Generalized gradient flows in Hadamard manifolds and convex optimization on entanglement polytopes

TL;DR

This paper develops a generalized gradient flow framework on Hadamard manifolds for convex optimization problems, with applications to entanglement polytopes and tensor-related quantum problems.

Contribution

It introduces a new class of gradient flows for convex functions on Hadamard manifolds, extending previous work and applying it to quantum tensor optimization problems.

Findings

Gradient flow attains infimum of Q(df_x) on basic manifolds.

Establishes duality relations in the generalized gradient flow.

Applies to convex optimization in quantum entanglement polytopes.

Abstract

In this paper, we address the optimization problem of minimizing $Q(df_x)$ over a Hadamard manifold ${\cal M}$, where $f$ is a convex function on ${\cal M}$, $df_x$ is the differential of $f$ at $x \in {\cal M}$, and $Q$ is a function on the cotangent bundle of ${\cal M}$. This problem generalizes the problem of minimizing the gradient norm $\|\nabla f(x)\|$ over ${\cal M}$, studied by Hirai and Sakabe FOCS2024. We formulate a natural class of $Q$ in terms of convexity and invariance under parallel transports, and introduce a generalization of the gradient flow of $f$ that is expected to minimize $Q(df_x)$. For basic classes of manifolds, including the product of the manifolds of positive definite matrices, we prove that this gradient flow attains $\inf_{x\in {\cal M}} Q(df_x)$ in the limit, and yields a duality relation. This result is applied to the Kempf-Ness optimization for GL-actions on tensors, which is Euclidean convex optimization on the class of moment polytopes, known as the entanglement polytopes. This type of convex optimization arises from tensor-related subjects in theoretical computer science, such as quantum functional, $G$-stable rank, and noncommutative rank.