An adaptive polyak heavy-ball method

The heavy-ball (HB) method has become a well-known practice for large-scale machine learning problems, and it can achieve the fastest local convergence rate when objective functions are smooth and strongly convex using Polyak’s optimal hyper-parameters. However, such convergence rates are based on specific uncertain and time-invariant hyper-parameters that limit its potential. In this paper, we propose an adaptive HB that estimates the Polyak’s optimal hyper-parameters at each iteration. Our adaptive approach employs the absolute differences of current and previous model parameters and their gradients. Such representation allows for a computationally efficient optimizer. We show that our method guarantees a global linear convergence rate for smooth and strongly convex objective functions. Whereas in the stochastic setting, we show that proposed stochastic algorithm converges almost surely for non-convex smooth functions with bounded gradient. We validate the effectiveness of our method on image classification datasets with no empirical tuning, and its superiority on quadratic and non-convex functions while comparing its performance to the state-of-the-art optimizers.