Wednesday, September 16, 2026

Time: 16:10-17:00

venue: Mathematics Building Room 527

Speaker: Dr. Yu-Chen Yang (Department of Statistics and Data Science, National Cheng Kung University)

Title: Extending t linear mixed models for longitudinal data with non-ignorable dropout applied to AIDS studies

Abstract:

Quadratic unconstrained binary optimization (QUBO) is a common framework for modeling large-scale combinatorial optimization problems in scientific computing, scheduling, manufacturing, and resource allocation. This talk presents a neural initialization and local search strategy for accelerating QUBO solvers based on digital and GPU annealers. From each QUBO matrix, bit-wise structural features are extracted and used by a neural network to predict variable-wise probabilities. Candidate binary solutions are generated by thresholding and Bernoulli sampling, refined by local search, and used as warm-starts for annealing. Experiments on random QUBO matrices and synthetic subset-sum instances show that the proposed approach reduces solve time while maintaining final objective quality.