A global minimum refers to the lowest point of a function over its entire domain, meaning it is the best possible solution to an optimization problem. Identifying a global minimum is crucial because it ensures that the solution is not just locally optimal, which can occur in complex landscapes with multiple minima. Finding this point relates closely to various concepts like problem formulation, the characteristics of convex functions, optimization techniques, and applications in training neural networks.
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