Open Access Issue
Multipass Streaming Algorithms for Regularized Submodular Maximization
Tsinghua Science and Technology 2024, 29 (1): 76-85
Published: 21 August 2023

In this work, we study a k-Cardinality Constrained Regularized Submodular Maximization ( k-CCRSM) problem, in which the objective utility is expressed as the difference between a non-negative submodular and a modular function. No multiplicative approximation algorithm exists for the regularized model, and most works have focused on designing weak approximation algorithms for this problem. In this study, we consider the k-CCRSM problem in a streaming fashion, wherein the elements are assumed to be visited individually and cannot be entirely stored in memory. We propose two multipass streaming algorithms with theoretical guarantees for the above problem, wherein submodular terms are monotonic and nonmonotonic.

Open Access Issue
A Note on Maximizing Regularized Submodular Functions Under Streaming
Tsinghua Science and Technology 2023, 28 (6): 1023-1029
Published: 28 July 2023

Recent progress in maximizing submodular functions with a cardinality constraint through centralized and streaming modes has demonstrated a wide range of applications and also developed comprehensive theoretical guarantees. The submodularity was investigated to capture the diversity and representativeness of the utilities, and the monotonicity has the advantage of improving the coverage. Regularized submodular optimization models were developed in the latest studies (such as a house on fire), which aimed to sieve subsets with constraints to optimize regularized utilities. This study is motivated by the setting in which the input stream is partitioned into several disjoint parts, and each part has a limited size constraint. A first threshold-based bicriteria (1/3,2/3)-approximation for the problem is provided.

Open Access Issue
Approximating (mB,mP)-Monotone BP Maximization and Extensions
Tsinghua Science and Technology 2023, 28 (5): 906-915
Published: 19 May 2023

The paper proposes the optimization problem of maximizing the sum of suBmodular and suPermodular (BP) functions with partial monotonicity under a streaming fashion. In this model, elements are randomly released from the stream and the utility is encoded by the sum of partial monotone suBmodular and suPermodular functions. The goal is to determine whether a subset from the stream of size bounded by parameter k subject to the summarized utility is as large as possible. In this work, a threshold-based streaming algorithm is presented for the BP maximization that attains a ((1-κ)/(2-κ)-𝒪(ε))-approximation with 𝒪(1/ε4log3(1/ε)log((2-κ)k/(1-κ)2)) memory complexity, where κ denotes the parameter of supermodularity ratio. We further consider a more general model with fair constraints and present a greedy-based algorithm that obtains the same approximation. We finally investigate this fair model under the streaming fashion and provide a ((1-κ)4/(2-2κ+κ2)2-𝒪(ε))-approximation algorithm.

total 3