10 Search Results for "Chen, Yanlin"

Document
DOI: 10.4230/LIPIcs.ESA.2025.51
Quantum Approximate k-Minimum Finding

Authors: Minbo Gao, Zhengfeng Ji, and Qisheng Wang

Published in: LIPIcs, Volume 351, 33rd Annual European Symposium on Algorithms (ESA 2025)

Abstract
Quantum k-minimum finding is a fundamental subroutine with numerous applications in combinatorial problems and machine learning. Previous approaches typically assume oracle access to exact function values, making it challenging to integrate this subroutine with other quantum algorithms. In this paper, we propose an (almost) optimal quantum k-minimum finding algorithm that works with approximate values for all k ≥ 1, extending a result of van Apeldoorn, Gilyén, Gribling, and de Wolf (FOCS 2017) for k = 1. As practical applications, we present efficient quantum algorithms for identifying the k smallest expectation values among multiple observables and for determining the k lowest ground state energies of a Hamiltonian with a known eigenbasis.
Cite as

Minbo Gao, Zhengfeng Ji, and Qisheng Wang. Quantum Approximate k-Minimum Finding. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 51:1-51:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{gao_et_al:LIPIcs.ESA.2025.51,
  author =	{Gao, Minbo and Ji, Zhengfeng and Wang, Qisheng},
  title =	{{Quantum Approximate k-Minimum Finding}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{51:1--51:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-395-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{351},
  editor =	{Benoit, Anne and Kaplan, Haim and Wild, Sebastian 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.2025.51},
  URN =		{urn:nbn:de:0030-drops-245192},
  doi =		{10.4230/LIPIcs.ESA.2025.51},
  annote =	{Keywords: Quantum Computing, Quantum Algorithms, Quantum Minimum Finding}
}
Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2025.6
QSETH Strikes Again: Finer Quantum Lower Bounds for Lattice Problem, Strong Simulation, Hitting Set Problem, and More

Authors: Yanlin Chen, Yilei Chen, Rajendra Kumar, Subhasree Patro, and Florian Speelman

Published in: LIPIcs, Volume 353, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025)

Abstract
Despite the wide range of problems for which quantum computers offer a computational advantage over their classical counterparts, there are also many problems for which the best known quantum algorithm provides a speedup that is only quadratic, or even subquadratic. Such a situation could also be desirable if we don't want quantum computers to solve certain problems fast - say problems relevant to post-quantum cryptography. When searching for algorithms and when analyzing the security of cryptographic schemes, we would like to have evidence that these problems are difficult to solve on quantum computers; but how do we assess the exact complexity of these problems? For most problems, there are no known ways to directly prove time lower bounds, however it can still be possible to relate the hardness of disparate problems to show conditional lower bounds. This approach has been popular in the classical community, and is being actively developed for the quantum case [Aaronson et al., 2020; Buhrman et al., 2021; Harry Buhrman et al., 2022; Andris Ambainis et al., 2022]. In this paper, by the use of the QSETH framework [Buhrman et al., 2021] we are able to understand the quantum complexity of a few natural variants of CNFSAT, such as parity-CNFSAT or counting-CNFSAT, and also are able to comment on the non-trivial complexity of approximate versions of counting-CNFSAT. Without considering such variants, the best quantum lower bounds will always be quadratically lower than the equivalent classical bounds, because of Grover’s algorithm; however, we are able to show that quantum algorithms will likely not attain even a quadratic speedup for many problems. These results have implications for the complexity of (variations of) lattice problems, the strong simulation and hitting set problems, and more. In the process, we explore the QSETH framework in greater detail and present a useful guide on how to effectively use the QSETH framework.
Cite as

Yanlin Chen, Yilei Chen, Rajendra Kumar, Subhasree Patro, and Florian Speelman. QSETH Strikes Again: Finer Quantum Lower Bounds for Lattice Problem, Strong Simulation, Hitting Set Problem, and More. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 353, pp. 6:1-6:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{chen_et_al:LIPIcs.APPROX/RANDOM.2025.6,
  author =	{Chen, Yanlin and Chen, Yilei and Kumar, Rajendra and Patro, Subhasree and Speelman, Florian},
  title =	{{QSETH Strikes Again: Finer Quantum Lower Bounds for Lattice Problem, Strong Simulation, Hitting Set Problem, and More}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025)},
  pages =	{6:1--6:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-397-3},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{353},
  editor =	{Ene, Alina and Chattopadhyay, Eshan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2025.6},
  URN =		{urn:nbn:de:0030-drops-243723},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2025.6},
  annote =	{Keywords: Quantum conditional lower bounds, Fine-grained complexity, Lattice problems, Quantum strong simulation, Hitting set problem, QSETH}
}
Document
DOI: 10.4230/LIPIcs.SEA.2025.5
A Comparative Study of Compressed, Learned, and Traditional Indexing Methods for Integer Data

Authors: Lorenzo Bellomo, Giuseppe Cianci, Luca de Rosa, Paolo Ferragina, and Mattia Odorisio

Published in: LIPIcs, Volume 338, 23rd International Symposium on Experimental Algorithms (SEA 2025)

Abstract
The rapid evolution of learned data structures has revolutionized database indexing, particularly for sorted integer datasets. While learned indexes excel in static scenarios due to their low memory footprint, reduced storage requirements, and fast lookup times, benchmarks like SOSD and TLI have largely overlooked compressed indexes and SIMD-based implementations of traditional indexes. This paper addresses this gap by introducing a comprehensive benchmarking framework that (i) evaluates traditional, learned, and compressed indexes across 12 datasets (real and synthetic) of varying types and sizes; (ii) integrates state-of-the-art SIMD-enhanced B-Tree variants; and (iii) measures critical performance metrics such as memory usage, construction time, and lookup efficiency. Our findings reveal that while learned indexes minimize memory usage, a feature useful when internal memory constraints are mandatory, SIMD-enhanced B-Trees consistently achieve superior lookup times with comparable extra space. On the other hand, compressed indexes like LA-vector and EliasFano provide very effective compression of the indexed data with slower access speeds (2x-3x). Another contribution of this paper is a publicly available benchmarking framework (composed of code and datasets) that makes our experiments reproducible and extensible to other indexes and datasets.
Cite as

Lorenzo Bellomo, Giuseppe Cianci, Luca de Rosa, Paolo Ferragina, and Mattia Odorisio. A Comparative Study of Compressed, Learned, and Traditional Indexing Methods for Integer Data. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 5:1-5:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bellomo_et_al:LIPIcs.SEA.2025.5,
  author =	{Bellomo, Lorenzo and Cianci, Giuseppe and de Rosa, Luca and Ferragina, Paolo and Odorisio, Mattia},
  title =	{{A Comparative Study of Compressed, Learned, and Traditional Indexing Methods for Integer Data}},
  booktitle =	{23rd International Symposium on Experimental Algorithms (SEA 2025)},
  pages =	{5:1--5:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-375-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{338},
  editor =	{Mutzel, Petra and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2025.5},
  URN =		{urn:nbn:de:0030-drops-232439},
  doi =		{10.4230/LIPIcs.SEA.2025.5},
  annote =	{Keywords: indexing data structures, compression, algorithm engineering, benchmark}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.39
Sampling with a Black Box: Faster Parameterized Approximation Algorithms for Vertex Deletion Problems

Authors: Barış Can Esmer and Ariel Kulik

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

Abstract
In this paper, we present Sampling with a Black Box, a unified framework for the design of parameterized approximation algorithms for vertex deletion problems (e.g., Vertex Cover, Feedback Vertex Set, etc.). The framework relies on two components: - A Sampling Step. A polynomial-time randomized algorithm that, given a graph G, returns a random vertex v such that the optimum of G⧵ {v} is smaller by 1 than the optimum of G, with some prescribed probability q. We show that such algorithms exist for multiple vertex deletion problems. - A Black Box algorithm which is either an exact parameterized algorithm, a polynomial-time approximation algorithm, or a parameterized-approximation algorithm. The framework combines these two components together. The sampling step is applied iteratively to remove vertices from the input graph, and then the solution is extended using the black box algorithm. The process is repeated sufficiently many times so that the target approximation ratio is attained with a constant probability. We use the technique to derive parameterized approximation algorithms for several vertex deletion problems, including Feedback Vertex Set, d-Hitting Set and 𝓁-Path Vertex Cover. In particular, for every approximation ratio 1 < β < 2, we attain a parameterized β-approximation for Feedback Vertex Set, which is faster than the parameterized β-approximation of [Jana, Lokshtanov, Mandal, Rai and Saurabh, MFCS 23']. Furthermore, our algorithms are always faster than the algorithms attained using Fidelity Preserving Transformations [Fellows, Kulik, Rosamond, and Shachnai, JCSS 18'].
Cite as

Barış Can Esmer and Ariel Kulik. Sampling with a Black Box: Faster Parameterized Approximation Algorithms for Vertex Deletion Problems. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 39:1-39:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{canesmer_et_al:LIPIcs.ICALP.2025.39,
  author =	{Can Esmer, Bar{\i}\c{s} and Kulik, Ariel},
  title =	{{Sampling with a Black Box: Faster Parameterized Approximation Algorithms for Vertex Deletion Problems}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{39:1--39:20},
  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.2025.39},
  URN =		{urn:nbn:de:0030-drops-234165},
  doi =		{10.4230/LIPIcs.ICALP.2025.39},
  annote =	{Keywords: Parameterized Approximation Algorithms, Random Sampling}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.35
Chain of Grounded Objectives: Concise Goal-Oriented Prompting for Code Generation

Authors: Sangyeop Yeo, Seung-Won Hwang, and Yu-Seung Ma

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
The use of Large Language Models (LLMs) for code generation has gained significant attention in recent years. Existing methods often aim to improve the quality of generated code by incorporating additional contextual information or guidance into input prompts. Many of these approaches adopt process-oriented reasoning strategies, mimicking human-like step-by-step thinking; however, they may not always align with the structured nature of programming languages. This paper introduces Chain of Grounded Objectives (CGO), a concise goal-oriented prompting approach that embeds functional objectives into prompts to enhance code generation. By focusing on precisely defined objectives rather than explicit procedural steps, CGO aligns more naturally with programming tasks while retaining flexibility. Empirical evaluations on HumanEval, MBPP, their extended versions, and LiveCodeBench show that CGO achieves accuracy comparable to or better than existing methods while using fewer tokens, making it a more efficient approach to LLM-based code generation.
Cite as

Sangyeop Yeo, Seung-Won Hwang, and Yu-Seung Ma. Chain of Grounded Objectives: Concise Goal-Oriented Prompting for Code Generation. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 35:1-35:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{yeo_et_al:LIPIcs.ECOOP.2025.35,
  author =	{Yeo, Sangyeop and Hwang, Seung-Won and Ma, Yu-Seung},
  title =	{{Chain of Grounded Objectives: Concise Goal-Oriented Prompting for Code Generation}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{35:1--35:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.35},
  URN =		{urn:nbn:de:0030-drops-233271},
  doi =		{10.4230/LIPIcs.ECOOP.2025.35},
  annote =	{Keywords: Artificial Intelligence, Natural Language Processing, Prompt Design, Large Language Models, Code Generation}
}
Document
DOI: 10.4230/OASIcs.FMBC.2025.3
Formal Verification in Solidity and Move: Insights from a Comparative Analysis

Authors: Massimo Bartoletti, Silvia Crafa, and Enrico Lipparini

Published in: OASIcs, Volume 129, 6th International Workshop on Formal Methods for Blockchains (FMBC 2025)

Abstract
Formal verification plays a crucial role in making smart contracts safer, being able to find bugs or to guarantee their absence, as well as checking whether the business logic is correctly implemented. For Solidity, even though there already exist several mature verification tools, the semantical quirks of the language can make verification quite hard in practice. Move, on the other hand, has been designed with security and verification in mind, and it has been accompanied since its early stages by a formal verification tool, the Move Prover. In this paper, we investigate through a comparative analysis: 1) how the different designs of the two contract languages impact verification, and 2) what is the state-of-the-art of verification tools for the two languages, and how do they compare on three paradigmatic use cases. Our investigation is supported by an open dataset of verification tasks performed in Certora and in the Aptos Move Prover.
Cite as

Massimo Bartoletti, Silvia Crafa, and Enrico Lipparini. Formal Verification in Solidity and Move: Insights from a Comparative Analysis. In 6th International Workshop on Formal Methods for Blockchains (FMBC 2025). Open Access Series in Informatics (OASIcs), Volume 129, pp. 3:1-3:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bartoletti_et_al:OASIcs.FMBC.2025.3,
  author =	{Bartoletti, Massimo and Crafa, Silvia and Lipparini, Enrico},
  title =	{{Formal Verification in Solidity and Move: Insights from a Comparative Analysis}},
  booktitle =	{6th International Workshop on Formal Methods for Blockchains (FMBC 2025)},
  pages =	{3:1--3:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-371-3},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{129},
  editor =	{Marmsoler, Diego and Xu, Meng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.FMBC.2025.3},
  URN =		{urn:nbn:de:0030-drops-230302},
  doi =		{10.4230/OASIcs.FMBC.2025.3},
  annote =	{Keywords: Smart contracts, Solidity, Move, Verification, Blockchain}
}
Document
DOI: 10.4230/LIPIcs.STACS.2025.17
Tight Approximation and Kernelization Bounds for Vertex-Disjoint Shortest Paths

Authors: Matthias Bentert, Fedor V. Fomin, and Petr A. Golovach

Published in: LIPIcs, Volume 327, 42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025)

Abstract
We examine the possibility of approximating Maximum Vertex-Disjoint Shortest Paths. In this problem, the input is an edge-weighted (directed or undirected) n-vertex graph G along with k terminal pairs (s_1,t_1),(s_2,t_2),…,(s_k,t_k). The task is to connect as many terminal pairs as possible by pairwise vertex-disjoint paths such that each path is a shortest path between the respective terminals. Our work is anchored in the recent breakthrough by Lochet [SODA '21], which demonstrates the polynomial-time solvability of the problem for a fixed value of k. Lochet’s result implies the existence of a polynomial-time ck-approximation for Maximum Vertex-Disjoint Shortest Paths, where c ≤ 1 is a constant. (One can guess 1/c terminal pairs to connect in k^O(1/c) time and then utilize Lochet’s algorithm to compute the solution in n^f(1/c) time.) Our first result suggests that this approximation algorithm is, in a sense, the best we can hope for. More precisely, assuming the gap-ETH, we exclude the existence of an o(k)-approximation within f(k) ⋅ poly(n) time for any function f that only depends on k. Our second result demonstrates the infeasibility of achieving an approximation ratio of m^{1/2-ε} in polynomial time, unless P = NP. It is not difficult to show that a greedy algorithm selecting a path with the minimum number of arcs results in a ⌈√𝓁⌉-approximation, where 𝓁 is the number of edges in all the paths of an optimal solution. Since 𝓁 ≤ n, this underscores the tightness of the m^{1/2-ε}-inapproximability bound. Additionally, we establish that Maximum Vertex-Disjoint Shortest Paths is fixed-parameter tractable when parameterized by 𝓁 but does not admit a polynomial kernel. Our hardness results hold for undirected graphs with unit weights, while our positive results extend to scenarios where the input graph is directed and features arbitrary (non-negative) edge weights.
Cite as

Matthias Bentert, Fedor V. Fomin, and Petr A. Golovach. Tight Approximation and Kernelization Bounds for Vertex-Disjoint Shortest Paths. In 42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 327, pp. 17:1-17:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bentert_et_al:LIPIcs.STACS.2025.17,
  author =	{Bentert, Matthias and Fomin, Fedor V. and Golovach, Petr A.},
  title =	{{Tight Approximation and Kernelization Bounds for Vertex-Disjoint Shortest Paths}},
  booktitle =	{42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025)},
  pages =	{17:1--17:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-365-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{327},
  editor =	{Beyersdorff, Olaf and Pilipczuk, Micha{\l} and Pimentel, Elaine and Thắng, Nguy\~{ê}n Kim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2025.17},
  URN =		{urn:nbn:de:0030-drops-228422},
  doi =		{10.4230/LIPIcs.STACS.2025.17},
  annote =	{Keywords: Inapproximability, Fixed-parameter tractability, Parameterized approximation}
}
Document
DOI: 10.4230/LIPIcs.STACS.2025.65
On Average Baby PIH and Its Applications

Authors: Yuwei Liu, Yijia Chen, Shuangle Li, Bingkai Lin, and Xin Zheng

Published in: LIPIcs, Volume 327, 42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025)

Abstract
The Parameterized Inapproximability Hypothesis (PIH) asserts that no FPT algorithm can decide whether a given 2CSP instance parameterized by the number of variables is satisfiable, or at most a constant fraction of its constraints can be satisfied simultaneously. In a recent breakthrough, Guruswami, Lin, Ren, Sun, and Wu (STOC 2024) proved the PIH under the Exponential Time Hypothesis (ETH). However, it remains a major open problem whether the PIH can be established assuming only W[1]≠FPT. Towards this goal, Guruswami, Ren, and Sandeep (CCC 2024) showed a weaker version of the PIH called the Baby PIH under W[1]≠FPT. In addition, they proposed one more intermediate assumption known as the Average Baby PIH, which might lead to further progress on the PIH. As the main contribution of this paper, we prove that the Average Baby PIH holds assuming W[1]≠FPT. Given a 2CSP instance where the number of its variables is the parameter, the Average Baby PIH states that no FPT algorithm can decide whether (i) it is satisfiable or (ii) any multi-assignment that satisfies all constraints must assign each variable more than r values on average for any fixed constant r > 1. So there is a gap between (i) and (ii) on the average number of values that are assigned to a variable, i.e., 1 vs. r. If this gap occurs in each variable instead of on average, we get the original Baby PIH. So central to our paper is an FPT self-reduction for 2CSP instances that turns the above gap for each variable into a gap on average. By the known W[1]-hardness for the Baby PIH, this proves that the Average Baby PIH holds under W[1] ≠ FPT. As applications, we obtain (i) for the first time, the W[1]-hardness of constant approximating k-ExactCover, and (ii) a tight relationship between running time lower bounds in the Average Baby PIH and approximating the parameterized Nearest Codeword Problem (k-NCP).
Cite as

Yuwei Liu, Yijia Chen, Shuangle Li, Bingkai Lin, and Xin Zheng. On Average Baby PIH and Its Applications. In 42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 327, pp. 65:1-65:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{liu_et_al:LIPIcs.STACS.2025.65,
  author =	{Liu, Yuwei and Chen, Yijia and Li, Shuangle and Lin, Bingkai and Zheng, Xin},
  title =	{{On Average Baby PIH and Its Applications}},
  booktitle =	{42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025)},
  pages =	{65:1--65:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-365-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{327},
  editor =	{Beyersdorff, Olaf and Pilipczuk, Micha{\l} and Pimentel, Elaine and Thắng, Nguy\~{ê}n Kim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2025.65},
  URN =		{urn:nbn:de:0030-drops-228900},
  doi =		{10.4230/LIPIcs.STACS.2025.65},
  annote =	{Keywords: Average Baby PIH, Parameterized Inapproximability, Constraint Satisfaction Problem, Exact Set Cover, W\lbrack1\rbrack-hardness}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2023.38
Quantum Algorithms and Lower Bounds for Linear Regression with Norm Constraints

Authors: Yanlin Chen and Ronald de Wolf

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

Abstract
Lasso and Ridge are important minimization problems in machine learning and statistics. They are versions of linear regression with squared loss where the vector θ ∈ ℝ^d of coefficients is constrained in either 𝓁₁-norm (for Lasso) or in 𝓁₂-norm (for Ridge). We study the complexity of quantum algorithms for finding ε-minimizers for these minimization problems. We show that for Lasso we can get a quadratic quantum speedup in terms of d by speeding up the cost-per-iteration of the Frank-Wolfe algorithm, while for Ridge the best quantum algorithms are linear in d, as are the best classical algorithms. As a byproduct of our quantum lower bound for Lasso, we also prove the first classical lower bound for Lasso that is tight up to polylog-factors.
Cite as

Yanlin Chen and Ronald de Wolf. Quantum Algorithms and Lower Bounds for Linear Regression with Norm Constraints. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 38:1-38:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{chen_et_al:LIPIcs.ICALP.2023.38,
  author =	{Chen, Yanlin and de Wolf, Ronald},
  title =	{{Quantum Algorithms and Lower Bounds for Linear Regression with Norm Constraints}},
  booktitle =	{50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)},
  pages =	{38:1--38:21},
  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.2023.38},
  URN =		{urn:nbn:de:0030-drops-180907},
  doi =		{10.4230/LIPIcs.ICALP.2023.38},
  annote =	{Keywords: Quantum algorithms, Regularized linear regression, Lasso, Ridge, Lower bounds}
}
Document
DOI: 10.4230/LIPIcs.STACS.2021.4
Improved (Provable) Algorithms for the Shortest Vector Problem via Bounded Distance Decoding

Authors: Divesh Aggarwal, Yanlin Chen, Rajendra Kumar, and Yixin Shen

Published in: LIPIcs, Volume 187, 38th International Symposium on Theoretical Aspects of Computer Science (STACS 2021)

Abstract
The most important computational problem on lattices is the Shortest Vector Problem (SVP). In this paper, we present new algorithms that improve the state-of-the-art for provable classical/quantum algorithms for SVP. We present the following results. 1) A new algorithm for SVP that provides a smooth tradeoff between time complexity and memory requirement. For any positive integer 4 ≤ q ≤ √n, our algorithm takes q^{13n+o(n)} time and requires poly(n)⋅ q^{16n/q²} memory. This tradeoff which ranges from enumeration (q = √n) to sieving (q constant), is a consequence of a new time-memory tradeoff for Discrete Gaussian sampling above the smoothing parameter. 2) A quantum algorithm that runs in time 2^{0.9533n+o(n)} and requires 2^{0.5n+o(n)} classical memory and poly(n) qubits. This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [Divesh Aggarwal et al., 2015] that has a time and space complexity 2^{n+o(n)}. 3) A classical algorithm for SVP that runs in time 2^{1.741n+o(n)} time and 2^{0.5n+o(n)} space. This improves over an algorithm of [Yanlin Chen et al., 2018] that has the same space complexity. The time complexity of our classical and quantum algorithms are expressed using a quantity related to the kissing number of a lattice. A known upper bound of this quantity is 2^{0.402n}, but in practice for most lattices, it can be much smaller and even 2^o(n). In that case, our classical algorithm runs in time 2^{1.292n} and our quantum algorithm runs in time 2^{0.750n}.
Cite as

Divesh Aggarwal, Yanlin Chen, Rajendra Kumar, and Yixin Shen. Improved (Provable) Algorithms for the Shortest Vector Problem via Bounded Distance Decoding. In 38th International Symposium on Theoretical Aspects of Computer Science (STACS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 187, pp. 4:1-4:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

Copy BibTex To Clipboard

@InProceedings{aggarwal_et_al:LIPIcs.STACS.2021.4,
  author =	{Aggarwal, Divesh and Chen, Yanlin and Kumar, Rajendra and Shen, Yixin},
  title =	{{Improved (Provable) Algorithms for the Shortest Vector Problem via Bounded Distance Decoding}},
  booktitle =	{38th International Symposium on Theoretical Aspects of Computer Science (STACS 2021)},
  pages =	{4:1--4:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-180-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{187},
  editor =	{Bl\"{a}ser, Markus and Monmege, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2021.4},
  URN =		{urn:nbn:de:0030-drops-136494},
  doi =		{10.4230/LIPIcs.STACS.2021.4},
  annote =	{Keywords: Lattices, Shortest Vector Problem, Discrete Gaussian Sampling, Time-Space Tradeoff, Quantum computation, Bounded distance decoding}
}
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