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What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Cholesky decomposition for symmetric matrices, Riemannian...
Apoorva Khare, Prateek Kumar Vishwakarma · 2025-08-01 · via math.ST updates on arXiv.org

For each $n \geq 1$ and sign pattern $ε\in \{ \pm 1 \}^n$, we introduce a cone of real symmetric matrices $LPM_n(ε)$: those with leading principal $k \times k$ minors of signs $ε_k$. These cones are pairwise disjoint and their union $LPM_n$ is an open dense cone in all symmetric matrices; they subsume positive and negative definite matrices, and symmetric (P-,) N-, PN-, almost P-, and almost N- matrices. We show that each $LPM_n$ matrix $A$ admits an uncountable family of Cholesky-type factorizations - yielding a unique lower triangular matrix $L$ with positive diagonals - with additional attractive properties: (i) each such factorization is algorithmic; and (ii) each such Cholesky map $A \mapsto L$ is a smooth diffeomorphism from $LPM_n(ε)$ onto an open Euclidean ball. We then show that (iii) the (diffeomorphic) balls $LPM_n(ε)$ are isometric Riemannian manifolds as well as isomorphic abelian Lie groups, each equipped with a translation-invariant Riemannian metric (and hence Riemannian means/barycentres). Moreover, (iv) this abelian metric group structure on each $LPM_n(ε)$ - and hence the log-Cholesky metric on Cholesky space - yields an isometric isomorphism onto a finite-dimensional Euclidean space. The complex version of this also holds. In the latter part, we show that the abelian group $PD_n$ of positive definite matrices, with its bi-invariant log-Cholesky metric, is precisely the identity-component of a larger group with an alternate metric: the open dense cone $LPM_n$. This also holds for Hermitian matrices over several subfields $\mathbb{F} \subseteq \mathbb{C}$. As a result, (v) the groups $LPM_n^{\mathbb{F}}$ and $LPM_\infty^{\mathbb{F}}$ admit a rich probability theory, and the cones $LPM_n(ε), TPM_n(ε)$ admit Wishart densities with signed Bartlett decompositions.