2 papers
cs.LG2026
Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression
Artur Zagitov, Alexander Miasnikov, Maxim Krutikov +5
Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints. Tensor decompositions have emerged as a promising direction, off…
math.OC2025
Adaptive Regularized Newton Method with Inexact Hessian
Aleksandr Shestakov, Nail Bashirov, Andrei Semenov +4
Newton's method is the most widespread high-order method, demanding the gradient and the Hessian of the objective function. However, one of the main disadvantages of Newtons method…