We introduce a general framework for large-scale model-based derivative-free optimization
based on iterative minimization within random subspaces. We present a probabilistic worst …

Derivative-free algorithms seek the minimum value of a given objective function without
using any derivative information. The performance of these methods often worsens as the …

We propose a general random subspace framework for unconstrained nonconvex
optimization problems that requires a weak probabilistic assumption on the subspace …

This work proposes a framework for large-scale stochastic derivative-free optimization (DFO)
by introducing STARS, a trust-region method based on iterative minimization in random …

A class of second-order algorithms is proposed for minimizing smooth nonconvex functions
that alternates between regularized Newton and negative curvature steps in an iteration …

While there already exist randomized subspace Newton methods that restrict the search
direction to a random subspace for a convex function, we propose a randomized subspace …

Derivative-free algorithms seek the minimum of a given function based only on function
values queried at appropriate points. Although these methods are widely used in practice …

We propose an algorithmic framework, that employs active subspace techniques, for
scalable global optimization of functions with low effective dimension (also referred to as low …

The work presented here is motivated by the development of StoDARS, a framework for
large-scale stochastic blackbox optimization that not only is both an algorithmic and …

We consider model-based derivative-free optimization (DFO) for large-scale problems,
based on iterative minimization in random subspaces. We provide the first worst-case …