Blockwise Stabilized Adaptive Cubic Regularization with Subsolvers via Recurrence

Cubic regularized Newton methods have the optimal $\mathcal{O}(ε^{-3/2})$ global rate, but a dense subproblem solve limits the feasible block size. Scalable Cubic Newton variants replace the true block curvature with a diagonal, low-rank, Kronecker-factored, or sketched surrogate and, most often, give up the exact cubic step. We introduce a blockwise optimizer that minimizes an independent cubic model per parameter tensor over the true block Hessian, under a per-block adaptive cubic constant and a monotone guard on the full loss. Arbitrarily large tensors are handled matrix-free in a Lanczos-built Krylov subspace, where we prove that the step minimizes the cubic model. The theory also supplies the $\mathcal{O}(ε^{-3/2})$ iteration complexity bound, a second-order guarantee, and monotone per-block descent. Four variants of this outer scheme are evaluated against the original adaptive regularization with cubics (ARC) optimizer, some other recent cubic Newton variants, Adam, SOAP, and L-BFGS. On a 91.4M-parameter implicit neural representation (INR), the variants introduced in this work are the only evaluated here cubic Newton methods whose steps stay exact on every block. Run to full convergence on FINER 2D image fitting, one of the ARC variants introduced here, ARC-$\varphi_1$, reaches 133.5 dB peak signal-to-noise ratio, while tuned Adam plateaus at 78.2 dB after about 70 minutes. In that time ARC-$\varphi_1$ reaches 95.6 dB.

Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering

Graph convolutional networks (GCNs) have demonstrated significant success in capturing complex user-item relationships for collaborative filtering (CF). However, due to their reliance on extensive model training, training-free graph filtering (GF)-based CF methods have emerged as a promising alternative, offering computational efficiency by smoothing graph signals via matrix operations. In particular, polynomial GF-based approaches demonstrate improved accuracy through their ability to design more expressive and flexible filtering functions. Despite these advantages, existing GF methods suffer from a critical memory bottleneck: they necessitate storing the full item similarity graph, incurring prohibitive memory costs for large-scale datasets, which limits their practical applicability. To tackle this challenge, we propose Mem-GF (Memory-efficient GF), a new GF-based CF method that departs from conventional designs by principally leveraging the structure of Krylov subspaces as a core mechanism for approximating polynomial graph filters without explicitly storing the item similarity graph. We theoretically analyze the minimum Krylov subspace size that guarantees lossless approximation. Through extensive experiments, we demonstrate that Mem-GF achieves up to 5.74$\times$ lower memory usage and 4.38$\times$ speedup in runtime, while consistently exceeding the recommendation accuracy of state-of-the-art GF and GCN-based methods. Mem-GF robustly scales to datasets with tens of millions of interactions, establishing itself as a practically viable and theoretically grounded solution for efficient CF.