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Separable Convex Mixed-Integer Optimization: Improved Algorithms and Lower Bounds

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10493489" target="_blank" >RIV/00216208:11320/24:10493489 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.4230/LIPIcs.ESA.2024.32" target="_blank" >https://doi.org/10.4230/LIPIcs.ESA.2024.32</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.4230/LIPIcs.ESA.2024.32" target="_blank" >10.4230/LIPIcs.ESA.2024.32</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Separable Convex Mixed-Integer Optimization: Improved Algorithms and Lower Bounds

  • Original language description

    We provide several novel algorithms and lower bounds in central settings of mixed-integer (non-)linear optimization, shedding new light on classic results in the field. This includes an improvement on record running time bounds obtained from a slight extension of Lenstra&apos;s 1983 algorithm [Math. Oper. Res.&apos;83] to optimizing under few constraints with small coefficients. This is important for ubiquitous tasks like knapsack-, subset sum- or scheduling problems [Eisenbrand and Weismantel, SODA&apos;18, Jansen and Rohwedder, ITCS&apos;19]. Further, we extend our algorithm to an intermediate linear optimization problem when the matrix has many rows that exhibit 2-stage stochastic structure, which adds to a prominent line of recent results on this and similarly restricted cases [Jansen et al. ICALP&apos;19, Cslovjecsek et al. SODA&apos;21, Brand et al. AAAI&apos;21, Klein, Reuter SODA&apos;22, Cslovjecsek et al. SODA&apos;24]. We also show that the generalization of two fundamental classes of structured constraints from these works (n-fold and 2-stage stochastic programs) to separable-convex mixed-integer optimization are harder than their mixed-integer, linear counterparts. This counters a widespread belief popularized initially by an influential paper of Hochbaum and Shanthikumar, namely that &quot;convex separable optimization is not much harder than linear optimization&quot; [J. ACM&apos;90]. To obtain our algorithms, we employ the mixed Graver basis introduced by Hemmecke [Math. Prog.&apos;03], and our work is the first to give bounds on the norm of its elements. Importantly, we use these bounds differently from how purely-integer Graver bounds are exploited in related approaches, and prove that, surprisingly, this cannot be avoided.

  • 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

    <a href="/en/project/GA22-22997S" target="_blank" >GA22-22997S: Efficient and Realistic Models in Computational Social Choice</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2024

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Leibniz International Proceedings in Informatics, LIPIcs

  • ISBN

    978-3-95977-338-6

  • ISSN

    1868-8969

  • e-ISSN

  • Number of pages

    18

  • Pages from-to

    1-18

  • Publisher name

    Schloss Dagstuhl -- Leibniz-Zentrum für Informatik

  • Place of publication

    Dagstuhl, DE

  • Event location

    London, UK

  • Event date

    Sep 2, 2024

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article