Benjamin Grimmer | DeepAI

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Featured Co-authors

Vahab Mirrokni
84 publications
Ning Liu
46 publications
Haihao Lu
19 publications
Lijun Ding
16 publications
Pratik Worah
6 publications
Danlin Li
1 publication

∙ 07/12/2023

Provably Faster Gradient Descent via Long Steps

This work establishes provably faster convergence rates for gradient des...

0 Benjamin Grimmer, et al. ∙

∙ 05/27/2023

Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization

We consider (stochastic) subgradient methods for strongly convex but pot...

0 Benjamin Grimmer, et al. ∙

∙ 03/09/2023

Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets

We consider feasibility and constrained optimization problems defined ov...

0 Ning Liu, et al. ∙

∙ 10/20/2020

Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems

Unlike nonconvex optimization, where gradient descent is guaranteed to c...

0 Benjamin Grimmer, et al. ∙

∙ 06/15/2020

The Landscape of Nonconvex-Nonconcave Minimax Optimization

Minimax optimization has become a central tool for modern machine learni...

0 Benjamin Grimmer, et al. ∙

∙ 11/11/2019

Bundle Method Sketching for Low Rank Semidefinite Programming

In this paper, we show that the bundle method can be applied to solve se...

0 Lijun Ding, et al. ∙

∙ 12/12/2017

Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity

We generalize the classic convergence rate theory for subgradient method...

0 Benjamin Grimmer, et al. ∙

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