  In this work, to efficiently help escape the stationary and saddle points, we
propose, analyze, and generalize a stochastic strategy performed as an operator
for a first-order gradient descent algorithm in order to increase the target
accuracy and reduce time consumption. Unlike existing algorithms, the proposed
stochastic the strategy does not require any batches and sampling techniques,
enabling efficient implementation and maintaining the initial first-order
optimizer's convergence rate, but provides an incomparable improvement of
target accuracy when optimizing the target functions. In short, the proposed
strategy is generalized, applied to Adam, and validated via the decomposition
of biomedical signals using Deep Matrix Fitting and another four peer
optimizers. The validation results show that the proposed random strategy can
be easily generalized for first-order optimizers and efficiently improve the
target accuracy.
