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Can you explain me the difference between both algorithms? They look very similar.

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    It would make more sense if you gave a context in which both of these notions are applied. The conjugate gradient method maintains a small but useful "memory" of what has been computed previously, while the notion of gradient descent only involves a current gradient direction for "descent". That might be the sort of distinction you would want to draw, but it can only become a more useful/concrete distinction in the setting of an intended application.2017-01-26

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