Integer programming in SciPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is integer programming in simple terms?

Integer programming is a way to solve math problems where some or all answers must be whole numbers, like counting apples or people.

beginner

What does the scipy.optimize library help with in integer programming?

It helps find the best solution to problems with limits and whole number rules using tools like milp for mixed-integer linear programming.

beginner

Why do we sometimes need integer solutions instead of decimals?

Because some things can't be split, like people, cars, or machines. You can't have 2.5 cars, so answers must be whole numbers.

intermediate

What is the difference between linear programming and integer programming?

Linear programming allows decimal answers, but integer programming requires some or all answers to be whole numbers.

intermediate

How does the milp function in scipy.optimize work?

It solves problems where you want to maximize or minimize a goal, with rules, and some answers must be integers. You give it the goal, rules, and which answers are integers.

What type of numbers does integer programming require for some variables?

A Whole numbers

B Any real numbers

C Only decimals

D Complex numbers

Integer programming requires some or all variables to be whole numbers (integers).

Which Python library provides tools for integer programming?

A seaborn

B matplotlib

C pandas

D scipy.optimize

scipy.optimize includes functions like milp for integer programming.

What does MILP stand for?

A Matrix Integer Linear Programming

B Multiple Integer Linear Problems

C Mixed-Integer Linear Programming

D Minimal Integer Linear Procedure

MILP means Mixed-Integer Linear Programming, where some variables are integers and others can be decimals.

Why might integer programming be harder than regular linear programming?

A Because it uses less memory

B Because integers limit possible solutions

C Because decimals are easier to count

D Because it ignores constraints

Integer constraints reduce possible answers, making the problem more complex to solve.

In integer programming, what is a common real-life example?

A Scheduling workers in shifts

B Measuring temperature

C Calculating speed

D Predicting weather

Scheduling workers requires whole numbers of people, a classic integer programming problem.

Explain integer programming and why it is useful in real life.

Think about problems where you can't split items or people.

Describe how you would use scipy.optimize's milp function to solve an integer programming problem.

Focus on inputs needed for milp and what it returns.