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[ lp ] Package

The lp package provides functionality to solve Linear Programming (LP) problems using the GLPK library. It allows you to define and solve LP models, and provides tools to handle the results.

Note

  • This package will automatically install GLPK on your system if it is not already installed.
  • If you use Linux, you must have C Compiler(make) installed.
  • If you use MacOS, you must have Xcode installed.

Installation

To install the lp package, use the following command:

go get github.com/HazelnutParadise/insyra/lp

Features

SolveFromFile(lpFile string, timeoutSeconds ...int) (*DataTable, *DataTable)

Solves an LP file using GLPK and returns the result as two DataTable. First DataTable contains the solution, and the second one contains additional information.

Parameters

  • lpFile string: The path to the LP file.
  • timeoutSeconds int(optional): The timeout for the solver in seconds.

Returns

  • *DataTable: The solution DataTable(the column name and the row name will not be set).
  • *DataTable: The additional information DataTable(the column name and the row name will be set).

Example

result, info := lp.SolveLPWithGLPK("model.lp", 10)
result.Show()
info.Show()

// convert to csv
result.ToCSV("solution.csv", false, false)
info.ToCSV("info.csv", true, true)

Tip

Using ToCSV method, you can easily export the result to a CSV file.

SolveModel(model *lpgen.LPModel, timeoutSeconds ...int) (*DataTable, *DataTable)

Solves an LPModel directly by passing the model to GLPK without generating a model file.

Parameters

  • model *lpgen.LPModel: The LPModel to solve.
  • timeoutSeconds int(optional): The timeout for the solver in seconds.

Returns

  • *DataTable: The solution DataTable(the column name and the row name will not be set).
  • *DataTable: The additional information DataTable(the column name and the row name will be set).

Example

model := lpgen.NewLPModel()
model.SetObjective("Maximize", "3 x1 + 5 x2 + x3")
model.AddConstraint("x1 + 2 x2 + 3 x3 <= 12")
model.AddConstraint("x2 + x3 + x4 <= 3")
model.AddConstraint("x1 + x2 + x3 + x4 <= 100")
model.AddBound("0 <= x1 <= 4")
model.AddBound("1 <= x2 <= 6")
model.AddBound("0 <= x3 <= 10")
model.AddIntegerVar("x1")
model.AddIntegerVar("x2")
model.AddIntegerVar("x3")
model.AddIntegerVar("x4")
model.AddBinaryVar("x1")
model.AddBinaryVar("x2")

result, info := lp.SolveModel(model, 10)
result.ToCSV("solution.csv", false, false)
info.ToCSV("info.csv", true, true)