# Profit Maximization for Online Advertising Demand-Side Platforms

arXiv: https://arxiv.org/abs/1706.01614 · HTML: https://mlsanity.com/paper/1706.01614/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2017.

## Built on by (most cited first, 9 of 9)

- [Bidding Agent Design in the LinkedIn Ad Marketplace](https://mlsanity.com/paper/2202.12472/) (2022)
- [Real-time Bidding Strategy in Display Advertising: An Empirical Analysis](https://mlsanity.com/paper/2212.02222/) (2022)
- [Bid Optimization using Maximum Entropy Reinforcement Learning](https://mlsanity.com/paper/2110.05032/) (2021)
- [Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes](https://mlsanity.com/paper/2212.13923/) (2022)
- [Bid2X: Revealing Dynamics of Bidding Environment in Online Advertising from A Foundation Model Lens](https://mlsanity.com/paper/2510.23410/) (2025)
- [An Analysis of Selection Bias Issue for Online Advertising](https://mlsanity.com/paper/2206.03853/) (2022)
- [Functional Optimization Reinforcement Learning for Real-Time Bidding](https://mlsanity.com/paper/2206.13939/) (2022)
- [A Profit-Maximizing Strategy for Advertising on the e-Commerce Platforms](https://mlsanity.com/paper/2211.01160/) (2022)
- [A Lightweight MPC Bidding Framework for Brand Auction Ads](https://mlsanity.com/paper/2603.07721/) (2026)

## Abstract (arXiv)

We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its own profit while acquiring valuable impressions for its advertiser clients. We formulate the problem of profit maximization for a DSP interacting with ad exchanges in a real-time bidding environment in a cost-per-click/cost-per-action pricing model. Our proposed formulation leads to a nonconvex optimization problem due to the joint optimization over both impression allocation and bid price decisions. We use Lagrangian relaxation to develop a tractable convex dual problem, which, due to the properties of second-price auctions, may be solved efficiently with subgradient methods. We propose a two-phase solution procedure, whereby in the first phase we solve the convex dual problem using a subgradient algorithm, and in the second phase we use the previously computed dual solution to set bid prices and then solve a linear optimization problem to obtain the allocation probability variables. On several synthetic examples, we demonstrate that our proposed solution approach leads to superior performance over a baseline method that is used in practice.