![scipy linprog scipy linprog](https://i.ytimg.com/vi/uuosEqyLJiM/hqdefault.jpg)
Problem += 4*A + 5*B <= 30, 'Designer Constraint' We add the objective function and constraints to the instance of the LpProblem we created earlier.
![scipy linprog scipy linprog](https://i.stack.imgur.com/LCMQv.png)
The type can also be LpContinuous or LpBinary. The first parameter is the name of the variable, the second parameter specifies the lower bound and third parameter specifies the type of the variable. We need to maximize our profits, therefore we use LpMaximize Create Decision Variables A = LpVariable('Car A', lowBound=0, cat=LpInteger)ī = LpVariable('Car B', lowBound=0, cat=LpInteger) The first parameter is the name of our problem and the second parameter is the type of the Problem.
#SCIPY LINPROG INSTALL#
Install and Import pulp pip install pulpįrom pulp import * Create an Instance of LpProblem problem = LpProblem('Car Factory', LpMaximize) Documentation for the library can be found here. PuLP is an open-source Python library used for Linear Programming. Using PuLP, we will be able to easily find the integral solutions PuLP
![scipy linprog scipy linprog](https://i.ytimg.com/vi/uuosEqyLJiM/maxresdefault.jpg)
One way to solve it is to plot the equations on a graph, find the feasible area and then plug in the value of the vertices.įinding the integer solutions is not so trivial, we can not round up the vertice values and consider it a solution since we may violate some constraints in doing so. The following point gives us our Objective Function which we need to maximize and the rest of the points give us our constraints. The first point gives us our decision variables. The Designer, Engineer and Machine can all work for 30 days.The Machine takes 2 days to build Car A and 7 days to build Car B.The Engineer takes 3 days to build Car A and 6 days to build Car B.The Designer takes 4 days to build Car A and 5 days to build Car B.Car A gives us a profit of 20k and Car B gives us a profit of 45k.We have two models of a car, Car A and Car B.Linear Programming is used to solve Optimization problems given a few constraints. Using PuLP to solve an optimization problem.I will be dividing the tutorial into two parts I briefly go over this technique in the first part of the tutorial In high school, we used to plot the equations on a graph, shade the feasible region and find the value of the equation to be maximized or minimized by substituting the variables with the verticle values of the shaded region. The value of one of the equations has to be maximized or minimized while the other equations are constraints. Linear Programming is used to solve optimization problems and has uses in various industries such as Manufacturing, Transportation, Food Diets etcĪ basic Linear Programming problem is where we are given multiple equations. PuLP is a python library which can be used to solve linear programming problems.