# Collaborative Research: CIF: Medium: Post-Modern Min-Max Optimization Theory: Departure from Classical Minimization Theory

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Johns Hopkins University (MD) · $525,000

## Abstract

Min-max optimization underpins technologies ranging from generative Artificial Intelligence (AI) to large-scale reinforcement learning, yet today’s methods remain slow and unreliable for many real-world tasks. This suboptimality stems from the traditional approach of adapting minimization techniques to the min-max setup, which necessarily overlooks the unique complexities inherent in min-max problems. This project fundamentally revises this approach, developing specialized theoretical frameworks and efficient algorithms tailored explicitly to min-max optimization. By establishing a deeper understanding of these unique characteristics, the proposed research will significantly enhance the efficiency and robustness of min-max optimization, directly impacting practical applications in machine learning and artificial intelligence.
 
Technically, this project will first explore core theoretical foundations under idealized convex-concave conditions, emphasizing accelerated convergence through anchor-type algorithms and enhanced stochastic methods with relaxed assumptions. Building upon this, the project will also develop practical algorithms that are robust to realistic, non-ideal conditions, including methods for nonconvex problems, efficient sampling strategies for stochastic settings, and adaptive update rules. Additionally, the research will investigate efficient alternating-update strategies, proximal gradient-type methods, and applications to training deep neural networks. T

## Key facts

- **NSF award ID:** 2504626
- **Awardee organization:** Johns Hopkins University (MD)
- **SAM.gov UEI:** FTMTDMBR29C7
- **PI:** Nicolas Loizou
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Machine Learning Theory, COMM & INFORMATION FOUNDATIONS, MEDIUM PROJECT, SIGNAL PROCESSING
- **Estimated total:** $525,000
- **Funds obligated:** $525,000
- **Transaction type:** Standard Grant
- **Period:** 08/15/2025 → 07/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2504626

## Citation

> US National Science Foundation, Award 2504626, Collaborative Research: CIF: Medium: Post-Modern Min-Max Optimization Theory: Departure from Classical Minimization Theory. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2504626. Licensed CC0.

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