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The document is the PuLP documentation for version 1.4.9, released on October 2, 2011. It provides a comprehensive guide to using the PuLP library for optimization problems in Python. The document covers key topics such as the optimization process, basic Python coding, installation instructions, and case studies including blending problems, set partitioning problems, and Sudoku formulation as linear programming. It also includes internal documentation on PuLP classes and solvers, along with author information.

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Why use PuLP Python?

PuLP Library for Python We recommend you the library PuLP [4], which is really helpful for writing down the model of the optimization problem. PuLP includes a default solver algorithm for these problems based on simplex combined with other algorithms, but it allows you to use other solvers (GUROBI, GLPK, CPLEX, etc.)

What is the difference between Pyomo and Cvxpy?

CVXPY support complex variables but doesnt support nonlinear function for constraints. On the other hand, Pyomo can support nonlinear problems but doesnt support complex variables.

What is PuLP modelling?

PuLP is a modeling framework for Linear (LP) and Integer Programing (IP) problems written in Python. Maintained by COIN-OR Foundation (Computational Infrastructure for Operations Research) PuLP interfaces with Solvers.

What is PuLP coding?

PuLP is a python library which can be used to solve linear programming problems. Linear Programming is used to solve optimization problems and has uses in various industries such as Manufacturing, Transportation, Food Diets etc.

What is PuLP used for in Python?

PuLP can only be used to model linear problems that fall under the LP, IP or MILP families (ie. with real or integer variables or a combination thereof). Unfortunately it cannot be used for non-linear problems, however there are many other Python libraries that we could use for such problems.

What is the difference between PuLP and SciPy?

SciPy is a general-purpose package for scientific computing with Python. PuLP is a Python linear programming API for defining problems and invoking external solvers.

Is Pyomo better than PuLP?

Id pick pyomo . I think it is more expressive and easier to read/work with. Youll need to a solver separately. If that is daunting, pulp comes with CBC built in.

What is the difference between PuLP and SciPy Linprog?

Unlike SciPy, PulP does not require us to provide inputs in the form of matrices or vectors. Instead, we can declare the objective function and constraints explicitly. Besides that, PuLP is more flexible than SciPy in model formulation. It supports both minimization and maximization objective functions.

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