Introduction to Optimization Constraints with SciPy

Explore bounds, linear and nonlinear constraints with practical Python examples

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Table of contents

1. Introduction
2. Implementation
   2.1 Unconstrained optimization
   2.2 Bounds
   2.3 Linear constraints
   2.4 Nonlinear constraints
   2.5 Applying different constraint types together
3. Conclusions

1. Introduction

Optimization is the process of picking the best elements from a set of potential candidates to reach a specific goal.

We perform a lot of optimization tasks in our everyday life: finding the shortest or fastest route to reach a destination, preparing a to-do list with daily assignments ordered by priority, buying groceries.

We can describe such problems starting with the definition of an objective function f(x).

Let us imagine that we are organizing a journey to another city, and we are trying assess a suitable departure time. In this example, the objective function f(x) is the duration of the trip as function of the departure time x.

We can formulate an optimization problem as the identification of the minimum or maximum value of the objective function. In our…