Local optimum

Just like when you try to climb a hill, you might get to a point that you can't go any higher because there's a "bump" in front of you that stops you from going to the very top. That spot where you are is called a "local optimum." It means that you are in the best spot you can be without having to go down first and then up the other side.

For example, imagine you are a tiny ant trying to find the shortest path from one end of a maze to the other. You might come across "dead ends" where you can't go any further, but you can still get to the end by taking a longer path. That's a local optimum for the ant.

Local optimums can be good, but they are not always the best option. Sometimes, you need to go down a little first to reach a higher peak. Just like the ant might need to backtrack a bit and take a different path to get to the end of the maze faster.

In computer science, we use the idea of local optimums to talk about finding solutions to complex problems. Local optimums can be helpful for solving problems, but we need to be careful that we're not stuck there and missing out on even better solutions.