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global minimum
This term is primarily used in mathematics, computer science, and optimization theory. It describes the absolute lowest point of a function, distinguishing it from a local minimum, which is only the lowest point within a specific neighborhood or region of the domain.
In practical application, finding the global minimum is often the primary goal of machine learning training, such as minimizing a loss function. However, because many complex functions have multiple valleys, algorithms may accidentally settle in a local minimum, failing to reach the true global minimum.
Meanings
Examples
The algorithm successfully converged to the global minimum of the cost function.
I wonder if the optimizer is stuck in a local dip or if it actually found the global minimum.
To find the global minimum, we must analyze the function across its entire domain.
The goal of this optimization process is to reach the global minimum.
Is this point truly the global minimum or just a local one?
The loss landscape is so complex that finding the global minimum is nearly impossible.
We need a more robust search strategy to ensure we hit the global minimum.
The function is convex, so any local minimum is also the global minimum.