Mathematical Optimization Heuristics Every Data Scientist Should Know

There are many different ways to solve mathematical optimization problems. You can use greedy algorithms, constraint programming, mixed integer programming, genetic algorithms, local search, and others. Depending on the size and type of the problem, and the solution quality desired, one technique may work better than the other. This post gives an overview of different heuristics for solving discrete optimization problems. First, I explain the three components you need to describe an optimization problem mathematically.