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Documents authored by Bercea, Ioana O.

Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.14
Algorithms for the Diverse-k-SAT Problem: The Geometry of Satisfying Assignments

Authors: Per Austrin, Ioana O. Bercea, Mayank Goswami, Nutan Limaye, and Adarsh Srinivasan

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
Given a k-CNF formula and an integer s ≥ 2, we study algorithms that obtain s solutions to the formula that are as dispersed as possible. For s = 2, this problem of computing the diameter of a k-CNF formula was initiated by Creszenzi and Rossi, who showed strong hardness results even for k = 2. The current best upper bound [Angelsmark and Thapper '04] goes to 4ⁿ as k → ∞. As our first result, we show that this quadratic blow up is not necessary by utilizing the Fast-Fourier transform (FFT) to give a O^*(2ⁿ) time exact algorithm for computing the diameter of any k-CNF formula. For s > 2, the problem was raised in the SAT community (Nadel '11) and several heuristics have been proposed for it, but no algorithms with theoretical guarantees are known. We give exact algorithms using FFT and clique-finding that run in O^*(2^{(s-1)n}) and O^*(s² |Ω_{𝐅}|^{ω ⌈ s/3 ⌉}) respectively, where |Ω_{𝐅}| is the size of the solutions space of the formula 𝐅 and ω is the matrix multiplication exponent. However, current SAT algorithms for finding one solution run in time O^*(2^{ε_{k}n}) for ε_{k} ≈ 1-Θ(1/k), which is much faster than all above run times. As our main result, we analyze two popular SAT algorithms - PPZ (Paturi, Pudlák, Zane '97) and Schöning’s ('02) algorithms, and show that in time poly(s)O^*(2^{ε_{k}n}), they can be used to approximate diameter as well as the dispersion (s > 2) problem. While we need to modify Schöning’s original algorithm for technical reasons, we show that the PPZ algorithm, without any modification, samples solutions in a geometric sense. We believe this geometric sampling property of PPZ may be of independent interest. Finally, we focus on diverse solutions to NP-complete optimization problems, and give bi-approximations running in time poly(s)O^*(2^{ε n}) with ε < 1 for several problems such as Maximum Independent Set, Minimum Vertex Cover, Minimum Hitting Set, Feedback Vertex Set, Multicut on Trees and Interval Vertex Deletion. For all of these problems, all existing exact methods for finding optimal diverse solutions have a runtime with at least an exponential dependence on the number of solutions s. Our methods show that by relaxing to bi-approximations, this dependence on s can be made polynomial.
Cite as

Per Austrin, Ioana O. Bercea, Mayank Goswami, Nutan Limaye, and Adarsh Srinivasan. Algorithms for the Diverse-k-SAT Problem: The Geometry of Satisfying Assignments. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 14:1-14:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{austrin_et_al:LIPIcs.ICALP.2025.14,
  author =	{Austrin, Per and Bercea, Ioana O. and Goswami, Mayank and Limaye, Nutan and Srinivasan, Adarsh},
  title =	{{Algorithms for the Diverse-k-SAT Problem: The Geometry of Satisfying Assignments}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{14:1--14:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l 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.2025.14},
  URN =		{urn:nbn:de:0030-drops-233916},
  doi =		{10.4230/LIPIcs.ICALP.2025.14},
  annote =	{Keywords: Exponential time algorithms, Satisfiability, k-SAT, PPZ, Sch\"{o}ning, Dispersion, Diversity}
}
Document
DOI: 10.4230/LIPIcs.ESA.2024.5
Online Sorting and Online TSP: Randomized, Stochastic, and High-Dimensional

Authors: Mikkel Abrahamsen, Ioana O. Bercea, Lorenzo Beretta, Jonas Klausen, and László Kozma

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

Abstract
In the online sorting problem, n items are revealed one by one and have to be placed (immediately and irrevocably) into empty cells of a size-n array. The goal is to minimize the sum of absolute differences between items in consecutive cells. This natural problem was recently introduced by Aamand, Abrahamsen, Beretta, and Kleist (SODA 2023) as a tool in their study of online geometric packing problems. They showed that when the items are reals from the interval [0,1] a competitive ratio of O(√n) is achievable, and no deterministic algorithm can improve this ratio asymptotically. In this paper, we extend and generalize the study of online sorting in three directions: - randomized: we settle the open question of Aamand et al. by showing that the O(√n) competitive ratio for the online sorting of reals cannot be improved even with the use of randomness; - stochastic: we consider inputs consisting of n samples drawn uniformly at random from an interval, and give an algorithm with an improved competitive ratio of Õ(n^{1/4}). The result reveals connections between online sorting and the design of efficient hash tables; - high-dimensional: we show that Õ(√n)-competitive online sorting is possible even for items from ℝ^d, for arbitrary fixed d, in an adversarial model. This can be viewed as an online variant of the classical TSP problem where tasks (cities to visit) are revealed one by one and the salesperson assigns each task (immediately and irrevocably) to its timeslot. Along the way, we also show a tight O(log n)-competitiveness result for uniform metrics, i.e., where items are of different types and the goal is to order them so as to minimize the number of switches between consecutive items of different types.
Cite as

Mikkel Abrahamsen, Ioana O. Bercea, Lorenzo Beretta, Jonas Klausen, and László Kozma. Online Sorting and Online TSP: Randomized, Stochastic, and High-Dimensional. In 32nd Annual European Symposium on Algorithms (ESA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 308, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{abrahamsen_et_al:LIPIcs.ESA.2024.5,
  author =	{Abrahamsen, Mikkel and Bercea, Ioana O. and Beretta, Lorenzo and Klausen, Jonas and Kozma, L\'{a}szl\'{o}},
  title =	{{Online Sorting and Online TSP: Randomized, Stochastic, and High-Dimensional}},
  booktitle =	{32nd Annual European Symposium on Algorithms (ESA 2024)},
  pages =	{5:1--5:15},
  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.2024.5},
  URN =		{urn:nbn:de:0030-drops-210766},
  doi =		{10.4230/LIPIcs.ESA.2024.5},
  annote =	{Keywords: sorting, online algorithm, TSP}
}
Document
DOI: 10.4230/LIPIcs.SWAT.2024.9
Daisy Bloom Filters

Authors: Ioana O. Bercea, Jakob Bæk Tejs Houen, and Rasmus Pagh

Published in: LIPIcs, Volume 294, 19th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2024)

Abstract
A filter is a widely used data structure for storing an approximation of a given set S of elements from some universe 𝒰 (a countable set). It represents a superset S' ⊇ S that is "close to S" in the sense that for x ∉ S, the probability that x ∈ S' is bounded by some ε > 0. The advantage of using a Bloom filter, when some false positives are acceptable, is that the space usage becomes smaller than what is required to store S exactly. Though filters are well-understood from a worst-case perspective, it is clear that state-of-the-art constructions may not be close to optimal for particular distributions of data and queries. Suppose, for instance, that some elements are in S with probability close to 1. Then it would make sense to always include them in S', saving space by not having to represent these elements in the filter. Questions like this have been raised in the context of Weighted Bloom filters (Bruck, Gao and Jiang, ISIT 2006) and Bloom filter implementations that make use of access to learned components (Vaidya, Knorr, Mitzenmacher, and Krask, ICLR 2021). In this paper, we present a lower bound for the expected space that such a filter requires. We also show that the lower bound is asymptotically tight by exhibiting a filter construction that executes queries and insertions in worst-case constant time, and has a false positive rate at most ε with high probability over input sets drawn from a product distribution. We also present a Bloom filter alternative, which we call the Daisy Bloom filter, that executes operations faster and uses significantly less space than the standard Bloom filter.
Cite as

Ioana O. Bercea, Jakob Bæk Tejs Houen, and Rasmus Pagh. Daisy Bloom Filters. In 19th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 294, pp. 9:1-9:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{bercea_et_al:LIPIcs.SWAT.2024.9,
  author =	{Bercea, Ioana O. and Houen, Jakob B{\ae}k Tejs and Pagh, Rasmus},
  title =	{{Daisy Bloom Filters}},
  booktitle =	{19th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2024)},
  pages =	{9:1--9:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-318-8},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{294},
  editor =	{Bodlaender, Hans L.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2024.9},
  URN =		{urn:nbn:de:0030-drops-200491},
  doi =		{10.4230/LIPIcs.SWAT.2024.9},
  annote =	{Keywords: Bloom filters, input distribution, learned data structures}
}
Document
DOI: 10.4230/LIPIcs.SWAT.2020.11
A Dynamic Space-Efficient Filter with Constant Time Operations

Authors: Ioana O. Bercea and Guy Even

Published in: LIPIcs, Volume 162, 17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020)

Abstract
A dynamic dictionary is a data structure that maintains sets of cardinality at most n from a given universe and supports insertions, deletions, and membership queries. A filter approximates membership queries with a one-sided error that occurs with probability at most ε. The goal is to obtain dynamic filters that are space-efficient (the space is 1+o(1) times the information-theoretic lower bound) and support all operations in constant time with high probability. One approach to designing filters is to reduce to the retrieval problem. When the size of the universe is polynomial in n, this approach yields a space-efficient dynamic filter as long as the error parameter ε satisfies log(1/ε) = ω(log log n). For the case that log(1/ε) = O(log log n), we present the first space-efficient dynamic filter with constant time operations in the worst case (whp). In contrast, the space-efficient dynamic filter of Pagh et al. [Anna Pagh et al., 2005] supports insertions and deletions in amortized expected constant time. Our approach employs the classic reduction of Carter et al. [Carter et al., 1978] on a new type of dictionary construction that supports random multisets.
Cite as

Ioana O. Bercea and Guy Even. A Dynamic Space-Efficient Filter with Constant Time Operations. In 17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 162, pp. 11:1-11:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{bercea_et_al:LIPIcs.SWAT.2020.11,
  author =	{Bercea, Ioana O. and Even, Guy},
  title =	{{A Dynamic Space-Efficient Filter with Constant Time Operations}},
  booktitle =	{17th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020)},
  pages =	{11:1--11:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-150-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{162},
  editor =	{Albers, Susanne},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2020.11},
  URN =		{urn:nbn:de:0030-drops-122582},
  doi =		{10.4230/LIPIcs.SWAT.2020.11},
  annote =	{Keywords: Data Structures}
}
Document
APPROX
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2019.18
On the Cost of Essentially Fair Clusterings

Authors: Ioana O. Bercea, Martin Groß, Samir Khuller, Aounon Kumar, Clemens Rösner, Daniel R. Schmidt, and Melanie Schmidt

Published in: LIPIcs, Volume 145, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)

Abstract
Clustering is a fundamental tool in data mining and machine learning. It partitions points into groups (clusters) and may be used to make decisions for each point based on its group. However, this process may harm protected (minority) classes if the clustering algorithm does not adequately represent them in desirable clusters - especially if the data is already biased. At NIPS 2017, Chierichetti et al. [Flavio Chierichetti et al., 2017] proposed a model for fair clustering requiring the representation in each cluster to (approximately) preserve the global fraction of each protected class. Restricting to two protected classes, they developed both a 4-approximation for the fair k-center problem and a O(t)-approximation for the fair k-median problem, where t is a parameter for the fairness model. For multiple protected classes, the best known result is a 14-approximation for fair k-center [Clemens Rösner and Melanie Schmidt, 2018]. We extend and improve the known results. Firstly, we give a 5-approximation for the fair k-center problem with multiple protected classes. Secondly, we propose a relaxed fairness notion under which we can give bicriteria constant-factor approximations for all of the classical clustering objectives k-center, k-supplier, k-median, k-means and facility location. The latter approximations are achieved by a framework that takes an arbitrary existing unfair (integral) solution and a fair (fractional) LP solution and combines them into an essentially fair clustering with a weakly supervised rounding scheme. In this way, a fair clustering can be established belatedly, in a situation where the centers are already fixed.
Cite as

Ioana O. Bercea, Martin Groß, Samir Khuller, Aounon Kumar, Clemens Rösner, Daniel R. Schmidt, and Melanie Schmidt. On the Cost of Essentially Fair Clusterings. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 145, pp. 18:1-18:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{bercea_et_al:LIPIcs.APPROX-RANDOM.2019.18,
  author =	{Bercea, Ioana O. and Gro{\ss}, Martin and Khuller, Samir and Kumar, Aounon and R\"{o}sner, Clemens and Schmidt, Daniel R. and Schmidt, Melanie},
  title =	{{On the Cost of Essentially Fair Clusterings}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)},
  pages =	{18:1--18:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-125-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{145},
  editor =	{Achlioptas, Dimitris and V\'{e}gh, L\'{a}szl\'{o} A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2019.18},
  URN =		{urn:nbn:de:0030-drops-112337},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2019.18},
  annote =	{Keywords: approximation, clustering, fairness, LP rounding}
}
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