International audienceWe describe a convergence acceleration technique for generic optimization problems. Our schemecomputes estimates of the optimum from a nonlinear average of the iterates produced by any optimizationmethod. The weights in this average are computed via a simple linear system, whose solution can be updatedonline. This acceleration scheme runs in parallel to the base algorithm, providing improved estimates of thesolution on the fly, while the original optimization method is running. Numerical experiments are detailed onclassical classification problems
