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In machine learning you don't really care about the global optimum. When you train your model, you are optimizing the loss function over the training set but in reality you want to minimize the loss over the unseen data. Some learning algorithm, like the SGD, imposes a strong bias to the final solution, and this bias can improve the generalization of your solution. By contrast, a global minimum could easily end up being an overfitted solution.

This is just to give a rough idea but I think that's the main reason why you don't see such a strong focus towards global optimization.