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Parameterized Algorithms for the Matrix Completion Problem

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00106817" target="_blank" >RIV/00216224:14330/18:00106817 - isvavai.cz</a>

  • Result on the web

    <a href="http://proceedings.mlr.press/v80/ganian18a.html" target="_blank" >http://proceedings.mlr.press/v80/ganian18a.html</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Parameterized Algorithms for the Matrix Completion Problem

  • Original language description

    We consider two matrix completion problems, in which we are given a matrix with missing entries and the task is to complete the matrix in a way that (1) minimizes the rank, or (2) minimizes the number of distinct rows. We study the parameterized complexity of the two aforementioned problems with respect to several parameters of interest, including the minimum number of matrix rows, columns, and rows plus columns needed to cover all missing entries. We obtain new algorithmic results showing that, for the bounded domain case, both problems are fixed-parameter tractable with respect to all aforementioned parameters. We complement these results with a lower-bound result for the unbounded domain case that rules out fixed-parameter tractability w.r.t. some of the parameters under consideration.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 35th International Conference on Machine Learning (ICML)

  • ISBN

    9781510867963

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    1642-1651

  • Publisher name

    PMLR

  • Place of publication

    USA

  • Event location

    Svedsko

  • Event date

    Jul 10, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000683379201078