Cholesky-Based Inversion Techniques

• Cholesky-based inversion is a method that uses the Cholesky decomposition to compute the inverse of HPD matrices, ensuring numerical stability and efficiency.
• It exploits sparsity and block structures, along with recursive update strategies, to optimize computations in applications like Gaussian process inference and precision estimation.
• Advanced variants incorporate dynamic updates, complex and dyadic extensions, and parallel processing to enhance performance in real-time adaptive systems.

Cholesky-based inversion refers to the family of algorithms and methodologies that use the Cholesky decomposition to compute the inverse (or pseudo-inverse) of Hermitian positive-definite (HPD) matrices and their structured extensions. The approach is characterized by its computational efficiency, numerical stability, and ability to exploit sparsity or block structure. Cholesky-based inversion forms the backbone of large-scale Gaussian process inference, sparse precision estimation, streaming pseudo-inverse algorithms, fast linear system solvers, complex matrix inversion, and parallel scientific computing.

1. Mathematical Principles and Standard Algorithms

Let $A \in \mathbb{R}^{n \times n}$ be symmetric positive-definite. The Cholesky decomposition provides a lower-triangular $L$ such that $A=L L^\top$, or equivalently an upper-triangular $R$ such that $A=R^\top R$. The inversion of $A$ follows directly as $A^{-1} = L^{-\top} L^{-1} = R^{-1} R^{-\top}$. The canonical algorithm computes $L$ (or $R$) via recursive block or tiled strategies (POTRF), then obtains $L^{-1}$ via block or column-wise backward substitution (TRTRI), finally accumulating $A^{-1}$ as $B=L^{-\top}L^{-1}$ (LAUUM) (Bouwmeester et al., 2010, Krishnamoorthy et al., 2011).

For dense matrices, the operation requires $O(n^3)$ flops with well-behaved numerical errors. Storage can be optimized via rectangular full packed format (RFPF), halving memory and maintaining full Level-3 BLAS throughput (0901.1696).

2. Sparse and Structured Inverse Cholesky Factorization

When $A$ is large and sparse (often arising as covariance or precision matrices), direct inversion is infeasible both in time and memory. Sparse Cholesky-based inversion algorithms, such as those minimizing Kullback-Leibler (KL) divergence between $\mathcal{N}(0, A)$ and $\mathcal{N}(0, L^{-\top} L^{-1})$ subject to prescribed sparsity, yield explicit closed-form solutions for $L$ through Schur complement inverses of local submatrices (Schäfer et al., 2020, Huan et al., 2023, Kang et al., 2021). These methods generalize the Vecchia approximation, central in spatial statistics, and can be computed in $O(N \log(N/\epsilon)^d)$ space and $O(N\log(N/\epsilon)^{2d})$ time, with $d$ the intrinsic dimension.

Greedy mutual-information selection of sparsity patterns, correlated conditioning distances, and supernode aggregation further accelerate computation for complex or non-Euclidean kernels. In block sparse contexts, block-Cholesky decomposition unifies modified Cholesky and Graphical lasso approaches for inverse covariance estimation under partial variable orderings and group structure (Kang et al., 2023).

3. Recursive and Dynamic Cholesky Inversion Algorithms

Dynamic or streaming applications necessitate updating Cholesky factors and their inverses as $A$ evolves, e.g., in wireless MIMO channel estimation or sequential learning. The modified recursive Cholesky (RChol) algorithm provides explicit, rank-1 update formulas for the factors, eschewing full recomputation (Pawar et al., 2017). Pseudoinverse updates on partitioned matrices leverage inverse-Cholesky construction for efficient one-step Greville extension, achieving $O(m n p + m p^2 + p^3)$ complexity for matrix augmentations, with enhanced stability (Zhu, 2020).

For square-root filtering and detection in V-BLAST/MIMO-OFDM, efficient inverse-Cholesky recursions avoid explicit back-substitution, halving divisions and achieving $3.9–5.2\times$ speed gains over previous algorithms, while preserving backward stability (Zhu et al., 2020).

4. Complex, Block, and Dyadic Extensions

Cholesky-based inversion extends beyond real SPD matrices to Hermitian PD cases and associated quadratic extension fields. Frobenius inversion for $X=A+iB$ with $X$ HPD utilizes two real Cholesky factorizations, real triangular solves, and multiplications, outperforming standard complex Cholesky inversion by $\sim22\%$ in leading order (Dai et al., 2022). Block Cholesky decomposition supports sparse inverse covariance estimation under partial information, with theoretical guarantees for positive-definiteness and consistency in high dimensions (Kang et al., 2023).

Recent developments exploit hidden dyadic sparsity structures, revealed via $\ell_1$-packing and recursive block-separator detection, resulting in inversion algorithms of cost $O(d k^2 \log^2(d/k))$ for matrices with dyadic structure (Kos et al., 13 May 2025). These techniques unify nested dissection, block tridiagonal forms, and sparse Gram-Schmidt orthogonalization under a common algebraic framework.

5. Computational Complexity, Parallelism, and Robustness

Cholesky-based inversion is amenable to efficient parallelization and critical-path minimization. Tiled algorithms (POTRF/TRTRI/LAUUM) and variant selection drive performance, with block sizes and in-place/out-of-place DAG structures determining the theoretical lower bounds on execution time. In realistic multicore environments, empirical speedups are tightly predicted by critical-path (CP) analysis (Bouwmeester et al., 2010).

Numerical stability is central: Cholesky factorization is inherently backward stable for HPD inputs. Inverse-Cholesky updates, whether recursive, block, or packed, maintain bounded errors provided pivots remain well-conditioned. Single-solve shortcuts marginally improve fixed-point stability over redundant solves, and block or dyadic variants avoid excessive condition-number inflation by operating on carefully structured subproblems (Krishnamoorthy et al., 2011, Zhu, 2020, Dai et al., 2022, Kos et al., 13 May 2025).

6. Applications and Implementation Considerations

Cholesky-based inversion methods underpin Gaussian process regression, conditional simulation, linear system solvers, streaming estimation in adaptive communications, covariance estimation in statistics, and preconditioning for iterative solvers.

Implementation relies on exploiting suitable data structures (RFPF, banded/dyadic storage), structured neighbor searches (k-d tree, ball tree, $L_1$ or correlation distances), BLAS/LAPACK calls for small dense solves, and algorithmic groupings (supernodes, block conditioning).

In practice, hyperparameters such as neighbor size $m$, radius $\rho$, or block/aggregation strategy balance sparsity against accuracy. Numerical experiments across diverse regimes demonstrate that Cholesky-based inversion variants consistently outperform alternatives (QR, LU, SVD, Gauss-Jordan, glasso), especially as scale, sparsity, and complexity increase (Schäfer et al., 2020, Huan et al., 2023, Kang et al., 2021, 0901.1696, Kang et al., 2023, Kos et al., 13 May 2025, Zhu et al., 2020).

7. Summary and Comparative Table

Cholesky-based inversion methods facilitate scalable, stable, and interpretable inverse computations for structured matrices in high-dimensional settings. They adapt to dynamic updates, exploit sparsity and block structure, extend to complex and group-wise contexts, and offer leading performance in parallel and real-time environments.

Variant/Class	Main Idea/Structure	Leading-Order Cost
Dense Cholesky-Inverse	$A^{-1}=L^{-\top}L^{-1}$	$O(n^3)$
Sparse KL-Minimization	$L$ via local Schur	$O(n\log(n/\epsilon)^{2d})$
Vecchia/Correlation-based	Ordered conditional	$O(nm^3)$
RChol (Recursive)	Rank-1 updates	$O(n^2)$ per update
Block/Group Cholesky	Partial ordering/groups	$O(\sum_{i<j}p_ip_j)$
Frobenius Complex Inv	$A+iB$ via 2 real Cholesky	$0.78$ of complex Cholesky
Dyadic/Separator-based	$\ell_1$ packing, block GS	$O(dk^2\log^2(d/k))$

These methods, driven by advances in algorithmic theory and practical implementation, comprise a foundational component of modern numerical linear algebra and large-scale statistical analysis.