OMMX Python SDK 1.7.0

OMMX Python SDK 1.7.0#

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Please refer to the GitHub Release for individual changes.

Summary#

QPLIB format parser#

Following the MPS format, support for the QPLIB format parser has been added.

import tempfile

# Example problem from QPLIB
#
# Furini, Fabio, et al. "QPLIB: a library of quadratic programming instances." Mathematical Programming Computation 11 (2019): 237-265 pages 42 & 43
# https://link.springer.com/article/10.1007/s12532-018-0147-4
contents = """
! ---------------
! example problem
! ---------------
MIPBAND # problem name
QML # problem is a mixed-integer quadratic program
Minimize # minimize the objective function
3 # variables
2 # general linear constraints
5 # nonzeros in lower triangle of Q^0
1 1 2.0 5 lines row & column index & value of nonzero in lower triangle Q^0
2 1 -1.0 |
2 2 2.0 |
3 2 -1.0 |
3 3 2.0 |
-0.2 default value for entries in b_0
1 # non default entries in b_0
2 -0.4 1 line of index & value of non-default values in b_0
0.0 value of q^0
4 # nonzeros in vectors b^i (i=1,...,m)
1 1 1.0 4 lines constraint, index & value of nonzero in b^i (i=1,...,m)
1 2 1.0 |
2 1 1.0 |
2 3 1.0 |
1.0E+20 infinity
1.0 default value for entries in c_l
0 # non default entries in c_l
1.0E+20 default value for entries in c_u
0 # non default entries in c_u
0.0 default value for entries in l
0 # non default entries in l
1.0 default value for entries in u
1 # non default entries in u
2 2.0 1 line of non-default indices and values in u
0 default variable type is continuous
1 # non default variable types
3 2 variable 3 is binary
1.0 default value for initial values for x
0 # non default entries in x
0.0 default value for initial values for y
0 # non default entries in y
0.0 default value for initial values for z
0 # non default entries in z
0 # non default names for variables
0 # non default names for constraints"#;
"""

# Create a named temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix='.qplib') as temp_file:
    temp_file.write(contents.encode())
    qplib_sample_path = temp_file.name


print(f"QPLIB sample file created at: {qplib_sample_path}")

QPLIB sample file created at: /tmp/tmpgj09u52g.qplib

from ommx import qplib

# Load a QPLIB file
instance = qplib.load_file(qplib_sample_path)

# Display decision variables and constraints
display(instance.decision_variables)
display(instance.constraints)
kind lower upper name subscripts description substituted_value
id
0 continuous 0.0 1.0 <NA> [] <NA> <NA>
1 continuous 0.0 2.0 <NA> [] <NA> <NA>
2 binary 0.0 1.0 <NA> [] <NA> <NA>
equality type used_ids name subscripts description
id
2 <=0 linear {0, 1} Qplib_constr_0 [c_l] [] <NA>
3 <=0 linear {0, 2} Qplib_constr_1 [c_l] [] <NA>

Change in behavior of {Solution, SampleSet}.feasible#

  • The behavior of feasible in ommx.v1.Solution and ommx.v1.SampleSet has been changed.

    • The handling of removed_constraints introduced in Python SDK 1.6.0 has been changed. In 1.6.0, feasible ignored removed_constraints, but in 1.7.0, feasible now considers removed_constraints.

    • Additionally, feasible_relaxed which explicitly ignores removed_constraints and feasible_unrelaxed which considers removed_constraints have been introduced. feasible is an alias for feasible_unrelaxed.

To understand the behavior, let’s consider the following simple optimization problem:

\[\begin{split} \begin{align*} \max &\quad x_0 + x_1 + x_2 \\ \text{s.t.} &\quad x_0 + x_1 \leq 1 \\ &\quad x_1 + x_2 \leq 1 \\ &\quad x_1, x_2, x_3 \in \{0, 1\} \end{align*} \end{split}\]
from ommx.v1 import DecisionVariable, Instance

x = [DecisionVariable.binary(i) for i in range(3)]

instance = Instance.from_components(
    decision_variables=x,
    objective=sum(x),
    constraints=[
        (x[0] + x[1] <= 1).set_id(0),
        (x[1] + x[2] <= 1).set_id(1),
    ],
    sense=Instance.MAXIMIZE,
)
instance.constraints
equality type used_ids name subscripts description
id
0 <=0 linear {0, 1} <NA> [] <NA>
1 <=0 linear {1, 2} <NA> [] <NA>

Next, we relax one of the constraints \(x_0 + x_1 \leq 1\).

instance.relax_constraint(constraint_id=0, reason="Manual relaxation")
display(instance.constraints)
display(instance.removed_constraints)
equality type used_ids name subscripts description
id
1 <=0 linear {1, 2} <NA> [] <NA>
equality type used_ids name subscripts description removed_reason
id
0 <=0 linear {0, 1} <NA> [] <NA> Manual relaxation

Now, \(x_0 = 1, x_1 = 1, x_2 = 0\) is not a solution to the original problem, but it is a solution to the relaxed problem. Therefore, feasible_relaxed will be True, but feasible_unrelaxed will be False. Since feasible is an alias for feasible_unrelaxed, it will be False.

solution = instance.evaluate({0: 1, 1: 1, 2: 0})
print(f"{solution.feasible=}")
print(f"{solution.feasible_relaxed=}")
print(f"{solution.feasible_unrelaxed=}")

solution.feasible=False
solution.feasible_relaxed=True
solution.feasible_unrelaxed=False