Submodular Secretary Problem with Shortlists

Authors Shipra Agrawal, Mohammad Shadravan, Cliff Stein

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Author Details

Shipra Agrawal
  • Columbia University, New York, NY, USA, 10027
Mohammad Shadravan
  • Columbia University, New York, NY, USA, 10027
Cliff Stein
  • Columbia University, New York, NY, USA, 10027

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Shipra Agrawal, Mohammad Shadravan, and Cliff Stein. Submodular Secretary Problem with Shortlists. In 10th Innovations in Theoretical Computer Science Conference (ITCS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 124, pp. 1:1-1:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


In submodular k-secretary problem, the goal is to select k items in a randomly ordered input so as to maximize the expected value of a given monotone submodular function on the set of selected items. In this paper, we introduce a relaxation of this problem, which we refer to as submodular k-secretary problem with shortlists. In the proposed problem setting, the algorithm is allowed to choose more than k items as part of a shortlist. Then, after seeing the entire input, the algorithm can choose a subset of size k from the bigger set of items in the shortlist. We are interested in understanding to what extent this relaxation can improve the achievable competitive ratio for the submodular k-secretary problem. In particular, using an O(k) sized shortlist, can an online algorithm achieve a competitive ratio close to the best achievable offline approximation factor for this problem? We answer this question affirmatively by giving a polynomial time algorithm that achieves a 1-1/e-epsilon-O(k^{-1}) competitive ratio for any constant epsilon>0, using a shortlist of size eta_epsilon(k)=O(k). This is especially surprising considering that the best known competitive ratio (in polynomial time) for the submodular k-secretary problem is (1/e-O(k^{-1/2}))(1-1/e) [Thomas Kesselheim and Andreas Tönnis, 2017]. The proposed algorithm also has significant implications for another important problem of submodular function maximization under random order streaming model and k-cardinality constraint. We show that our algorithm can be implemented in the streaming setting using a memory buffer of size eta_epsilon(k)=O(k) to achieve a 1-1/e-epsilon-O(k^{-1}) approximation. This result substantially improves upon [Norouzi-Fard et al., 2018], which achieved the previously best known approximation factor of 1/2 + 8 x 10^{-14} using O(k log k) memory; and closely matches the known upper bound for this problem [McGregor and Vu, 2017].

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Submodular optimization and polymatroids
  • Theory of computation → Online algorithms
  • Theory of computation → Streaming, sublinear and near linear time algorithms
  • Submodular Optimization
  • Secretary Problem
  • Streaming Algorithms


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