Nonlinear Optimization
Adagrad is an adaptive learning rate optimization algorithm that adjusts the learning rate for each parameter individually based on the historical gradients. This means that parameters with larger gradients will have their learning rates decreased, while those with smaller gradients will have their learning rates increased, making it particularly effective for sparse data and varying feature frequencies. The method promotes efficient training by allowing more attention to be paid to infrequent features while stabilizing updates for frequent ones.
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