In this work, we investigate online bicriteria algorithms that consider both coverage and cost in the team formation problem, which selects a set of experts with the objective of maximizing the difference of two set functions $f-\ell$, where function f is non-negative normalized monotone approximately submodular, and function ℓ is non-negative linear. By exploiting the problem's combinatorial structure, we present three bicriteria algorithms along with their corresponding competitive analysis. The first two algorithms handle the cases where function f is γ-weakly submodular, and strictly γ-weakly submodular, respectively. The last algorithm is more general by integrating the first two with extra parameters introduced.

在这项工作中，我们研究了在团队形成问题中同时考虑覆盖率和成本的在线双标准算法，该算法选择了一组专家，目的是最大程度地提高两个集合功能之间的差异 $F-\ell$，其中函数f为近似负模的非负归一化单调，函数ℓ为非负线性。通过利用问题的组合结构，我们提出了三种双标准算法及其相应的竞争分析。前两种算法分别处理函数f为γ-弱次模和严格γ-弱次模的情况。通过将前两个与引入的额外参数集成在一起，最后一种算法更加通用。