Computing market equilibria is a problem of both theoretical and applied interest. Much research to date focuses on the case of static Fisher markets with full information on buyers' utility functions and item supplies. Motivated by real-world markets, we consider an online setting: individuals have linear, additive utility functions; items arrive sequentially and must be allocated and priced irrevocably.

The action cost differs by row, where the corresponding optimal policy parameterization is highlighted in green.

Many recent machine learning research problems utilize the stochastic loss functions.