EconPapers    
Economics at your fingertips  
 

Streaming Algorithms for Non-Submodular Functions Maximization with d-Knapsack Constraint on the Integer Lattice

Jingjing Tan (), Ruiqi Yang, Yapu Zhang () and Mingyue Zhu ()
Additional contact information
Jingjing Tan: School of Mathematics and Information Science, Weifang University, Weifang 261061, P. R. China
Ruiqi Yang: Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing 100124, P. R. China
Yapu Zhang: Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing 100124, P. R. China
Mingyue Zhu: Beijing Institute for Scientific and Engineering Computing, Beijing University of Technology, Beijing 100124, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2023, vol. 40, issue 05, 1-16

Abstract: We study the problem of maximizing a monotone non-submodular function under a d-knapsack constraint on the integer lattice. We propose three streaming algorithms to approach this problem. We first design a two-pass min{α(1 − 𠜀)/2α+1d, 1 − 1/α w2α − 𠜀}-approximate algorithm with total memory complexity O(log dβ−1/β𠜀), and total query complexity for each element O(log ∥ B ∥∞log dβ−1/𠜀). The algorithm relies on a binary search technique to determine the amount of the current elements to be added into the output solution. It also requires to have a good estimate of the optimal value, we use the maximum value of the unit standard vector which can be obtained by reading a round of data to construct a guess set of the optimal value. Then, we modify our algorithm to avoid a repetitive reading of data by dynamically update the maximum value of the unit vector along with the coming elements, and obtain a one-pass streaming algorithm with same approximate ratio. Moreover, we design an improved StreamingKnapsack algorithm to reduce the memory complexity to O(d/𠜀2).

Keywords: Streaming algorithm; non-submodular function; integer lattice; knapsack constraint (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0217595923400183
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:apjorx:v:40:y:2023:i:05:n:s0217595923400183

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0217595923400183

Access Statistics for this article

Asia-Pacific Journal of Operational Research (APJOR) is currently edited by Gongyun Zhao

More articles in Asia-Pacific Journal of Operational Research (APJOR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().

 
Page updated 2025-03-20
Handle: RePEc:wsi:apjorx:v:40:y:2023:i:05:n:s0217595923400183