3 papers
cs.LG2026
Learning to Design Convex Relaxations for Nonconvex Optimization
Jinrun Liu, Bai Yan, Tengfei Liu +5
Convex relaxations make nonconvex optimization tractable by replacing hard nonconvex problems with convex surrogates that give valid bounds. Yet their design is still largely manua…
cs.PF2024
Two Criteria for Performance Analysis of Optimization Algorithms
Yunpeng Jing, HaiLin Liu, Qunfeng Liu
Performance analysis is crucial in optimization research, especially when addressing black-box problems through nature-inspired algorithms. Current practices often rely heavily on…
math.OC2024
Absolute Ranking: An Essential Normalization for Benchmarking Optimization Algorithms
Yunpeng Jinng, Qunfeng Liu
Evaluating performance across optimization algorithms on many problems presents a complex challenge due to the diversity of numerical scales involved. Traditional data processing m…