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Ermolieva, T., Ermoliev, Y., Obersteiner, M. and Rovenskaya, E. (2021) Chapter 4 Two-Stage Nonsmooth Stochastic Optimization and Iterative Stochastic Quasigradient Procedure for Robust Estimation, Machine Learning and Decision Making. In: Roberts, F.S. and Sheremet, I.A., Eds., Resilience in the Digital Age, Springer, 45-74.
https://doi.org/10.1007/978-3-030-70370-7_4

has been cited by the following article:

• TITLE: A Perspective on Stochastic Search Efficiency via Quasigradient Techniques in Constrained Models

  AUTHORS: Gilberto Pérez-Lechuga

  KEYWORDS: Fejer Successions, Integer Programming, Stochastic Convergence Techniques, Random Search

  JOURNAL NAME: American Journal of Operations Research, Vol.15 No.6, November 7, 2025

  ABSTRACT: This article examines some of the properties of quasi-Fejer sequences when used in quasi-gradiental techniques as an alternative to stochastic search techniques for optimizing unconstrained mathematical programming models. The convergence and efficiency of the method are analyzed, and its potential use as an interior-point algorithm for optimizing integer linear programming models is explored, ensuring the feasibility of the solution at each stage of the search. To achieve this, it is proposed to remain within the feasible region by using small perturbations around the points found until convergence is reached. This alternative is compared with the traditional Branch and Bound method using software programs available for this purpose. The results obtained suggest that the technique, applied to models with few variables, is inefficient but is practical for large-scale models, since simple changes in the components of the located points generate a feasible sequence that almost always converges.