  The phenomenon of model-wise double descent, where the test error peaks and
then reduces as the model size increases, is an interesting topic that has
attracted the attention of researchers due to the striking observed gap between
theory and practice \citep{Belkin2018ReconcilingMM}. Additionally, while double
descent has been observed in various tasks and architectures, the peak of
double descent can sometimes be noticeably absent or diminished, even without
explicit regularization, such as weight decay and early stopping. In this
paper, we investigate this intriguing phenomenon from the optimization
perspective and propose a simple optimization-based explanation for why double
descent sometimes occurs weakly or not at all. To the best of our knowledge, we
are the first to demonstrate that many disparate factors contributing to
model-wise double descent (initialization, normalization, batch size, learning
rate, optimization algorithm) are unified from the viewpoint of optimization:
model-wise double descent is observed if and only if the optimizer can find a
sufficiently low-loss minimum. These factors directly affect the condition
number of the optimization problem or the optimizer and thus affect the final
minimum found by the optimizer, reducing or increasing the height of the double
descent peak. We conduct a series of controlled experiments on random feature
models and two-layer neural networks under various optimization settings,
demonstrating this optimization-based unified view. Our results suggest the
following implication: Double descent is unlikely to be a problem for
real-world machine learning setups. Additionally, our results help explain the
gap between weak double descent peaks in practice and strong peaks observable
in carefully designed setups.
