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From the 1 of 14 linked papers with an AI index.

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14 papers

quant-ph2026

Hardware-Aware QUBO Reformulation of Constrained Binary Optimization via the Walsh-Fourier Transform

Loong Kuan Lee, Harsha Nagarajan, Thore Gerlach +3

The paper proposes a slack‑free, penalty‑based method that reformulates constrained binary optimization problems into QUBO form using a Walsh‑Fourier projection that respects the c…

math.OC2026

Efficient Graph Partitioning under Resource Constraints: A Cutting-Plane Framework for Distribution Grids

Duong Thuy Anh Nguyen, Harsha Nagarajan, Robert Ferrando +2

This paper presents an optimal network topology control framework using cutting-plane methods for efficient network partitioning with controllable edges. The objective is to enable…

eess.SY2026

Load Block Modeling in Distribution Systems: Network Reconfiguration for Load Restoration

David M. Fobes, Harsha Nagarajan, Manuel Garcia +2

The distribution system restoration (DSR) problem has received considerable attention over the last decade or more. Solutions to the DSR problem identify the best set or sequence o…

eess.SY2026

Multi-Region Optimal Energy Storage Arbitrage

Md Umar Hashmi, Harsha Nagarajan, Dirk Van Hertem1

The increasing interconnection of power systems through AC and DC links enables energy storage units to access multiple electricity markets yet most existing arbitrage models remai…

math.OC2026

A Hybrid Decomposition Approach for Stochastic Unit Commitment with Combined-Cycle Generators

Rosemary Barrass, Harsha Nagarajan, Mathieu Tanneau +2

The U.S. power grid is undergoing a major paradigm shift with the increased development of renewable generators, electric vehicles, and data centers. In response to this growing ne…

eess.SY2025

Sparse Neural Approximations for Bilevel Adversarial Problems in Power Grids

Young-ho Cho, Harsha Nagarajan, Deepjyoti Deka +1

The adversarial worst-case load shedding (AWLS) problem is pivotal for identifying critical contingencies under line outages. It is naturally cast as a bilevel program: the upper l…