This Python-based LP modeler is designed to create and solve linear programming (LP) problems. It allows for optimization of complex systems through mathematical algorithms to improve decision making in various fields.
PuLP is a highly recommended software for linear programming solutions. It stands out because it allows users to generate MPS or LP files and communicate with various solvers such as GLPK, COIN CLP/CBC, CPLEX, and XPRESS. Comprehensive instructions on how to use this software can easily be found on the wiki.
One of the core functions of PuLP is to create new variables using LpVariable(). This feature allows users to define the bounds for a variable, for instance, create a variable x with a lower limit of 0 and an upper limit of 3: x = LpVariable("x", 0, 3).
Another vital function of PuLP is to create new problems using LpProblem(). The created problem can either be minimized or maximized, based on the user’s preference. When creating a problem, different variables and expressions can be added to the problem using the “+” operator.
PuLP also allows users to choose a solver among the ones available and solve the linear problem. Like in the example given: status = prob.solve(GLPK(msg = 0)). It is important to note that PuLP also reveals the status of the solution to the user, which can be Optimal, Infeasible, or Unbounded.
Lastly, PuLP allows users to get variable values using value(), obtain an expression, or constraint from a list using lpSum(), and construct a linear expression to be used as a constraint or variable using lpDot(). Essentially, PuLP provides everything one needs to execute the linear programming problem-solving process.
One of the core functions of PuLP is to create new variables using LpVariable(). This feature allows users to define the bounds for a variable, for instance, create a variable x with a lower limit of 0 and an upper limit of 3: x = LpVariable("x", 0, 3).
Another vital function of PuLP is to create new problems using LpProblem(). The created problem can either be minimized or maximized, based on the user’s preference. When creating a problem, different variables and expressions can be added to the problem using the “+” operator.
PuLP also allows users to choose a solver among the ones available and solve the linear problem. Like in the example given: status = prob.solve(GLPK(msg = 0)). It is important to note that PuLP also reveals the status of the solution to the user, which can be Optimal, Infeasible, or Unbounded.
Lastly, PuLP allows users to get variable values using value(), obtain an expression, or constraint from a list using lpSum(), and construct a linear expression to be used as a constraint or variable using lpDot(). Essentially, PuLP provides everything one needs to execute the linear programming problem-solving process.
What's New
Version 1.4.1: N/A