7 Search Results for "Molinaro, Marco"

Document
DOI: 10.4230/LIPIcs.ESA.2024.89
Locally Computing Edge Orientations

Authors: Slobodan Mitrović, Ronitt Rubinfeld, and Mihir Singhal

Published in: LIPIcs, Volume 308, 32nd Annual European Symposium on Algorithms (ESA 2024)

Abstract
We consider the question of orienting the edges in a graph G such that every vertex has bounded out-degree. For graphs of arboricity α, there is an orientation in which every vertex has out-degree at most α and, moreover, the best possible maximum out-degree of an orientation is at least α - 1. We are thus interested in algorithms that can achieve a maximum out-degree of close to α. A widely studied approach for this problem in the distributed algorithms setting is a "peeling algorithm" that provides an orientation with maximum out-degree α(2+ε) in a logarithmic number of iterations. We consider this problem in the local computation algorithm (LCA) model, which quickly answers queries of the form "What is the orientation of edge (u,v)?" by probing the input graph. When the peeling algorithm is executed in the LCA setting by applying standard techniques, e.g., the Parnas-Ron paradigm, it requires Ω(n) probes per query on an n-vertex graph. In the case where G has unbounded degree, we show that any LCA that orients its edges to yield maximum out-degree r must use Ω(√ n/r) probes to G per query in the worst case, even if G is known to be a forest (that is, α = 1). We also show several algorithms with sublinear probe complexity when G has unbounded degree. When G is a tree such that the maximum degree Δ of G is bounded, we demonstrate an algorithm that uses Δ n^{1-log_Δ r + o(1)} probes to G per query. To obtain this result, we develop an edge-coloring approach that ultimately yields a graph-shattering-like result. We also use this shattering-like approach to demonstrate an LCA which 4-colors any tree using sublinear probes per query.
Cite as

Slobodan Mitrović, Ronitt Rubinfeld, and Mihir Singhal. Locally Computing Edge Orientations. In 32nd Annual European Symposium on Algorithms (ESA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 308, pp. 89:1-89:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{mitrovic_et_al:LIPIcs.ESA.2024.89,
  author =	{Mitrovi\'{c}, Slobodan and Rubinfeld, Ronitt and Singhal, Mihir},
  title =	{{Locally Computing Edge Orientations}},
  booktitle =	{32nd Annual European Symposium on Algorithms (ESA 2024)},
  pages =	{89:1--89:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-338-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{308},
  editor =	{Chan, Timothy and Fischer, Johannes and Iacono, John and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2024.89},
  URN =		{urn:nbn:de:0030-drops-211603},
  doi =		{10.4230/LIPIcs.ESA.2024.89},
  annote =	{Keywords: local computation algorithms, edge orientation, tree coloring}
}
Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.13
The Average-Value Allocation Problem

Authors: Kshipra Bhawalkar, Zhe Feng, Anupam Gupta, Aranyak Mehta, David Wajc, and Di Wang

Published in: LIPIcs, Volume 317, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)

Abstract
We initiate the study of centralized algorithms for welfare-maximizing allocation of goods to buyers subject to average-value constraints. We show that this problem is NP-hard to approximate beyond a factor of e/(e-1), and provide a 4e/(e-1)-approximate offline algorithm. For the online setting, we show that no non-trivial approximations are achievable under adversarial arrivals. Under i.i.d. arrivals, we present a polytime online algorithm that provides a constant approximation of the optimal (computationally-unbounded) online algorithm. In contrast, we show that no constant approximation of the ex-post optimum is achievable by an online algorithm.
Cite as

Kshipra Bhawalkar, Zhe Feng, Anupam Gupta, Aranyak Mehta, David Wajc, and Di Wang. The Average-Value Allocation Problem. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 13:1-13:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{bhawalkar_et_al:LIPIcs.APPROX/RANDOM.2024.13,
  author =	{Bhawalkar, Kshipra and Feng, Zhe and Gupta, Anupam and Mehta, Aranyak and Wajc, David and Wang, Di},
  title =	{{The Average-Value Allocation Problem}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{13:1--13:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-348-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{317},
  editor =	{Kumar, Amit and Ron-Zewi, Noga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2024.13},
  URN =		{urn:nbn:de:0030-drops-210062},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.13},
  annote =	{Keywords: Resource allocation, return-on-spend constraint, approximation algorithm, online algorithm}
}
Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.29
Approximation Algorithms for Correlated Knapsack Orienteering

Authors: David Alemán Espinosa and Chaitanya Swamy

Published in: LIPIcs, Volume 317, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)

Abstract
We consider the correlated knapsack orienteering (CorrKO) problem: we are given a travel budget B, processing-time budget W, finite metric space (V,d) with root ρ ∈ V, where each vertex is associated with a job with possibly correlated random size and random reward that become known only when the job completes. Random variables are independent across different vertices. The goal is to compute a ρ-rooted path of length at most B, in a possibly adaptive fashion, that maximizes the reward collected from jobs that processed by time W. To our knowledge, CorrKO has not been considered before, though prior work has considered the uncorrelated problem, stochastic knapsack orienteering, and correlated orienteering, which features only one budget constraint on the sum of travel-time and processing-times. Gupta et al. [Gupta et al., 2015] showed that the uncorrelated version of this problem has a constant-factor adaptivity gap. We show that, perhaps surprisingly and in stark contrast to the uncorrelated problem, the adaptivity gap of CorrKO is is at least Ω(max{√log(B),√(log log(W))}). Complementing this result, we devise non-adaptive algorithms that obtain: (a) O(log log W)-approximation in quasi-polytime; and (b) O(log W)-approximation in polytime. This also establishes that the adaptivity gap for CorrKO is at most O(log log W). We obtain similar guarantees for CorrKO with cancellations, wherein a job can be cancelled before its completion time, foregoing its reward. We show that an α-approximation for CorrKO implies an O(α)-approximation for CorrKO with cancellations. We also consider the special case of CorrKO where job sizes are weighted Bernoulli distributions, and more generally where the distributions are supported on at most two points (2CorrKO). Although weighted Bernoulli distributions suffice to yield an Ω(√{log log B}) adaptivity-gap lower bound for (uncorrelated) stochastic orienteering, we show that they are easy instances for CorrKO. We develop non-adaptive algorithms that achieve O(1)-approximation, in polytime for weighted Bernoulli distributions, and in (n+log B)^O(log W)-time for 2CorrKO. (Thus, our adaptivity-gap lower-bound example, which uses distributions of support-size 3, is tight in terms of support-size of the distributions.) Finally, we leverage our techniques to provide a quasi-polynomial time O(log log B) approximation algorithm for correlated orienteering improving upon the approximation guarantee in [Bansal and Nagarajan, 2015].
Cite as

David Alemán Espinosa and Chaitanya Swamy. Approximation Algorithms for Correlated Knapsack Orienteering. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 29:1-29:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{alemanespinosa_et_al:LIPIcs.APPROX/RANDOM.2024.29,
  author =	{Alem\'{a}n Espinosa, David and Swamy, Chaitanya},
  title =	{{Approximation Algorithms for Correlated Knapsack Orienteering}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{29:1--29:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-348-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{317},
  editor =	{Kumar, Amit and Ron-Zewi, Noga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2024.29},
  URN =		{urn:nbn:de:0030-drops-210224},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.29},
  annote =	{Keywords: Approximation algorithms, Stochastic orienteering, Adaptivity gap, Vehicle routing problems, LP rounding algorithms}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2023.92
Online Demand Scheduling with Failovers

Authors: Konstantina Mellou, Marco Molinaro, and Rudy Zhou

Published in: LIPIcs, Volume 261, 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)

Abstract
Motivated by cloud computing applications, we study the problem of how to optimally deploy new hardware subject to both power and robustness constraints. To model the situation observed in large-scale data centers, we introduce the Online Demand Scheduling with Failover problem. There are m identical devices with capacity constraints. Demands come one-by-one and, to be robust against a device failure, need to be assigned to a pair of devices. When a device fails (in a failover scenario), each demand assigned to it is rerouted to its paired device (which may now run at increased capacity). The goal is to assign demands to the devices to maximize the total utilization subject to both the normal capacity constraints as well as these novel failover constraints. These latter constraints introduce new decision tradeoffs not present in classic assignment problems such as the Multiple Knapsack problem and AdWords. In the worst-case model, we design a deterministic ≈ 1/2-competitive algorithm, and show this is essentially tight. To circumvent this constant-factor loss, which represents substantial capital losses for big cloud providers, we consider the stochastic arrival model, where all demands come i.i.d. from an unknown distribution. In this model we design an algorithm that achieves sub-linear additive regret (i.e. as OPT or m increases, the multiplicative competitive ratio goes to 1). This requires a combination of different techniques, including a configuration LP with a non-trivial post-processing step and an online monotone matching procedure introduced by Rhee and Talagrand.
Cite as

Konstantina Mellou, Marco Molinaro, and Rudy Zhou. Online Demand Scheduling with Failovers. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 92:1-92:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{mellou_et_al:LIPIcs.ICALP.2023.92,
  author =	{Mellou, Konstantina and Molinaro, Marco and Zhou, Rudy},
  title =	{{Online Demand Scheduling with Failovers}},
  booktitle =	{50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)},
  pages =	{92:1--92:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-278-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{261},
  editor =	{Etessami, Kousha and Feige, Uriel and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.92},
  URN =		{urn:nbn:de:0030-drops-181443},
  doi =		{10.4230/LIPIcs.ICALP.2023.92},
  annote =	{Keywords: online algorithms, approximation algorithms, resource allocation}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2020.72
Knapsack Secretary with Bursty Adversary

Authors: Thomas Kesselheim and Marco Molinaro

Published in: LIPIcs, Volume 168, 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)

Abstract
The random-order or secretary model is one of the most popular beyond-worst case model for online algorithms. While this model avoids the pessimism of the traditional adversarial model, in practice we cannot expect the input to be presented in perfectly random order. This has motivated research on best of both worlds (algorithms with good performance on both purely stochastic and purely adversarial inputs), or even better, on inputs that are a mix of both stochastic and adversarial parts. Unfortunately the latter seems much harder to achieve and very few results of this type are known. Towards advancing our understanding of designing such robust algorithms, we propose a random-order model with bursts of adversarial time steps. The assumption of burstiness of unexpected patterns is reasonable in many contexts, since changes (e.g. spike in a demand for a good) are often triggered by a common external event. We then consider the Knapsack Secretary problem in this model: there is a knapsack of size k (e.g., available quantity of a good), and in each of the n time steps an item comes with its value and size in [0,1] and the algorithm needs to make an irrevocable decision whether to accept or reject the item. We design an algorithm that gives an approximation of 1 - Õ(Γ/k) when the adversarial time steps can be covered by Γ ≥ √k intervals of size Õ(n/k). In particular, setting Γ = √k gives a (1 - O((ln² k)/√k))-approximation that is resistant to up to a (ln k)/√k-fraction of the items being adversarial, which is almost optimal even in the absence of adversarial items. Also, setting Γ = Ω̃(k) gives a constant approximation that is resistant to up to a constant fraction of items being adversarial. While the algorithm is a simple "primal" one it does not possess the crucial symmetry properties exploited in the traditional analyses. The strategy of our analysis is more robust and significantly different from previous ones, and we hope it can be useful for other beyond-worst-case models.
Cite as

Thomas Kesselheim and Marco Molinaro. Knapsack Secretary with Bursty Adversary. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 72:1-72:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{kesselheim_et_al:LIPIcs.ICALP.2020.72,
  author =	{Kesselheim, Thomas and Molinaro, Marco},
  title =	{{Knapsack Secretary with Bursty Adversary}},
  booktitle =	{47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)},
  pages =	{72:1--72:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-138-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{168},
  editor =	{Czumaj, Artur and Dawar, Anuj and Merelli, Emanuela},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2020.72},
  URN =		{urn:nbn:de:0030-drops-124798},
  doi =		{10.4230/LIPIcs.ICALP.2020.72},
  annote =	{Keywords: Beyond worst-case, secretary problem, random order, online algorithms, knapsack}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2020.32
Robust Algorithms for the Secretary Problem

Authors: Domagoj Bradac, Anupam Gupta, Sahil Singla, and Goran Zuzic

Published in: LIPIcs, Volume 151, 11th Innovations in Theoretical Computer Science Conference (ITCS 2020)

Abstract
In classical secretary problems, a sequence of n elements arrive in a uniformly random order, and we want to choose a single item, or a set of size K. The random order model allows us to escape from the strong lower bounds for the adversarial order setting, and excellent algorithms are known in this setting. However, one worrying aspect of these results is that the algorithms overfit to the model: they are not very robust. Indeed, if a few "outlier" arrivals are adversarially placed in the arrival sequence, the algorithms perform poorly. E.g., Dynkin’s popular 1/e-secretary algorithm is sensitive to even a single adversarial arrival: if the adversary gives one large bid at the beginning of the stream, the algorithm does not select any element at all. We investigate a robust version of the secretary problem. In the Byzantine Secretary model, we have two kinds of elements: green (good) and red (rogue). The values of all elements are chosen by the adversary. The green elements arrive at times uniformly randomly drawn from [0,1]. The red elements, however, arrive at adversarially chosen times. Naturally, the algorithm does not see these colors: how well can it solve secretary problems? We show that selecting the highest value red set, or the single largest green element is not possible with even a small fraction of red items. However, on the positive side, we show that these are the only bad cases, by giving algorithms which get value comparable to the value of the optimal green set minus the largest green item. (This benchmark reminds us of regret minimization and digital auctions, where we subtract an additive term depending on the "scale" of the problem.) Specifically, we give an algorithm to pick K elements, which gets within (1-ε) factor of the above benchmark, as long as K ≥ poly(ε^{-1} log n). We extend this to the knapsack secretary problem, for large knapsack size K. For the single-item case, an analogous benchmark is the value of the second-largest green item. For value-maximization, we give a poly log^* n-competitive algorithm, using a multi-layered bucketing scheme that adaptively refines our estimates of second-max over time. For probability-maximization, we show the existence of a good randomized algorithm, using the minimax principle. We hope that this work will spur further research on robust algorithms for the secretary problem, and for other problems in sequential decision-making, where the existing algorithms are not robust and often tend to overfit to the model.
Cite as

Domagoj Bradac, Anupam Gupta, Sahil Singla, and Goran Zuzic. Robust Algorithms for the Secretary Problem. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 32:1-32:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{bradac_et_al:LIPIcs.ITCS.2020.32,
  author =	{Bradac, Domagoj and Gupta, Anupam and Singla, Sahil and Zuzic, Goran},
  title =	{{Robust Algorithms for the Secretary Problem}},
  booktitle =	{11th Innovations in Theoretical Computer Science Conference (ITCS 2020)},
  pages =	{32:1--32:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-134-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{151},
  editor =	{Vidick, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2020.32},
  URN =		{urn:nbn:de:0030-drops-117171},
  doi =		{10.4230/LIPIcs.ITCS.2020.32},
  annote =	{Keywords: stochastic optimization, robust optimization, secretary problem, matroid secretary, robust secretary}
}
Document
DOI: 10.4230/LIPIcs.ICALP.2018.71
Maximizing Profit with Convex Costs in the Random-order Model

Authors: Anupam Gupta, Ruta Mehta, and Marco Molinaro

Published in: LIPIcs, Volume 107, 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)

Abstract
Suppose a set of requests arrives online: each request gives some value v_i if accepted, but requires using some amount of each of d resources. Our cost is a convex function of the vector of total utilization of these d resources. Which requests should be accept to maximize our profit, i.e., the sum of values of the accepted demands, minus the convex cost? We consider this problem in the random-order a.k.a. secretary model, and show an O(d)-competitive algorithm for the case where the convex cost function is also supermodular. If the set of accepted demands must also be independent in a given matroid, we give an O(d^3 alpha)-competitive algorithm for the supermodular case, and an improved O(d^2 alpha) if the convex cost function is also separable. Here alpha is the competitive ratio of the best algorithm for the submodular secretary problem. These extend and improve previous results known for this problem. Our techniques are simple but use powerful ideas from convex duality, which give clean interpretations of existing work, and allow us to give the extensions and improvements.
Cite as

Anupam Gupta, Ruta Mehta, and Marco Molinaro. Maximizing Profit with Convex Costs in the Random-order Model. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 71:1-71:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{gupta_et_al:LIPIcs.ICALP.2018.71,
  author =	{Gupta, Anupam and Mehta, Ruta and Molinaro, Marco},
  title =	{{Maximizing Profit with Convex Costs in the Random-order Model}},
  booktitle =	{45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)},
  pages =	{71:1--71:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-076-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{107},
  editor =	{Chatzigiannakis, Ioannis and Kaklamanis, Christos and Marx, D\'{a}niel and Sannella, Donald},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2018.71},
  URN =		{urn:nbn:de:0030-drops-90751},
  doi =		{10.4230/LIPIcs.ICALP.2018.71},
  annote =	{Keywords: Online algorithms, secretary problem, random order, convex duality}
}
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