Yeah what you’re describing, in computerized maths, is called a “local minimum”.
When you’re seeking out a global minimum of a function, such as in this example:
You might be tempted to think “well, i start somewhere, then i take one small step after another, always going downhill, until i am at the lowest point”. But what happens in practice, is that you get stuck in so called “local minimums”. For a visualization:
That is the fundamental problem with being short-sighted (i.e., taking one small step after another, always going downhill). If you really want to find the global minimum, you have to think globally. You have to apply analysis to the whole function, not just local parts of it, to find the best spot.
Great visualization and further explication for more visual learners. Love this type of comment on Lemmy where it’s to further discussion and help clarify.
Appreciate that now I can use “local minimum” in a correct way with people :D
Yeah what you’re describing, in computerized maths, is called a “local minimum”.
When you’re seeking out a global minimum of a function, such as in this example:
You might be tempted to think “well, i start somewhere, then i take one small step after another, always going downhill, until i am at the lowest point”. But what happens in practice, is that you get stuck in so called “local minimums”. For a visualization:
That is the fundamental problem with being short-sighted (i.e., taking one small step after another, always going downhill). If you really want to find the global minimum, you have to think globally. You have to apply analysis to the whole function, not just local parts of it, to find the best spot.
Great visualization and further explication for more visual learners. Love this type of comment on Lemmy where it’s to further discussion and help clarify.
Appreciate that now I can use “local minimum” in a correct way with people :D