  This work elaborates on the TRust-region-ish (TRish) algorithm, a stochastic
optimization method for finite-sum minimization problems proposed by Curtis et
al. in [Curtis2019, Curtis2022]. A theoretical analysis that complements the
results in the literature is presented, and the issue of tuning the involved
hyper-parameters is investigated. Our study also focuses on a practical version
of the method, which computes the stochastic gradient by means of the inner
product test and the orthogonality test proposed by Bollapragada et al. in
[Bollapragada2018]. It is shown experimentally that this implementation
improves the performance of TRish and reduces its sensitivity to the choice of
the hyper-parameters.
