From the 1 of 6 linked papers with an AI index.
6 papers
FunL2O: LLM-Guided Feature Function Design for Learning to Optimize
Bingheng Li, Junyang Cai, Yupeng Zhang +3
The paper presents FunL2O, a framework that uses large language models to automatically generate feature functions for learning-to-optimize systems, showing improved performance ov…
Quantum Variational Approaches to the Maximum Independent Set Problem at Utility Scale
Kalyan Dasgupta, Sumanta Mukherjee, Dhriti Verma +4
We study variational quantum algorithms for the Maximum Independent Set (MIS) problem on benchmark graphs of 64, 99, and 180 vertices. The Variational Quantum Eigensolver (VQE) and…
Hamiltonian-Guided Leverage Embedding: Robust Subspace Compression for Efficient QAOA Parameter Estimation
Sumanta Mukherjee, Kalyan Dasgupta, Surya Shravan Kumar Sajja +5
The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical framework for combinatorial optimization on near-term quantum devices. A central bottleneck is t…
Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization
Lam M. Nguyen, Dzung T. Phan, Jayant Kalagnanam
Shuffling strategies for stochastic gradient descent (SGD), including incremental gradient, shuffle-once, and random reshuffling, are supported by rigorous convergence analyses for…
Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation
Vinicius Lima, Dzung T. Phan, Jayant Kalagnanam +2
We present a framework for training trustworthy large language model (LLM) agents for optimization modeling via a verifiable synthetic data generation pipeline. Focusing on linear…
Cardinality-Regularized Hawkes-Granger Model
Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan +1
We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing o…