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. The optimal bidding mechanism for a large family of multi-constrained advertising problems is derived, and it is demonstrated how the solution can be implemented as three separate subsystems; dealing with impression valuation, campaign control, and bid shading optimization, respectively. 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.