Efficient Convex Optimization Requires Superlinear Memory

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Marsden, Annie, Sharan, Vatsal, Sidford, Aaron, Valiant, Gregory
Formato: Preprint
Publicado: 2022
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866909265735712768
author Marsden, Annie
Sharan, Vatsal
Sidford, Aaron
Valiant, Gregory
author_facet Marsden, Annie
Sharan, Vatsal
Sidford, Aaron
Valiant, Gregory
contents We show that any memory-constrained, first-order algorithm which minimizes $d$-dimensional, $1$-Lipschitz convex functions over the unit ball to $1/\mathrm{poly}(d)$ accuracy using at most $d^{1.25 - δ}$ bits of memory must make at least $\tildeΩ(d^{1 + (4/3)δ})$ first-order queries (for any constant $δ\in [0, 1/4]$). Consequently, the performance of such memory-constrained algorithms are a polynomial factor worse than the optimal $\tilde{O}(d)$ query bound for this problem obtained by cutting plane methods that use $\tilde{O}(d^2)$ memory. This resolves a COLT 2019 open problem of Woodworth and Srebro.
format Preprint
id arxiv_https___arxiv_org_abs_2203_15260
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Efficient Convex Optimization Requires Superlinear Memory
Marsden, Annie
Sharan, Vatsal
Sidford, Aaron
Valiant, Gregory
Machine Learning
Computational Complexity
Data Structures and Algorithms
Optimization and Control
We show that any memory-constrained, first-order algorithm which minimizes $d$-dimensional, $1$-Lipschitz convex functions over the unit ball to $1/\mathrm{poly}(d)$ accuracy using at most $d^{1.25 - δ}$ bits of memory must make at least $\tildeΩ(d^{1 + (4/3)δ})$ first-order queries (for any constant $δ\in [0, 1/4]$). Consequently, the performance of such memory-constrained algorithms are a polynomial factor worse than the optimal $\tilde{O}(d)$ query bound for this problem obtained by cutting plane methods that use $\tilde{O}(d^2)$ memory. This resolves a COLT 2019 open problem of Woodworth and Srebro.
title Efficient Convex Optimization Requires Superlinear Memory
topic Machine Learning
Computational Complexity
Data Structures and Algorithms
Optimization and Control
url https://arxiv.org/abs/2203.15260