📝 SummarXiv

Abstract

Non-monotone trust-region methods are known to provide additional benefits for scalar and multi-objective optimization, such as enhancing the probability of convergence and improving the speed of convergence. For optimization of set-valued maps, non-monotone trust-region methods have not yet been explored and investigated to see if they show similar benefits. Thus, in this article, we propose two non-monotone trust-region schemes--max-type and average-type for set-valued optimization. Using these methods, the aim is to find \emph{K}-critical points for a non-convex unconstrained set optimization problem through vectorization and oriented-distance scalarization. The main modification in the existing trust region method for set optimization occurs in reduction ratios, where max-type uses the maximum over function values from the last few iterations, and avg-type uses an exponentially weighted moving average of successive previous function values till the current iteration. Under appropriate assumptions, we show the global convergence of the proposed methods. To verify their effectiveness, we numerically compare their performance with the existing trust region method, steepest descent method, and conjugate gradient method using performance profile in terms of three metrics: number of non-convergence, number of iterations, and computation time.