Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets
Researchers have developed a novel principle for online inventory optimization (OIO) that achieves optimal performance on general convex sets. This method, which involves maintaining a hidden target and projecting it onto the feasible order-up-to set, improves regret guarantees for OIO and offers the first polylogarithmic regret for strongly convex losses. The analysis introduces a 'norm alignment' principle, reducing the problem to one-dimensional queue control, which has been validated through experiments on both synthetic and real-world inventory data. AI
IMPACT This research advances theoretical understanding in online learning and optimization, potentially impacting future inventory management systems.