Optimization is the way of life. We all have finite resources and time and we want to make the most of them. From using your time productively to solving supply chain problems for your company – every thing uses optimization.
It is also a very interesting topic – it starts with simple problems, but can get very complex. For example, sharing a chocolate between siblings is a simple optimization problem. We don’t think in mathematical term while solving it. On the other hand devising inventory and warehousing strategy for an e-tailer can be very complex. Millions of SKUs with different popularity in different regions to be delivered in defined time and resources – you see what I mean!
Linear programming (LP) is one of the simplest ways to perform optimization. It helps you solve some very complex optimization problems by making a few simplifying assumptions. As an analyst you are bound to come across applications and problems to be solved by Linear Programming.
For some reason, LP doesn’t get as much attention as it deserves while learning data science. So, I thought let me do justice to this awesome technique. I decided to write an article which explains Linear programming in simple English. I have kept the content as simple as possible. The idea is to get you started and excited about Linear Programming.