A Unique Inverse Decomposition of Positive Definite Matrices under Linear Constraints

TL;DR

This paper introduces a unique inverse decomposition of positive definite matrices constrained by linear subspaces, with theoretical guarantees, computational algorithms, and applications in finance.

Contribution

It presents a novel, unique decomposition method for positive definite matrices under linear constraints, with a variational characterization and efficient algorithms.

Findings

Proves existence and uniqueness of the decomposition under nondegeneracy conditions.

Develops Newton-type algorithms with convergence guarantees.

Demonstrates applications in exponential utility maximization in finance.

Abstract

We study a nonlinear decomposition of a positive definite matrix into two components: the inverse of another positive definite matrix and a symmetric matrix constrained to lie in a prescribed linear subspace. Equivalently, the inverse component is required to belong to the orthogonal complement of that subspace with respect to the trace inner product. Under a sharp nondegeneracy condition on the subspace, we show that every positive definite matrix admits a \emph{unique} decomposition of this form. This decomposition admits a variational characterization as the unique minimizer of a strictly convex log-determinant optimization problem, which in turn yields a natural dual formulation that can be efficiently exploited computationally. We derive several properties, including the stability of the decomposition. We further develop feasibility-preserving Newton-type algorithms with provable convergence guarantees and analyze their per-iteration complexity in terms of algebraic properties of the decomposed matrix and the underlying subspace. Finally, we show that the proposed decomposition arises naturally in exponential utility maximization, a central problem in mathematical finance.