Near linear time 5/3-approximation algorithms for two-level power assignment problems. - Benjamin Grimmer; Kan Qiao (FOMC, 2014)

Design and evaluation of the gemtc framework for GPU-enabled many-task computing. - Scott J. Krieder; Justin M. Wozniak; Timothy G. Armstrong; Michael Wilde; Daniel S. Katz; Benjamin Grimmer; Ian T. Foster; Ioan Raicu (HPDC, 2014)

Dual-Fitting Approximation Algorithms for Network Connectivity Problems. - Benjamin Grimmer (CoRR, 2015)

Nash equilibrium and the price of anarchy in priority based network routing. - Benjamin Grimmer; Sanjiv Kapoor (INFOCOM, 2016)

Improved approximation algorithms for single-tiered relay placement. - Gruia Calinescu; Benjamin Grimmer; Satyajayant Misra; Sutep Tongngam; Guoliang Xue; Weiyi Zhang 001 (J. Comb. Optim., 2016)

Economic inefficiency in resource allocation games. - Praneeth Tota; Sanjiv Kapoor; Benjamin Grimmer (Allerton, 2017)

Dual-Based Approximation Algorithms for Cut-Based Network Connectivity Problems. - Benjamin Grimmer (Algorithmica, 2018)

Radial Subgradient Method. - Benjamin Grimmer (SIAM J. Optim., 2018)

Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems. - Damek Davis; Benjamin Grimmer (SIAM J. Optim., 2019)

Convergence Rates for Deterministic and Stochastic Subgradient Methods without Lipschitz Continuity. - Benjamin Grimmer (SIAM J. Optim., 2019)

The Landscape of Nonconvex-Nonconcave Minimax Optimization. - Benjamin Grimmer; Haihao Lu; Pratik Worah; Vahab S. Mirrokni (CoRR, 2020)

Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems. - Benjamin Grimmer; Haihao Lu; Pratik Worah; Vahab S. Mirrokni (ALT, 2022)

A Simple Nearly Optimal Restart Scheme For Speeding Up First-Order Methods. - James Renegar; Benjamin Grimmer (Found. Comput. Math., 2022)

Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets. - Ning Liu; Benjamin Grimmer (CoRR, 2023)

Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization. - Benjamin Grimmer; Danlin Li (CoRR, 2023)

The landscape of the proximal point method for nonconvex-nonconcave minimax optimization. - Benjamin Grimmer; Haihao Lu; Pratik Worah; Vahab Mirrokni (Math. Program., 2023)

Optimal Convergence Rates for the Proximal Bundle Method. - Mateo Díaz; Benjamin Grimmer (SIAM J. Optim., 2023)

General Hölder Smooth Convergence Rates Follow from Specialized Rates Assuming Growth Bounds. - Benjamin Grimmer (J. Optim. Theory Appl., 2023)

Revisiting Spectral Bundle Methods: Primal-Dual (Sub)linear Convergence Rates. - Lijun Ding; Benjamin Grimmer (SIAM J. Optim., 2023)

Provably Faster Gradient Descent via Long Steps. - Benjamin Grimmer (SIAM J. Optim., 2024)

On the Linear Convergence of Extragradient Methods for Nonconvex-Nonconcave Minimax Problems. - Saeed Hajizadeh; Haihao Lu; Benjamin Grimmer (INFORMS J. Optim., 2024)

On optimal universal first-order methods for minimizing heterogeneous sums. - Benjamin Grimmer (Optim. Lett., 2024)

Radial duality part I: foundations. - Benjamin Grimmer (Math. Program., 2024)

Radial duality part II: applications and algorithms. - Benjamin Grimmer (Math. Program., 2024)

Accelerated Objective Gap and Gradient Norm Convergence for Gradient Descent via Long Steps. - Benjamin Grimmer; Kevin Shu; Alex L. Wang (INFORMS J. Optim., 2025)

First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or without Slater Points. - Zhichao Jia; Benjamin Grimmer (SIAM J. Optim., 2025)

Goldstein stationarity in Lipschitz constrained optimization. - Benjamin Grimmer; Zhichao Jia (Optim. Lett., 2025)