2026-07-23T04:16:39Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:5918 2024-03-12T11:59:01Z ddc:004 open_access

Max-Sum Diversity Via Convex Programming Cevallos, Alfonso Eisenbrand, Friedrich Zenklusen, Rico Geometric Dispersion Embeddings Approximation Algorithms Convex Programming Matroids Diversity maximization is an important concept in information retrieval, computational geometry and operations research. Usually, it is a variant of the following problem: Given a ground set, constraints, and a function f that measures diversity of a subset, the task is to select a feasible subset S such that f(S) is maximized. The sum-dispersion function f(S) which is the sum of the pairwise distances in S, is in this context a prominent diversification measure. The corresponding diversity maximization is the "max-sum" or "sum-sum" diversification. Many recent results deal with the design of constant-factor approximation algorithms of diversification problems involving sum-dispersion function under a matroid constraint. In this paper, we present a PTAS for the max-sum diversity problem under a matroid constraint for distances d(.,.) of negative type. Distances of negative type are, for example, metric distances stemming from the l_2 and l_1 norms, as well as the cosine or spherical, or Jaccard distance which are popular similarity metrics in web and image search. Our algorithm is based on techniques developed in geometric algorithms like metric embeddings and convex optimization. We show that one can compute a fractional solution of the usually non-convex relaxation of the problem which yields an upper bound on the optimum integer solution. Starting from this fractional solution, we employ a deterministic rounding approach which only incurs a small loss in terms of objective, thus leading to a PTAS. This technique can be applied to other previously studied variants of the max-sum dispersion function, including combinations of diversity with linear-score maximization, improving over the previous constant-factor approximation algorithms. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Alfonso Cevallos and Friedrich Eisenbrand and Rico Zenklusen 2016 Is Part Of LIPIcs, Volume 51, 32nd International Symposium on Computational Geometry (SoCG 2016) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SoCG.2016.26 urn:nbn:de:0030-drops-59186 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2016.26 eng https://creativecommons.org/licenses/by/3.0/legalcode