Bid Optimization by Multivariable Control in Display Advertising

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

A Parallelizable Acceleration Framework for Packing Linear Programs

This paper presents an acceleration framework for packing linear programming problems where the amount of data available is limited, i.e., where the number of constraints m is small compared to the variable dimension n. The framework can be used as a black box to speed up linear programming solvers dramatically, by two orders of magnitude in our experiments. We present worst-case guarantees on the quality of the solution and the speedup provided by the algorithm, showing that the framework provides an approximately optimal solution while running the original solver on a much smaller problem. The framework can be used to accelerate exact solvers, approximate solvers, and parallel/distributed solvers. Further, it can be used for both linear programs and integer linear programs

Online Auctions with Dual-Threshold Algorithms: An Experimental Study and Practical Evaluation

Online auctions are a viable alternative to conventional posted price mechanisms. Agrawal, Wang, and Ye [1] have proposed two primal-dual algorithms for revenue-maximizing multi-item allocation tasks. Although promising in terms of theoretical properties and competitive ratios, there is alack of evidence regarding the real-world practicability of these mechanisms, for instance referring to online auction-based tickets sales. In this paper, we conduct an experimental study on both the One-Time Learning Algorithm(OLA) and the Dynamic Learning Algorithm (DLA) based on synthetic data, revealing the remarkable aptitude of the latter for non-trivial online auctions. Being robust to most input variations, the inherent dynamic update of dual thresholds achieves a superior balance with respect to the trade-off between objective function values and runtimes. We address critical sensitivities quantitatively and draft several small extensions by incorporating input distribution knowledge