Speaker
Andrea Angino
(Unidistance Suisse)
Description
We present a two-level trust-region (TLTR) method for solving unconstrained nonlinear optimization problems. The TLTR method employs a composite iteration step based on two distinct search directions: a fine-level direction, derived from minimization in the full high-resolution space, and a coarse-level direction, obtained through minimization in a subspace generated via random projection, enabling accelerated convergence. By blending the strengths of full-space and subspace approaches, the TLTR method aligns with multigrid methodologies that leverage hierarchical representations for efficiency. Numerical experiments, with applications in machine learning, demonstrate the efficacy of the proposed method.
Primary author
Andrea Angino
(Unidistance Suisse)
Co-authors
Ms
Alena Kopanicakova
(Toulouse-INP/IRIT/ANITI)
Mr
Rolf Krause
(Unidistance Suisse)
