| Name | msopt JSON |
| Version |
0.0.8
JSON |
| download |
| home_page | None |
| Summary | A mip model framework like gurobi, make write lp and mip easy, and support gurobi, cbc, highs solver. |
| upload_time | 2024-07-28 02:07:01 |
| maintainer | None |
| docs_url | None |
| author | allen |
| requires_python | >=3.11 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Using gurobi syntax for MIP modeling
The syntax of gurobi is very traversal for mip modeling, but the syntax of some open-source solvers and their corresponding API interfaces often uses the conventional naming convention of Python, which is inconvenient for programming with complex constraints.
Therefore, I developed a Python library called gurobi2, which encapsulates commonly used objects such as variable sets, create constraints, constraint sets, and large M methods, and provides a programming experience consistent with gurobipy. I hope to improve the efficiency and experience of programming through this.
For specific API usage, please refer to the official documentation of Gurobi: https://www.gurobi.com/documentation/current/refman/index.html
Here is an example:
```python
from msopt.api import Model, Param, INF, quicksum
m = Model("example", "CBC")
x = m.addVars([1,2,3], vtype="C", name="x")
y = m.addVars([1,2,3], vtype="B", name="y")
z = m.addVars([1,2,3], vtype="I", name="z")
m.addConstr(x.sum() == 1, name="c1")
m.addConstr(y.sum() >= 1, name="c2")
m.addConstr(z[1] == 1, name="c3")
objective = quicksum(x[i] for i in [1,2,3])
m.setObjective(objective, "maximize")
m.setParam(Param.TimeLimit, 10)
m.setParam(Param.MIPGap, 0.01)
m.optimize()
print("status: ", m.status)
print("Objective value: ", m.objVal)
print("x: ", x[1].x, x[2].x, x[3].x)
print("y: ", y[1].x, y[2].x, y[3].x)
print("z: ", z[1].x, z[2].x, z[3].x)
```
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"description": "# Using gurobi syntax for MIP modeling\n\nThe syntax of gurobi is very traversal for mip modeling, but the syntax of some open-source solvers and their corresponding API interfaces often uses the conventional naming convention of Python, which is inconvenient for programming with complex constraints.\n\nTherefore, I developed a Python library called gurobi2, which encapsulates commonly used objects such as variable sets, create constraints, constraint sets, and large M methods, and provides a programming experience consistent with gurobipy. I hope to improve the efficiency and experience of programming through this.\n\nFor specific API usage, please refer to the official documentation of Gurobi: https://www.gurobi.com/documentation/current/refman/index.html\n\nHere is an example:\n\n```python\n\nfrom msopt.api import Model, Param, INF, quicksum\n\n\nm = Model(\"example\", \"CBC\")\n\nx = m.addVars([1,2,3], vtype=\"C\", name=\"x\")\ny = m.addVars([1,2,3], vtype=\"B\", name=\"y\")\nz = m.addVars([1,2,3], vtype=\"I\", name=\"z\")\n\nm.addConstr(x.sum() == 1, name=\"c1\")\nm.addConstr(y.sum() >= 1, name=\"c2\")\nm.addConstr(z[1] == 1, name=\"c3\")\n\nobjective = quicksum(x[i] for i in [1,2,3])\nm.setObjective(objective, \"maximize\")\n\nm.setParam(Param.TimeLimit, 10)\nm.setParam(Param.MIPGap, 0.01)\nm.optimize()\n\nprint(\"status: \", m.status)\nprint(\"Objective value: \", m.objVal)\nprint(\"x: \", x[1].x, x[2].x, x[3].x)\nprint(\"y: \", y[1].x, y[2].x, y[3].x)\nprint(\"z: \", z[1].x, z[2].x, z[3].x)\n\n```",
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