Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees

@article{Qian2022ResultDB,
  title={Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees},
  author={Chao Qian and Danqin Liu and Zhi-Hua Zhou},
  journal={ArXiv},
  year={2022},
  volume={abs/2110.09332}
}

Figures and Tables from this paper

References

SHOWING 1-10 OF 54 REFERENCES
Max-Sum Diversification, Monotone Submodular Functions, and Dynamic Updates
TLDR
A greedy algorithm for a cardinality constraint and a local search algorithm for an arbitrary matroid constraint are proposed and it is proved that both algorithms achieve constant approximation ratios.
Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints
TLDR
This paper proposes a new algorithm FPOMC by combining the merits of the generalized greedy algorithm and POMC, and proves thatFPOMC can maintain the best known approximation guarantee efficiently.
A General Coreset-Based Approach to Diversity Maximization under Matroid Constraints
TLDR
The first coreset-based algorithms for diversity maximization under matroid constraints for various diversity functions are devise, together with efficient sequential, MapReduce, and Streaming implementations, and are capable of dealing with the large input instances typical of the big data scenario.
Linear Relaxations for Finding Diverse Elements in Metric Spaces
TLDR
This paper studies an objective known as {\em sum-min} diversity, which is known to be effective in many applications, and gives the first constant factor approximation algorithm, and compares the quality of the solutions produced by the method with other popular diversity maximization algorithms.
Max-Sum Diversity Via Convex Programming
TLDR
This paper presents a PTAS for the max-sum diversification problem under a matroid constraint for distances d(\cdot,\cdot) of negative type, which are popular similarity metrics in web and image search.
Multiobjective Evolutionary Algorithms Are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions
  • Chao Qian
  • Mathematics, Computer Science
    Evolutionary Computation
  • 2021
TLDR
It is proved that by optimizing the original objective function (g-c) and the size simultaneously, the GSEMO fails to achieve a good polynomial-time approximation guarantee, but it is also proven that by optimized a distorted objective function and thesize simultaneous, theGSEMO can still achieve the best-known polynometric- time approximation guarantee.
Interactive multiobjective evolutionary algorithm based on decomposition and compression
TLDR
An interactive multiobjective evolutionary algorithm (MOEA) called iDMOEA-εC is proposed, which utilizes the DM’s preferences to compress the objective space directly and progressively for identifying the DM's preferred region.
Diversity maximization under matroid constraints
TLDR
A combinatorial proof technique for maximizing diversity under matroid constraints uses the existence of a family of Latin squares which may also be of independent interest, and the first constant-factor approximation algorithm for this problem, using a new technique.
Constrained Monotone $k$ -Submodular Function Maximization Using Multiobjective Evolutionary Algorithms With Theoretical Guarantee
TLDR
This paper proposes a new approach which employs a multiobjective evolutionary algorithm to maximize the given objective and minimize the size simultaneously and proves that the proposed method can obtain the asymptotically tight approximation guarantee.
...
1
2
3
4
5
...