TL;DR本文提出了单遍流式和在线算法的受约束 k - 次模最大化,其中包含基数和背包约束限制,该算法可以提供不错的近似解和高效的解决方案,并在广告分配等应用实例上得到了验证。
Abstract
Constrained $k$-submodular maximization is a general framework that captures
many discrete optimization problems such as ad allocation, influence
maximization, personalized recommendation, and many others. In many of these
applications, datasets are large or decisions need to be made i