# Bid Optimization by Multivariable Control in Display Advertising

arXiv: https://arxiv.org/abs/1905.10928 · HTML: https://mlsanity.com/paper/1905.10928/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 30 of 37)

- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021)
- [ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning](https://mlsanity.com/paper/2206.05240/) (2022)
- [MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration](https://mlsanity.com/paper/2202.04348/) (2022)
- [Offline Reinforcement Learning for Optimizing Production Bidding Policies](https://mlsanity.com/paper/2310.09426/) (2023)
- [Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search](https://mlsanity.com/paper/2106.04075/) (2021)
- [Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning](https://mlsanity.com/paper/2306.07106/) (2023)
- [We Know What You Want: An Advertising Strategy Recommender System for Online Advertising](https://mlsanity.com/paper/2105.14188/) (2021)
- [An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising](https://mlsanity.com/paper/2502.05187/) (2025)
- [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2312.17503/) (2023)
- [Computation Resource Allocation Solution in Recommender Systems](https://mlsanity.com/paper/2103.02259/) (2021)
- [Analysis of Dual-Based PID Controllers through Convolutional Mirror Descent](https://mlsanity.com/paper/2202.06152/) (2022)
- [A Real-World Implementation of Unbiased Lift-based Bidding System](https://mlsanity.com/paper/2202.13868/) (2022)
- [Impression Allocation and Policy Search in Display Advertising](https://mlsanity.com/paper/2203.07073/) (2022)
- [BAT: Benchmark for Auto-bidding Task](https://mlsanity.com/paper/2505.08485/) (2025)
- [Bid Optimization using Maximum Entropy Reinforcement Learning](https://mlsanity.com/paper/2110.05032/) (2021)
- [Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks](https://mlsanity.com/paper/2204.11588/) (2022)
- [Lightweight Auto-bidding based on Traffic Prediction in Live Advertising](https://mlsanity.com/paper/2508.06069/) (2025)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective](https://mlsanity.com/paper/2607.27265/) (2026)
- [A Unified Framework for Campaign Performance Forecasting in Online Display Advertising](https://mlsanity.com/paper/2202.11877/) (2022)
- [Functional Optimization Reinforcement Learning for Real-Time Bidding](https://mlsanity.com/paper/2206.13939/) (2022)
- [Risk-Aware Bid Optimization for Online Display Advertisement](https://mlsanity.com/paper/2210.15837/) (2022)
- [Cost-Control in Display Advertising: Theory vs Practice](https://mlsanity.com/paper/2409.03874/) (2024)
- [HALO: Hindsight-Augmented Learning for Online Auto-Bidding](https://mlsanity.com/paper/2508.03267/) (2025)
- [Smart Fast Finish: Preventing Overdelivery via Daily Budget Pacing at DoorDash](https://mlsanity.com/paper/2509.07929/) (2025)
- [Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies](https://mlsanity.com/paper/2601.02754/) (2026)
- [Statistical Learning from Attribution Sets](https://mlsanity.com/paper/2602.06276/) (2026)

## Abstract (arXiv)

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.