Jonah Langlieb will present his General Exam "Use GPU-based First Order Methods to speed up Benders Decomposed Capacity Expansion Optimization Models" on Thursday, May 28, 2026 at 10:00 AM in CS 302.

Committee Members: Jesse Jenkins (advisor), Felix Heide, Jonathan Mayer, Bartolomeo Stellato

Abstract:

Use GPU-based First Order Methods to speed up Capacity Expansion Optimization Models

Electricity grid expansion is rapidly emerging as one of the most critical infrastructure bottlenecks in the United States, driven by rapid data center growth, increasing cross-sector electrification, and world-wide climate goals. To guide an estimated $479 billion in annual global electricity infrastructure investments [1], system planners, policymakers, and researchers use capacity expansion models (CEMs) to evaluate future grid pathways. These models are formulated as large-scale mixed-integer linear programs (MILPs) and are central to balancing complex trade-offs across grid reliability, cost, and emissions. However, despite advances such as parallel CPU-based Benders decomposition, current approaches still encounter computational limits that restrict the scope of policy analysis, particularly when co-optimizing generation and transmission at realistic spatial and temporal scales.

This work explores how recent advances in GPU computing and GPU-native first-order methods, particularly cuPDLPx [2], can be leveraged to address these challenges. We show that Benders-decomposed CEMs exhibit structural properties such as repeated linear subproblems, shared sparsity patterns, and limited communication, that are well suited to GPU acceleration. We develop and evaluate approaches that apply GPU-accelerated first-order methods to subproblems, the master problem, and the monolithic relaxation, leveraging warm starts and parallelism. By integrating domain knowledge of energy systems, algorithmic structure, and low-level systems engineering, this work provides a scalable foundation for, and opens new avenues toward, generation/transmission co-optimization and higher resolution planning models.

Reading List:

https://docs.google.com/document/d/1jCdk9Cdbaa0qwe-vyOhXJGr-GbhN3NJDry2eS5deHzY/edit?tab=t.0

Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.