We present global convergence rates for a line-search method which is based on random first-order models and directions whose quality is ensured only with certain probability. We show that in terms of the order of the accuracy, the evaluation complexity of such a method is the same as its counterparts that use deterministic accurate models; the use of probabilistic models only increases the complexity by a constant, which depends on the probability of the models being good. We particularize and improve these results in the convex and strongly convex case. We also analyze a probabilistic cubic regularization variant that allows approximate probabilistic second-order models and show improved complexity bounds compared to probabilistic first-order methods; again, as a function of the accuracy, the probabilistic cubic regularization bounds are of the same (optimal) order as for the deterministic case.
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Note that throughout, \(f(x^k)\ne f_k\), since \(f_k=F_k(\omega _k)\) is a related measure of progress towards optimality.
Note that a recently-proposed cubic regularization variant  can dispense with the approximate global minimization condition altogether while maintaining the optimal complexity bound of ARC. A probabilistic variant of  can be constructed similarly to probabilistic ARC, and our analysis here can be extended to provide same-order complexity bounds.
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We would like to thank Alexander Stolyar for helpful discussions on stochastic processes. We also would like to thank Zaikun Zhang, who was instrumental in helping us significantly simplify the analysis of the stochastic process in Sect. 2.
The work of C. Cartis was partially supported by the Oxford University EPSRC Platform Grant EP/I01893X/1. The work of K. Scheinberg is partially supported by NSF Grants DMS 10-16571, DMS 13-19356, CCF-1320137, AFOSR Grant FA9550-11-1-0239, and DARPA Grant FA 9550-12-1-0406 negotiated by AFOSR.
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Cartis, C., Scheinberg, K. Global convergence rate analysis of unconstrained optimization methods based on probabilistic models. Math. Program. 169, 337–375 (2018). https://doi.org/10.1007/s10107-017-1137-4
- Line-search methods
- Cubic regularization methods
- Random models
- Global convergence analysis
Mathematics Subject Classification