# Exploiting Smooth Substructure in Non-Smooth Stochastic Optimization

> **NSF 01002425DB NSF RESEARCH & RELATED ACTIVIT** · University of California-San Diego (CA) · $300,000

## Abstract

Recent years have seen an unprecedented growth of the use of large data sets in various high impact fields, such as signal processing, imaging, and artificial intelligence. The task of extracting useful information from vast amounts of data typically leads to solving large-scale optimization problems. The size of such problems poses a variety of challenges for computation and is the bottleneck for further progress in applications. The investigator aims to advance techniques of large-scale optimization, with applications throughout science and engineering. The resulting algorithms will enable discovery of trends and patterns in the observed data and will enable accurate predictions about unobserved data. The technical aspects of the project combine elements from a variety of mathematical and applied disciplines, and an effective mix of numerical experimentation, teaching, and discovery is central to the proposal. Graduate students and postdocs will participate in all aspects of the project.

Statistical estimation, signal processing, and learning from data rely on solving challenging optimization problems that are large-scale, stochastic, nonsmooth, and often nonconvex. Despite such irregularity, the domains of typical optimization problems decompose into “active manifolds”, which common algorithms “identify” in finite time, thereby opening the door to second-order acceleration strategies. This project studies the stochastic subgradient method and its common variants, which 

## Key facts

- **NSF award ID:** 2552323
- **Awardee organization:** University of California-San Diego (CA)
- **SAM.gov UEI:** UYTTZT6G9DT1
- **PI:** Dmitriy Drusvyatskiy
- **Primary program:** 01002425DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Machine Learning Theory
- **Estimated total:** $300,000
- **Funds obligated:** $181,124
- **Transaction type:** Continuing Grant
- **Period:** 07/01/2025 → 05/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2552323

## Citation

> US National Science Foundation, Award 2552323, Exploiting Smooth Substructure in Non-Smooth Stochastic Optimization. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2552323. Licensed CC0.

---

*[NSF Awards dataset](/datasets/nsf-awards) · CC0 1.0*