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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.

168,657 papers · 148 categories

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4 results for “Laplacian-constrained”

Does the

\ell_1

-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?cs.LG

The $\ell_1$ -norm fails to produce sparse solutions in Laplacian constrained graphical models, leading to a complete graph.

problem Learning a sparse graph under Laplacian constrained Gaussian graphical models.

method Introduced a nonconvex sparsity penalty and proposed a new estimator using a sequence of weighted

\ell_1

-norm penalized sub-problems. Developed a projected gradient descent algorithm with linear convergence rate.

result The proposed estimator can recover the edges correctly with high probability and is effective on both synthetic and real-world data sets.

Graphical Models Laplacian Constrained Sparse Graph Nonconvex Penalty Projected Gradient Descent

2020-06-26abs ↗pdf ↗

Spectral Sparsification of Laplacian-Constrained Gaussian and Hüsler-Reiss Graphical Modelsstat.ME

Spectral sparsification improves Laplacian-constrained graph learning.

problem Improving accuracy of Laplacian-constrained graph learning.

method Spectral graph sparsification as a post-estimation operation.

result Improved accuracy of Laplacian-constrained graph learning.

spectral sparsification graph learning Laplacian-constrained graphical models

2026-06-15abs ↗pdf ↗

Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistencystat.ML

Paper proves conditions for estimating precision matrices with Laplacian constraints.

problem Estimating high-dimensional precision matrices with Laplacian constraints.

method Minimizing Stein's loss with conditions on graph connectivity and Laplacian constraints.

result High-dimensional consistency achieved with Laplacian constraints, independent of graph structure.

precision matrix Stein's loss graph Laplacian high dimensional consistency Laplacian constraints

2021-10-31abs ↗pdf ↗

Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Modelsstat.ML

Paper proposes a method to improve graph clustering by integrating node textual metadata with node signals in GGMs.

problem Graph learning in Gaussian Graphical Models with auxiliary node metadata.

method Laplacian-constrained Gaussian Graphical Models with majorization-minimization algorithm.

result The proposed method outperforms state-of-the-art approaches that use either signals or metadata alone.

graph learning Gaussian Graphical Models metadata node signals majorization-minimization

2026-02-17abs ↗pdf ↗