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Double descent describes how test error first follows the classic U-shape with increasing model complexity, spikes near the interpolation threshold, and then drops again in the highly overparameterized regime.
Matrix rank is the number of pivots after Gaussian elimination and equals the dimension of both the column space and the row space.