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Joint work with Arnoud v. den Boer   Abstract: This paper contributes to the ongoing debate on the plausibility of tacit collusion between sellers in algorithmic marketplaces, which can be detrimental to customers and social welfare. We study a broad class of assortment decisions routinely made by sellers on online platforms, including which set of products is offered to customers, at what price, and how are they displayed. In this context, algorithmic decision-support tools are extensively studied in the operations literature and widely…

Abstract: First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved. These methods face two persistent challenges: manual hyperparameter tuning and convergence time to high-accuracy solutions. To address these, we explore how Reinforcement Learning (RL) can learn a policy to tune parameters to accelerate convergence. In experiments with well-known QP benchmarks we find that our RL policy, RLQP, significantly outperforms state-of-the-art QP solvers by up to 3x.…