42 citations · 94 across the 15 of their papers we have counts for
3 papers · 1 filter
FunL2O: LLM-Guided Feature Function Design for Learning to Optimize
Bingheng Li, Junyang Cai, Yupeng Zhang +3
Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance. A…
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…