Scalable Multi-Objective Optimization in Programmatic Advertising via Feedback Control

The majority of online advertising is served through real-time bidding, and advertising campaigns are often defined as optimization problems. This paper deals with advertiser profit maximization subject to multiple advertiser performance constraints. We derive the optimal bidding mechanism for a large family of multi-constrained advertising problems and demonstrate how the solution can be implemented as three separate  subsystems dealing with impression valuation, campaign control, and bid shading optimization. Feedback control plays a critical role to make this optimization scalable and adaptive. A proof of concept campaign control system is proposed and evaluated in simulations.