Linear Optimization with or-tools


Getting started

Over the last couple of months I’ve been getting my feet wet with linear programming and mathematical optimisation. I got a sense of how it all worked from this Discrete Optimisation course in Coursera and googling around I discovered that there are a ton of tools out there to help you solve optimisation problems. Makes sense, why would you want to implement a solving algorithm from scratch when some of the best minds in the history of mankind have already given it a shot?

Solvers, as these tools are often called, can reach the hundreds of thousands of dollars… and they are worth every penny! But since I wanted to play with one without forking up the dough, I narrowed my search down to open-source options.

Hans Mittelmann from the Arizona State University performs regular automated benchmarks on different mathematical optimisation tools. He publishes the benchmarks at Following what I read in the results, I picked Google Optimization Tools (OR-Tools) because it performed fairly well and… well, because if you’re looking for a sinister tool to model and solve hard problems in the human world, you can rarely go wrong with Google.

GLOP has C++ and Python APIs. I’m better at Python and I expected to quickly put together a web front-end for this, so I picked the latter one.

TL;DR. Just gimme the code

You can get the full code here.


The Problem

I picked a variation of the knapsack problem: a grocery-shopping example in which you try to maximise the number of calories you can buy with a limited budget. I got the problem from this blog post:

Basically: You walk into a grocery store with a grocery bag and some cash, to buy groceries for a week. You need to follow these rules:

1. Your bag can hold ten pounds.
2. You have $100
3. You need about 2000 calories a day, so a weekly shopping trip is about 14,000 calories.
4. You must purchase at least 4 ounces of each grocery item.

These are the groceries you can by and their price per pound:

Ham:     650 cals,  $4
Lettuce:  70 cals,  $1.5
Cheese: 1670 cals,  $5
Tuna:    830 cals, $20
Bread:  1300 cals,  $1.20


Installing OR-Tools

Follow the instructions at You will need Python and Python setuptools installed in your machine.


Coding the problem

We can split the coding of the problem into 6 elements:

1. Identify the problem

We tell or-tools that we are attempting to solve a linear programming problem. We create a solver variable that is going to contain all the necessary items to solve the problem.

from ortools.linear_solver import pywraplp
solver = pywraplp.Solver('SolveSimpleSystem',pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)


2. Ingest the input

We are going to send our table of possible groceries, calories and prices as a nested list:

food = [['ham',650, 4],


3. Configure the decision variables

We need 5 decision variables which contain how many pounds of each product you are going to buy. Instead of creating 5 variables, we can create a list of size 5 (the size of the food list). Each item in the list will contain a decision variable.

Each decision variable will be created with a call to the NumVar method of the solver variable, passing the minimum amount of groceries we can buy, the maximum (infinity), and a unique name for the variable (contained in the previously-defined food list).

    #food is a list of groceries, calories and prices
    variable_list = [[]] * len(food)
    for i in range(0, len(food)):
        #you must buy at least minShop of each
        variable_list[i] = solver.NumVar(minShop, solver.infinity(), str(food[i][0]))

They pythonic way of writing that loop is using a list comprehension. However I’m using the loop for readability.

    #same thing but with comprehension
    variable_list=[solver.NumVar(minShop, solver.infinity(), str(food[i][0])) for i in range(0, len(food))]


4. Configure the constraints

This is where most of the magic happens. We will create one constraint per “rule” specified in the problem description.

In linear programming each constraint is specified in terms of addition of the decision variables:

lower bound <= var1+var2+var3… <=upper bound

In the knapsack problem, the conversion is pretty straightforward. In some other cases, you have to re-think and re-model your problem in these terms.

We will create a list of 3 constraints, calling Constraint(lower bound, upper bound) for each one, and then walk the variables list and call SetCoefficient for each of the variables.

    #Define the constraints    
    #Constraint 1: totalWeight<maxWeight
    #ham + lettuce + cheese + tuna + bread <= maxWeight
    constraint_list.append(solver.Constraint(0, maxWeight))
    for i in range(0, len(food)):

    #Constraint 2: totalPrice<=maxCost 
    constraint_list.append(solver.Constraint(0, maxCost)) 
    for i in range(0, len(food)): 

    #Constraint 3: totalCalories>=minCals
    constraint_list.append(solver.Constraint(minCals, minCals + 100))
    for i in range(0, len(food)):

Note that the 4th rule of the problem, “You must purchase at least 4 ounces of each grocery item,” is already coded in the variables definition.


5. Configure the objective function

Similar to the constraint definition, the goal function is specified in terms of addition of the decision variables:

goal = Maximize/Minimize (var1+var2+var3…)

If we wish to minimize cost, we walk our variable list, get the price from the food list, and set the objective.

        for i in range(0, len(variable_list[)):
            objective.SetCoefficient(variable_list[i], food[i][2])

Say we wanted to maximize calories intake. We would do the same, but taking the calories value from the food list, and setting a maximization goal

# Define our objective: maximizing calories
for i in range(0, len(food)):
    objective.SetCoefficient(variable_list[i], food[i][1])


6. Solve!

After all these configuration steps, we just call the solve method against the solver variable and print out a solution if we find it.

    result_status = solve(solver)

    if result_status == solver.OPTIMAL:
        print('Successful solve.')
        # The problem has an optimal solution.
        print(('Problem solved in %f milliseconds' % solver.wall_time()))
        # The objective value of the solution.
        print(('Optimal objective value = %f' % solver.Objective().Value()))
        # The value of each variable in the solution.
        for variable in variable_list:
            print(('%s = %f' % (, variable.solution_value())))
        print(('Variable sum = %f' % var_sum));

        print('Advanced usage:')
        print(('Problem solved in %d iterations' % solver.iterations()))

        for variable in variable_list:
            print(('%s: reduced cost = %f' % (, variable.reduced_cost())))
        activities = solver.ComputeConstraintActivities()
        for i, constraint in enumerate(constraint_list):
            print(('constraint %d: dual value = %f\n'
              '               activity = %f' %
              (i, constraint.dual_value(), activities[constraint.index()])))

    elif result_status == solver.INFEASIBLE:
        print('No solution found.')
    elif result_status == solver.POSSIBLE_OVERFLOW:
        print('Some inputs are too large and may cause an integer overflow.')


You can get the full code here.


Next steps

We got a solution, but you need to know a little bit of python to run this program, change its inputs or read the solution. Wouldn’t it be nice to have some sort of user-friendly UI? This will be the subject of future posts.

Building a web front-end with falcon and gunicorn

Containerizing the solution with docker


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