pulp.tar.gz

pulp

We make optimization decisions every day. To improve the performance of your applications, computers need to do the same. This course will help you optimize computing functions through the open-source Python library PuLP.

In this course, you’ll learn about linear programming, an optimization approach that maximizes or minimizes functions through defined algebra, and the PuLP library along with its various optimizers available via APIs. You’ll identify the variables and constraints of computing functions and define the optimization goals. You’ll then learn and practice common linear programming algorithms. Finally, you’ll be introduced to potential applications in the real world.

By the end of the course, you will be able to optimize computing functions using Python and PuLP.

Master Linear Programming in Python with PuLP

PuLP employs an API solver from an available list of optimizers to solve the given linear programming problem. 

We can use solvers by specifying them as an argument in the `solve` method.For instance, in order to use the GLPK method for solving `prob`, we should use this construction:
```
prob.solve(GLPK(msg = 0))
``` 
While this is not compulsory, some solvers allow the users to pass an initial guess as an argument. This allows solvers to optimize the solution around that guess.

Currently, the PuLP library has the following optimizers for solving LP problems:



- MOSEK
- FICO XPRESS
- Choco SolverMIPCL
- SICP


We’ll talk about some optimizers that are good for linear programming below.


## The COIN-OR branch and cut optimizer

**COIN-OR branch and cut, or CBC**, is the default optimization solver in PuLP. It is an open-source solver by the COIN-OR project, which finds the non-integral solutions of an optimization problem, branches the answer to the nearest integral solutions, cuts the answers (branches) that do not optimize the problem, and continues the process until the upper bound equals the lower bound.




## The COIN-OR LP optimizer

**COIN-OR LP** stands for COIN-OR linear programming, often abbreviated as CLP. It is another open-source optimization solver by the COIN-OR project designed to optimize the problems of the form. 
$$\min{c^tx}$$

where:
<center>

$$row_{lower bound}\le Ax \le row_{upper bound}$$

$$column_{lower bound}\le x \le column_{upper bound}$$
</center>

## The CPLEX optimizer

**CPLEX** is an optimization software by IBM and is available as a library for many programming languages, including Python. It can solve several types of linear and quadratic optimization problems.

We’ll talk about some optimizers that are good for linear programming below.


# The COIN-OR branch and cut optimizer

**COIN-OR branch and cut, or CBC**, is the default optimization solver in PuLP. It is an open-source solver by the COIN-OR project, which finds the non-integral solutions of an optimization problem, branches the answer to the nearest integral solutions, cuts the answers (branches) that do not optimize the problem, and continues the process until the upper bound equals the lower bound.




# The COIN-OR LP optimizer

**COIN-OR LP** stands for COIN-OR linear programming, often abbreviated as CLP. It is another open-source optimization solver by the COIN-OR project designed to optimize the problems of the form. 
$$\min{c^tx}$$

where:
<center>

$$row_{lower bound}\le Ax \le row_{upper bound}$$

$$column_{lower bound}\le x \le column_{upper bound}$$
</center>

# The CPLEX optimizer

**CPLEX** is an optimization software by IBM and is available as a library for many programming languages, including Python. It can solve several types of linear and quadratic optimization problems.

Learn about the different solvers that PuLP uses for optimizing a linear programming problem.

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Solvers in PuLP

The COIN-OR branch and cut optimizer

The COIN-OR LP optimizer