best objective -1.316187146063e-01, best bound -1.317051087920e-01, gap 0.0656%

    MATLAB:

    Compute Server job ID: efd4000b-4c4b-4f93-b711-f524efab07dc
    Capacity available on 'GUROBI-TEST' - connecting...
    Established HTTP unencrypted connection
    Optimize a model with 54127 rows, 53740 columns and 199891 nonzeros
    Model has 1 quadratic constraint
    Variable types: 33613 continuous, 20127 integer (20127 binary)
    Coefficient statistics:
    Matrix range [2e-05, 2e+00]
    QMatrix range [1e-03, 2e+04]
    QLMatrix range [3e-04, 3e+02]
    Objective range [1e+00, 1e+00]
    Bounds range [2e-03, 1e+00]
    RHS range [2e-03, 2e+02]
    QRHS range [2e+02, 2e+02]
    Presolve removed 51786 rows and 51543 columns
    Presolve time: 0.20s
    Presolved: 2341 rows, 2197 columns, 22964 nonzeros
    Variable types: 1123 continuous, 1074 integer (1074 binary)

    Root relaxation: objective 1.970000e+00, 1009 iterations, 0.03 seconds

    Nodes | Current Node | Objective Bounds | Work
    Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time

    0 0 1.97000 0 3 - 1.97000 - - 0s
    0 0 1.97000 0 2 - 1.97000 - - 0s
    H 0 0 1.9700001 1.97000 0.00% - 0s
    * 0 0 0 1.9700001 1.97000 0.00% - 0s

    Cutting planes:
    Gomory: 2
    Cover: 2
    MIR: 1
    Flow cover: 1

    Explored 1 nodes (1585 simplex iterations) in 0.90 seconds
    Thread count was 8 (of 8 available processors)

    Solution count 1: 1.97

    Optimal solution found (tolerance 1.00e-02)
    Best objective 1.970000097123e+00, best bound 1.970000000000e+00, gap 0.0000%
    gurobi_safe: optimal solution found
    writing data to P:\vk_backup\gurobi_models\test_17420.lp
    Compute Server job ID: 4147efc6-91ea-4449-8165-9c51ea29ac1a
    Capacity available on 'GUROBI-TEST' - connecting...
    Established HTTP unencrypted connection
    Optimize a model with 54127 rows, 53740 columns and 199891 nonzeros
    Model has 6106 quadratic objective terms
    Model has 1 quadratic constraint
    Variable types: 33613 continuous, 20127 integer (20127 binary)
    Coefficient statistics:
    Matrix range [2e-05, 2e+00]
    QMatrix range [1e-03, 2e+04]
    QLMatrix range [3e-04, 3e+02]
    Objective range [2e-07, 2e-01]
    QObjective range [1e-06, 2e+01]
    Bounds range [2e-03, 1e+00]
    RHS range [2e-03, 2e+02]
    QRHS range [2e+02, 2e+02]
    Presolve removed 51782 rows and 51539 columns
    Presolve time: 0.19s
    Presolved: 2345 rows, 2201 columns, 22972 nonzeros
    Presolved model has 2452 quadratic objective terms
    Variable types: 1127 continuous, 1074 integer (1074 binary)

    Root relaxation: objective -1.317051e-01, 3140 iterations, 0.21 seconds

    Nodes | Current Node | Objective Bounds | Work
    Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time

    0 0 -0.13171 0 20 - -0.13171 - - 0s
    H 0 0 -0.1316172 -0.13171 0.07% - 0s

    Explored 1 nodes (3140 simplex iterations) in 0.44 seconds
    Thread count was 8 (of 8 available processors)

    Solution count 1: -0.131617

    Optimal solution found (tolerance 1.00e-02)
    Best objective -1.316172097689e-01, best bound -1.317051131492e-01, gap 0.0668%
    gurobi_safe: optimal solution found

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  • Eli Towle
    Gurobi Staff Gurobi Staff

    Hi Quang,

    Can you check if there is a round-off error in either of your scripts? E.g., in Python, we sometimes see calculations like this:

    >>> 0.1 + 0.2
    0.30000000000000004

    Thanks,
    Eli

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  • Quang Ngu
    • Edited
    Gurobi-versary
    First Comment
    First Question

    Hi Eli,

    Thanks for looking into this.

    Python:

    It looks like Python is contributing a round-off error in my scripts.

    MATLAB:

    It looks like there is a round-off error in my MATLAB script as well.

     Potential Solutions?

    Python: This is a bit tricky. I use the quad/linear expression class to set the constraints and objective function and I don't know how I can round-off the coefficients in the expression class. I don't want to round the numbers going into the expression class. I'm guessing the expression class does some arithmetic with the numbers I pass it. Is there a good way to do this? or do I have to loop through all the coefficients and round it?

    MATLAB: I tried rounding the Q, A, Qc, and etc.. matrices before passing it to gurobi but it looks like the rounding errors are still there, probably due to how MATLAB stores the numbers. Is there a way to tell gurobi to drop the least significant decimals?

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