Convergence of Implementable Descent Algorithms for Unconstrained Optimization

Abstract

Descent algorithms use sufficient descent directions combined with stepsize rules, such as the Armijo rule, to produce sequences of iterates whose cluster points satisfy some necessary optimality conditions. In this note, we present a proof that the whole sequence of iterates converges for quasiconvex objective functions.