Exact gradient methods with memory

The Inexact Gradient Method with Memory (IGMM) is able to considerably outperform the Gradient Method by employing a piece-wise linear lower model on the smooth part of the objective. However, the auxiliary problem can only be solved within a fixed tolerance at every iteration. The need to contain the inexactness narrows the range of problems to which IGMM can be applied and degrades the worst-case convergence rate. In this work, we show how a simple modification of IGMM removes the tolerance parameter from the analysis. The resulting Exact Gradient Method with Memory (EGMM) is as broadly applicable as the Bregman Distance Gradient Method/NoLips and has the same worst-case rate of $$ , the best for its class. Under necessarily stricter assumptions, we can accelerate EGMM without error accumulation yielding an Accelerated Gradient Method with Memory (AGMM) possessing a worst-case rate of $$ . In our preliminary computational experiments EGMM displays excellent performance, sometimes surpassing accelerated methods. When the model discards old information, AGMM also consistently exceeds the Fast Gradient Method.