inear Programming in Python: A Straight Forward Tutorial. Linear programming is one of the most common optimization techniques.

Linear programming is one of the most common optimization techniques. It has a wide range of applications and is frequently used in operations research, industrial design, planning, and the list goes on. Alas, it is not as hyped as machine learning is (which is certainly a form of optimization itself), but is the go-to method for problems that can be formulated through decision variables that have linear relationships. This is a fast practical tutorial, I will perhaps cover the Simplex algorithm and the theory in a later post.

Just to get an idea, we are going to solve a simple problem regarding production scheduling. Imagine that you work for a company that builds computers. A computer is a fairly complex product, and there are several factories that assemble them which the company pays a certain amount per unit. The cost of this computer model on the market is fixed at 500$, different factories assemble the computers at different speeds and costs. Factory **f0** produces 2000 per day at 450$ per unit, factory **f1** 1500 per day at 420$ per unit and **f2** 1000 per day at 400$ per unit. We have 1 month to assemble 80 000 units under the constraint that no factory is to produce more than double the units than any other factory. The question is, **what is the optimal production allocation between the factories** such that we **maximize** **the profit** obtained from selling the computers under those constraints?

In this tutorial we are going to be using Python and a linear programming optimization package PuLP, copy-paste install with pip:

```
pip install pulp
```

Now, in order to solve the computer production problem with linear programming, we need the following things:

- The set of decision variables
- The set of linear constraints on those variables
- A linear objective function to maximize or minimize

So, open up your favorite editor and let’s get started. Before defining the variables in PuLP, we need to create a problem object with the following code:

```
from pulp import *
problem = LpProblem("problemName", LpMaximize)
```

We are going to add the constraints and objective function to this object. Notice that the problem constructor receives a problem name and **LpMaximize**, which means we want to maximize our objective function. In our case, this is the profit from selling a certain number of computers. Additionally, we also define the constants that we received from the problem statement:

```
# factory cost per day
cf0 = 450
cf1 = 420
cf2 = 400
# factory throughput per day
f0 = 2000
f1 = 1500
f2 = 1000
# production goal
goal = 80000
# time limit
max_num_days = 30
num_factories = 3
```

In the next section, we will define the decision variables.

In PuLP, a decision variable is defined the following way:

```
variable = LpVariable("variableName")
```

Sometimes we need to provide bounds to the variable (the default is no bounds), in this case, we would write the following:

```
var = LpVariable("boundedVariableName",lowerBound,upperBound)
```

Yet another useful way to define variables in PuLP is using the dicts function. This can be very useful in cases where we need to define a large number of variables of the same type and bounds, **variableNames** would be a list of keys for the dictionary:

```
varDict = LpVariable.dicts("varDict", variableNames, lowBound, upBound)
```

So based on the previous definitions, the decision variables for the computer production problem are the days that we spend producing for each factory:

```
# factories
num_factories = 3
factory_days = LpVariable.dicts("factoryDays", list(range(num_factories)), 0, 30, cat="Continuous")
```

Now that we defined our decision variables we can shift to defining the constraints for the problem. Note that the constraints have to be linear in a linear programming setting. The constraints that we care about are that the number of units assembled should be above or equal to the goal amount and the production constraint that no factory should produce more than double as the other factory:

```
# goal constraint
c1 = factory_days[0]*f1 + factory_days[1]*f2 + factory_days[2] * f3 >= goal
# production constraints
c2 = factory_days[0]*f0 <= 2*factory_days[1]*f1
c3 = factory_days[0]*f0 <= 2*factory_days[2]*f2
c4 = factory_days[1]*f1 <= 2*factory_days[2]*f2
c5 = factory_days[1]*f1 <= 2*factory_days[0]*f0
c6 = factory_days[2]*f2 <= 2*factory_days[1]*f1
c7 = factory_days[2]*f2 <= 2*factory_days[0]*f0
# adding the constraints to the problem
problem += c1
problem += c2
problem += c3
problem += c4
problem += c5
problem += c6
problem += c7
```

The objective function for the computer assembly problem is basically minimizing the cost of assembling all of those computers. This can be written simply as maximizing the negative cost:

```
# objective function
problem += -factory_days[0]*cf0*f0 - factory_days[1]*cf1*f1 - factory_days[2]*cf2*f2
```

Let’s take a look at the problem definition, this can be simply achieved by the call:

```
print(problem)
```

This results in the following output which I think is self-explanatory, it lists the objective function, the constraints and the various decision variables in the problem:

```
computerAssembly:
MAXIMIZE
-900000*factoryDays_0 + -630000*factoryDays_1 + -400000*factoryDays_2 + 0
SUBJECT TO
_C1: 1500 factoryDays_0 + 1000 factoryDays_1 + 1000 factoryDays_2 >= 80000
_C2: 2000 factoryDays_0 - 3000 factoryDays_1 <= 0
_C3: 2000 factoryDays_0 - 2000 factoryDays_2 <= 0
_C4: 1500 factoryDays_1 - 2000 factoryDays_2 <= 0
_C5: - 4000 factoryDays_0 + 1500 factoryDays_1 <= 0
_C6: - 3000 factoryDays_1 + 1000 factoryDays_2 <= 0
_C7: - 4000 factoryDays_0 + 1000 factoryDays_2 <= 0
VARIABLES
factoryDays_0 <= 30 Continuous
factoryDays_1 <= 30 Continuous
factoryDays_2 <= 30 Continuous
```

After we defined everything necessary in our linear programming problem, we can call the solve method which outputs 1 if the problem is solved and -1 if it was infeasible, it is a simple one-liner:

```
# solving
problem.solve()
```

The solutions to the problem can be obtained by accessing the varValue attribute of each variable:

```
for i in range(3):
print(f"Factory {i}: {factory_days[i].varValue}")
```

This results in the following output:

```
Factory 0: 23.076923
Factory 1: 15.384615
Factory 2: 30.0
```

In linear programming, we assume that the relationships between the variables are linear and that the variables themselves are continuous. As a follow up on this tutorial, I will be covering Mixed Integer Programming, where the variables can be integers, which will prove a very useful thing since it can be used to simulate boolean logic.

**30s ad**

☞ Python for Beginners: Become a Certified Python Developer

☞ Introduction to Python for Beginners

☞ The Python 3 Bible™ | Go from Beginner to Advanced in Python

☞ Complete Python Bootcamp: Go from zero to hero in Python

☞ Learn Python Through Exercises

Linear programming is one of the most common optimization techniques. It has a wide range of applications and is frequently used in operations research, industrial design, planning, and the list goes on. Alas, it is not as hyped as machine learning is (which is certainly a form of optimization itself), but is the go-to method for problems that can be formulated through decision variables that have linear relationships. This is a fast practical tutorial, I will perhaps cover the Simplex algorithm and the theory in a later post.

Just to get an idea, we are going to solve a simple problem regarding production scheduling. Imagine that you work for a company that builds computers. A computer is a fairly complex product, and there are several factories that assemble them which the company pays a certain amount per unit. The cost of this computer model on the market is fixed at 500$, different factories assemble the computers at different speeds and costs. Factory **f0** produces 2000 per day at 450$ per unit, factory **f1** 1500 per day at 420$ per unit and **f2** 1000 per day at 400$ per unit. We have 1 month to assemble 80 000 units under the constraint that no factory is to produce more than double the units than any other factory. The question is, **what is the optimal production allocation between the factories** such that we **maximize** **the profit** obtained from selling the computers under those constraints?

In this tutorial we are going to be using Python and a linear programming optimization package PuLP, copy-paste install with pip:

```
pip install pulp
```

Now, in order to solve the computer production problem with linear programming, we need the following things:

- The set of decision variables
- The set of linear constraints on those variables
- A linear objective function to maximize or minimize

So, open up your favorite editor and let’s get started. Before defining the variables in PuLP, we need to create a problem object with the following code:

```
from pulp import *
problem = LpProblem("problemName", LpMaximize)
```

We are going to add the constraints and objective function to this object. Notice that the problem constructor receives a problem name and **LpMaximize**, which means we want to maximize our objective function. In our case, this is the profit from selling a certain number of computers. Additionally, we also define the constants that we received from the problem statement:

```
# factory cost per day
cf0 = 450
cf1 = 420
cf2 = 400
# factory throughput per day
f0 = 2000
f1 = 1500
f2 = 1000
# production goal
goal = 80000
# time limit
max_num_days = 30
num_factories = 3
```

In the next section, we will define the decision variables.

In PuLP, a decision variable is defined the following way:

```
variable = LpVariable("variableName")
```

Sometimes we need to provide bounds to the variable (the default is no bounds), in this case, we would write the following:

```
var = LpVariable("boundedVariableName",lowerBound,upperBound)
```

Yet another useful way to define variables in PuLP is using the dicts function. This can be very useful in cases where we need to define a large number of variables of the same type and bounds, **variableNames** would be a list of keys for the dictionary:

```
varDict = LpVariable.dicts("varDict", variableNames, lowBound, upBound)
```

So based on the previous definitions, the decision variables for the computer production problem are the days that we spend producing for each factory:

```
# factories
num_factories = 3
factory_days = LpVariable.dicts("factoryDays", list(range(num_factories)), 0, 30, cat="Continuous")
```

Now that we defined our decision variables we can shift to defining the constraints for the problem. Note that the constraints have to be linear in a linear programming setting. The constraints that we care about are that the number of units assembled should be above or equal to the goal amount and the production constraint that no factory should produce more than double as the other factory:

```
# goal constraint
c1 = factory_days[0]*f1 + factory_days[1]*f2 + factory_days[2] * f3 >= goal
# production constraints
c2 = factory_days[0]*f0 <= 2*factory_days[1]*f1
c3 = factory_days[0]*f0 <= 2*factory_days[2]*f2
c4 = factory_days[1]*f1 <= 2*factory_days[2]*f2
c5 = factory_days[1]*f1 <= 2*factory_days[0]*f0
c6 = factory_days[2]*f2 <= 2*factory_days[1]*f1
c7 = factory_days[2]*f2 <= 2*factory_days[0]*f0
# adding the constraints to the problem
problem += c1
problem += c2
problem += c3
problem += c4
problem += c5
problem += c6
problem += c7
```

The objective function for the computer assembly problem is basically minimizing the cost of assembling all of those computers. This can be written simply as maximizing the negative cost:

```
# objective function
problem += -factory_days[0]*cf0*f0 - factory_days[1]*cf1*f1 - factory_days[2]*cf2*f2
```

Let’s take a look at the problem definition, this can be simply achieved by the call:

```
print(problem)
```

This results in the following output which I think is self-explanatory, it lists the objective function, the constraints and the various decision variables in the problem:

```
computerAssembly:
MAXIMIZE
-900000*factoryDays_0 + -630000*factoryDays_1 + -400000*factoryDays_2 + 0
SUBJECT TO
_C1: 1500 factoryDays_0 + 1000 factoryDays_1 + 1000 factoryDays_2 >= 80000
_C2: 2000 factoryDays_0 - 3000 factoryDays_1 <= 0
_C3: 2000 factoryDays_0 - 2000 factoryDays_2 <= 0
_C4: 1500 factoryDays_1 - 2000 factoryDays_2 <= 0
_C5: - 4000 factoryDays_0 + 1500 factoryDays_1 <= 0
_C6: - 3000 factoryDays_1 + 1000 factoryDays_2 <= 0
_C7: - 4000 factoryDays_0 + 1000 factoryDays_2 <= 0
VARIABLES
factoryDays_0 <= 30 Continuous
factoryDays_1 <= 30 Continuous
factoryDays_2 <= 30 Continuous
```

After we defined everything necessary in our linear programming problem, we can call the solve method which outputs 1 if the problem is solved and -1 if it was infeasible, it is a simple one-liner:

```
# solving
problem.solve()
```

The solutions to the problem can be obtained by accessing the varValue attribute of each variable:

```
for i in range(3):
print(f"Factory {i}: {factory_days[i].varValue}")
```

This results in the following output:

```
Factory 0: 23.076923
Factory 1: 15.384615
Factory 2: 30.0
```

In linear programming, we assume that the relationships between the variables are linear and that the variables themselves are continuous. As a follow up on this tutorial, I will be covering Mixed Integer Programming, where the variables can be integers, which will prove a very useful thing since it can be used to simulate boolean logic.

**30s ad**

☞ Python for Beginners: Become a Certified Python Developer

☞ Introduction to Python for Beginners

☞ The Python 3 Bible™ | Go from Beginner to Advanced in Python

Python GUI Programming Projects using Tkinter and Python 3

Description

Learn Hands-On Python Programming By Creating Projects, GUIs and Graphics

Python is a dynamic modern object -oriented programming language

It is easy to learn and can be used to do a lot of things both big and small

Python is what is referred to as a high level language

Python is used in the industry for things like embedded software, web development, desktop applications, and even mobile apps!

SQL-Lite allows your applications to become even more powerful by storing, retrieving, and filtering through large data sets easily

If you want to learn to code, Python GUIs are the best way to start!

I designed this programming course to be easily understood by absolute beginners and young people. We start with basic Python programming concepts. Reinforce the same by developing Project and GUIs.

Why Python?

The Python coding language integrates well with other platforms – and runs on virtually all modern devices. If you’re new to coding, you can easily learn the basics in this fast and powerful coding environment. If you have experience with other computer languages, you’ll find Python simple and straightforward. This OSI-approved open-source language allows free use and distribution – even commercial distribution.

When and how do I start a career as a Python programmer?

In an independent third party survey, it has been revealed that the Python programming language is currently the most popular language for data scientists worldwide. This claim is substantiated by the Institute of Electrical and Electronic Engineers, which tracks programming languages by popularity. According to them, Python is the second most popular programming language this year for development on the web after Java.

Python Job Profiles

Software Engineer

Research Analyst

Data Analyst

Data Scientist

Software Developer

Python Salary

The median total pay for Python jobs in California, United States is $74,410, for a professional with one year of experience

Below are graphs depicting average Python salary by city

The first chart depicts average salary for a Python professional with one year of experience and the second chart depicts the average salaries by years of experience

Who Uses Python?

This course gives you a solid set of skills in one of today’s top programming languages. Today’s biggest companies (and smartest startups) use Python, including Google, Facebook, Instagram, Amazon, IBM, and NASA. Python is increasingly being used for scientific computations and data analysis

Take this course today and learn the skills you need to rub shoulders with today’s tech industry giants. Have fun, create and control intriguing and interactive Python GUIs, and enjoy a bright future! Best of Luck

Who is the target audience?

Anyone who wants to learn to code

For Complete Programming Beginners

For People New to Python

This course was designed for students with little to no programming experience

People interested in building Projects

Anyone looking to start with Python GUI development

Basic knowledge

Access to a computer

Download Python (FREE)

Should have an interest in programming

Interest in learning Python programming

Install Python 3.6 on your computer

What will you learn

Build Python Graphical User Interfaces(GUI) with Tkinter

Be able to use the in-built Python modules for their own projects

Use programming fundamentals to build a calculator

Use advanced Python concepts to code

Build Your GUI in Python programming

Use programming fundamentals to build a Project

Signup Login & Registration Programs

Quizzes

Assignments

Job Interview Preparation Questions

& Much More

Guide to Python Programming Language

Description

The course will lead you from beginning level to advance in Python Programming Language. You do not need any prior knowledge on Python or any programming language or even programming to join the course and become an expert on the topic.

The course is begin continuously developing by adding lectures regularly.

Please see the Promo and free sample video to get to know more.

Hope you will enjoy it.

Basic knowledge

An Enthusiast Mind

A Computer

Basic Knowledge To Use Computer

Internet Connection

What will you learn

Will Be Expert On Python Programming Language

Build Application On Python Programming Language

Python Programming Tutorials For Beginners

Description

Hello and welcome to brand new series of wiredwiki. In this series i will teach you guys all you need to know about python. This series is designed for beginners but that doesn't means that i will not talk about the advanced stuff as well.

As you may all know by now that my approach of teaching is very simple and straightforward.In this series i will be talking about the all the things you need to know to jump start you python programming skills. This series is designed for noobs who are totally new to programming, so if you don't know any thing about

programming than this is the way to go guys Here is the links to all the videos that i will upload in this whole series.

In this video i will talk about all the basic introduction you need to know about python, which python version to choose, how to install python, how to get around with the interface, how to code your first program. Than we will talk about operators, expressions, numbers, strings, boo leans, lists, dictionaries, tuples and than inputs in python. With

Lots of exercises and more fun stuff, let's get started.

Download free Exercise files.

Dropbox: https://bit.ly/2AW7FYF

Who is the target audience?

First time Python programmers

Students and Teachers

IT pros who want to learn to code

Aspiring data scientists who want to add Python to their tool arsenal

Basic knowledge

Students should be comfortable working in the PC or Mac operating system

What will you learn

know basic programming concept and skill

build 6 text-based application using python

be able to learn other programming languages

be able to build sophisticated system using python in the future