On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

Trend · papers per month

count % of corpus

4 results for “block-preconditioning”

Scalable Mean-Field Variational Inference via Preconditioned Primal-Dual Optimizationstat.ML

New algorithm speeds up large-scale statistical inference.

problem Efficiently solving large-scale mean-field variational inference problems.

method Developed a novel primal-dual algorithm (PD-VI) and a block-preconditioned extension (P

^2

D-VI) for mean-field variational inference.

result PD-VI and P

^2

D-VI achieve faster convergence and better solution quality compared to existing methods.

mean-field variational inference primal-dual optimization scalable inference block-preconditioning convergence guarantees

2026-02-07abs ↗pdf ↗

Graph Metric Learning via Gershgorin Disc Alignmentcs.LG

A fast metric learning framework using Gershgorin disc alignment.

problem Learning effective metrics for graph-based data.

method Fast projection-free metric learning via Gershgorin disc alignment.

result Efficiently computed graph metric matrices outperform competing methods.

Graph metric learning Metric learning Gershgorin disc alignment Frank-Wolfe iterations Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG)

2020-01-28abs ↗pdf ↗

Signed Graph Metric Learning via Gershgorin Disc Perfect Alignmentcs.LG

A new metric learning framework for signed graphs using Gershgorin disc alignment.

problem Learning Mahalanobis metrics from signed graphs efficiently.

method Proposes a fast metric learning framework using Gershgorin disc perfect alignment (GDPA) to circumvent full eigen-decomposition.

result Proves that Gershgorin disc left-ends of similarity transform are perfectly aligned at the smallest eigenvalue, enabling efficient optimization.

signed graphs metric learning Gershgorin disc Frank-Wolfe method Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG)

2020-06-15abs ↗pdf ↗

Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challengecs.MS

Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is demonstrated to efficiently solve eigenvalue problems for graph Laplacians that appear in spectral clustering. For static graph partitioning, 10-20 iterations of LOBPCG without preconditioning result in ~10x error reduction, enough to achieve 100% corr…

2017-08-21abs ↗pdf ↗