arXiv subjects

David Huckleberry Gutman

Publications and source records attributed to David Huckleberry Gutman.

5 recordsLinked to original sources

arXiv2026

An Invitation to Higher-Order Riemannian Optimization: Optimal and Implementable Methods

This paper presents the first optimal-rate $p$-th order methods with $p\geq 1$ for finding first and second-order stationary points of non-convex smooth objective functions over Riemannian manifolds. In contrast to the geodesically convex setting, we definitively establish that the optimal oracle complexity of non-convex optimization over manifolds matches that over Euclidean space. In parallel with the complexity analysis, we introduce a general framework for systematically studying higher-order regularity on Riemannian manifolds that characterizes its joint dependence on the objective function and the chosen retraction. To the best of our knowledge, this framework constitutes the first known application in optimization of pullback connections and the Sasaki metric to the study of retraction-based pullbacks of the objective function. We provide clean derivative bounds based on a new covariant Faà di Bruno formula derived within our framework. For $p=3$, our methods are fully implementable via a new Krylov-based framework for minimizing quartically regularized cubic polynomials. This is the first Krylov method for this class of polynomials and may be of independent interest beyond Riemannian optimization.

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arXiv2024

The Randomized Block Coordinate Descent Method in the Hölder Smooth Setting

This work provides the first convergence analysis for the Randomized Block Coordinate Descent method for minimizing a function that is both Hölder smooth and block Hölder smooth. Our analysis applies to objective functions that are non-convex, convex, and strongly convex. For non-convex functions, we show that the expected gradient norm reduces at an $O\left(k^{\fracγ{1+γ}}\right)$ rate, where $k$ is the iteration count and $γ$ is the Hölder exponent. For convex functions, we show that the expected suboptimality gap reduces at the rate $O\left(k^{-γ}\right)$. In the strongly convex setting, we show this rate for the expected suboptimality gap improves to $O\left(k^{-\frac{2γ}{1-γ}}\right)$ when $γ>1$ and to a linear rate when $γ=1$. Notably, these new convergence rates coincide with those furnished in the existing literature for the Lipschitz smooth setting.

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arXiv2023

Quantum Enhanced Pattern Search Optimization

This paper introduces a quantum-classical hybrid algorithm for generalized pattern search (GPS) algorithms. We introduce a quantum search step algorithm using amplitude amplification, which reduces the number of oracle calls needed during the search step from O(N) classical calls to O(N^(1/2)) quantum calls. This work addresses three fundamental issues with using a quantum search step with GPS. First we address the need to mark an improved mesh point, a requirement of the amplitude amplification algorithm. Second, we introduce a modified version of the amplitude amplification algorithm QSearch, which is guaranteed to terminate using a finite number of iterations. Third, we avoid disrupting the GPS algorithm's convergence by limiting the quantum algorithm to the search step.

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arXiv2022

The Inexact Cyclic Block Proximal Gradient Method and Properties of Inexact Proximal Maps

This paper expands the Cyclic Block Proximal Gradient method for block separable composite minimization by allowing for inexactly computed gradients and proximal maps. The resultant algorithm, the Inexact Cyclic Block Proximal Gradient (I-CBPG) method, shares the same convergence rate as its exactly computed analogue provided the allowable errors decrease sufficiently quickly or are pre-selected to be sufficiently small. We provide numerical experiments that showcase the practical computational advantage of I-CBPG for certain fixed tolerances of approximation error and for a dynamically decreasing error tolerance regime in particular. We establish a tight relationship between inexact proximal map evaluations and $δ$-subgradients in our $δ$-Second Prox Theorem. This theorem forms the foundation of our convergence analysis and enables us to show that inexact gradient computations and other notions of inexact proximal map computation can be subsumed within a single unifying framework.

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arXiv2020

Coordinate Descent Without Coordinates: Tangent Subspace Descent on Riemannian Manifolds

We extend coordinate descent to manifold domains, and provide convergence analyses for geodesically convex and non-convex smooth objective functions. Our key insight is to draw an analogy between coordinate blocks in Euclidean space and tangent subspaces of a manifold. Hence, our method is called tangent subspace descent (TSD). The core principle behind ensuring convergence of TSD is the appropriate choice of subspace at each iteration. To this end, we propose two novel conditions, the gap ensuring and $C$-randomized norm conditions on deterministic and randomized modes of subspace selection respectively, that promise convergence for smooth functions and that are satisfied in practical contexts. We propose two subspace selection rules of particular practical interest that satisfy these conditions: a deterministic one for the manifold of square orthogonal matrices, and a randomized one for the Stiefel manifold. Our proof-of-concept numerical experiments on the orthogonal Procrustes problem demonstrate TSD's efficacy.

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