Worst-case convergence analysis of relatively inexact gradient descent on smooth convex functions

Abstract: We consider the classical gradient descent algorithm with constant stepsizes, where some error is introduced in the computation of each gradient. More specifically, we assume some relative bound on the inexactness, in the sense that the norm of the difference between the true gradient and its approximate value is bounded by a certain fraction of the gradient norm. This paper presents a worst-case convergence analysis of this so-called relatively inexact gradient descent on smooth convex functions, using the Performance Estimation Problem (PEP) framework. We first derive the exact worst-case behavior of the method after one step. Then we study the case of several steps and provide computable upper and lower bounds using the PEP framework. Finally, we discuss the optimal choice of constant stepsize according to the obtained worst-case convergence rates.