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        <identifier>oai:drops-oai.dagstuhl.de:16951</identifier>
        <datestamp>2024-03-06T10:58:48Z</datestamp>
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          <dc:title>Online Metric Allocation and Time-Varying Regularization</dc:title>
          <dc:creator>Bansal, Nikhil</dc:creator>
          <dc:creator>Coester, Christian</dc:creator>
          <dc:subject>Online algorithms</dc:subject>
          <dc:subject>competitive analysis</dc:subject>
          <dc:subject>k-server</dc:subject>
          <dc:subject>metrical task systems</dc:subject>
          <dc:subject>mirror descent</dc:subject>
          <dc:subject>regularization</dc:subject>
          <dc:description>We introduce a general online allocation problem that connects several of the most fundamental problems in online optimization. Let M be an n-point metric space. Consider a resource that can be allocated in arbitrary fractions to the points of M. At each time t, a convex monotone cost function c_t: [0,1] → ℝ_+ appears at some point r_t ∈ M. In response, an algorithm may change the allocation of the resource, paying movement cost as determined by the metric and service cost c_t(x_{r_t}), where x_{r_t} is the fraction of the resource at r_t at the end of time t. For example, when the cost functions are c_t(x) = α x, this is equivalent to randomized MTS, and when the cost functions are c_t(x) = ∞⋅1_{x &lt; 1/k}, this is equivalent to fractional k-server.&#13;
Because of an inherent scale-freeness property of the problem, existing techniques for MTS and k-server fail to achieve similar guarantees for metric allocation. To handle this, we consider a generalization of the online multiplicative update method where we decouple the rate at which a variable is updated from its value, resulting in interesting new dynamics. We use this to give an O(log n)-competitive algorithm for weighted star metrics. We then show how this corresponds to an extension of the online mirror descent framework to a setting where the regularizer is time-varying. Using this perspective, we further refine the guarantees of our algorithm.&#13;
We also consider the case of non-convex cost functions. Using a simple 𝓁₂²-regularizer, we give tight bounds of Θ(n) on tree metrics, which imply deterministic and randomized competitive ratios of O(n²) and O(nlog n) respectively on arbitrary metrics.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Nikhil Bansal and Christian Coester</dc:contributor>
          <dc:date>2022</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ESA.2022.13</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-169515</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.13</dc:identifier>
          <dc:language>eng</dc:language>
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