Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization
This work addresses optimization challenges in machine learning where exact function evaluations are infeasible, but it is incremental, building on the existing STP method.
The paper tackles the problem of unconstrained smooth minimization when only approximate function evaluations are available, proposing the MiSTP method, which achieves competitive performance on multiple machine learning tasks.
In this paper, we propose a new zero order optimization method called minibatch stochastic three points (MiSTP) method to solve an unconstrained minimization problem in a setting where only an approximation of the objective function evaluation is possible. It is based on the recently proposed stochastic three points (STP) method (Bergou et al., 2020). At each iteration, MiSTP generates a random search direction in a similar manner to STP, but chooses the next iterate based solely on the approximation of the objective function rather than its exact evaluations. We also analyze our method's complexity in the nonconvex and convex cases and evaluate its performance on multiple machine learning tasks.