Tootfinder

An efficient quantum algorithm for computing $S$-units and its applications
Jean-Francois Biasse, Fang Song
https://arxiv.org/abs/2510.02280 https://arxiv.…

An efficient quantum algorithm for computing $S$-units and its applications
In this paper, we provide details on the proofs of the quantum polynomial time algorithm of Biasse and Song (SODA 16) for computing the $S$-unit group of a number field. This algorithm directly implies polynomial time methods to calculate class groups, S-class groups, relative class group and the unit group, ray class groups, solve the principal ideal problem, solve certain norm equations, and decompose ideal classes in the ideal class group. Additionally, combined with a result of Cramer, Duca…

On Euclidean Algorithms for oriented linear Grassmanians
Arnold Maxim, Karpenkov Oleg
https://arxiv.org/abs/2509.01733 https://arxiv.org/pdf/2509.01733

On Euclidean Algorithms for oriented linear Grassmanians
In this paper we study Euclidean algorithms and the corresponding continued fractions for oriented linear Grassmanians $G(k,n)$. We propose two algorithms: Maximal Element Elimination algorithm and Minimal Element Elimination algorithm. The first algorithm reduces the absolute maximal value of the Plücker coordinates; the algorithm works only in $G(2,n)$. The second algorithm eliminates the Plücker coordinate with the smallest absolute values, while all other coordinates may increase; the alg…

A James-Stein Estimator based Generalized OMP Algorithm for Robust Signal Recovery using Sparse Representation
Debraj Banerjee, Amitava Chatterjee
https://arxiv.org/abs/2509.01410

A James-Stein Estimator based Generalized OMP Algorithm for Robust Signal Recovery using Sparse Representation
In this paper, we introduce a novel algorithm named JS-gOMP, which enhances the generalized Orthogonal Matching Pursuit (gOMP) algorithm for improved noise robustness in sparse signal processing. The JS-gOMP algorithm uniquely incorporates the James-Stein estimator, optimizing the trade-off between signal recovery and noise suppression. This modification addresses the challenges posed by noise in the dictionary, a common issue in sparse representation scenarios. Comparative analyses demonstrate…

A Mixed Precision Eigensolver Based on the Jacobi Algorithm
Zhengbo Zhou
https://arxiv.org/abs/2509.00495 https://arxiv.org/pdf/2509.00495

A Mixed Precision Eigensolver Based on the Jacobi Algorithm
The classic method for computing the spectral decomposition of a real symmetric matrix, the Jacobi algorithm, can be accelerated by using mixed precision arithmetic. The Jacobi algorithm is aiming to reduce the off-diagonal entries iteratively using Givens rotations. We investigate how to use the low precision to speed up this algorithm based on the approximate spectral decomposition in low precision. We first study two different index choosing techniques, classical and cyclic-by-row, for the…

Exponential convergence of a distributed divide-and-conquer algorithm for constrained convex optimization on networks
Nazar Emirov, Guohui Song, Qiyu Sun
https://arxiv.org/abs/2510.01511

Exponential convergence of a distributed divide-and-conquer algorithm for constrained convex optimization on networks
We propose a divide-and-conquer (DAC) algorithm for constrained convex optimization over networks, where the global objective is the sum of local objectives attached to individual agents. The algorithm is fully distributed: each iteration solves local subproblems around selected fusion centers and coordinates only with neighboring fusion centers. Under standard assumptions of smoothness, strong convexity, and locality on the objective function, together with polynomial growth conditions on the …

Quantum Key-Recovery Attacks on FBC Algorithm
Yan-Ying Zhu, Bin-Bin Cai, Fei Gao, Song Lin
https://arxiv.org/abs/2508.00448 https://arxiv.org/pdf/2508.0044…

Quantum Key-Recovery Attacks on FBC Algorithm
With the advancement of quantum computing, symmetric cryptography faces new challenges from quantum attacks. These attacks are typically classified into two models: Q1 (classical queries) and Q2 (quantum superposition queries). In this context, we present a comprehensive security analysis of the FBC algorithm considering quantum adversaries with different query capabilities. In the Q2 model, we first design 4-round polynomial-time quantum distinguishers for FBC-F and FBC-KF structures, and then…

Algorithm Adaptation Bias in Recommendation System Online Experiments
Chen Zheng, Zhenyu Zhao
https://arxiv.org/abs/2509.00199 https://arxiv.org/pdf/2509.0…

Algorithm Adaptation Bias in Recommendation System Online Experiments
Online experiments (A/B tests) are widely regarded as the gold standard for evaluating recommender system variants and guiding launch decisions. However, a variety of biases can distort the results of the experiment and mislead decision-making. An underexplored but critical bias is algorithm adaptation effect. This bias arises from the flywheel dynamics among production models, user data, and training pipelines: new models are evaluated on user data whose distributions are shaped by the incumbe…

Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation
Guangzeng Han, Weisi Liu, Xiaolei Huang
https://arxiv.org/abs/2509.02040

Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation
Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that combines genetic algorithms with LLMs to augment synthetic data generation. Our approach treats semantic text attributes as gene sequences and leverages the LLM to simulate crossover and mutation operations. This genetic process enhances data quality and diversity by creating novel attribute combinations, yielding syntheti…

A Strongly Polynomial-Time Combinatorial Algorithm for the Nucleolus in Convex Games
Giacoomo Maggiorano, Alessandro Sosso, Gautier Stauffer
https://arxiv.org/abs/2509.02380 htt…

A Strongly Polynomial-Time Combinatorial Algorithm for the Nucleolus in Convex Games
The nucleolus is a fundamental solution concept in cooperative game theory, yet computing it is NP-hard in general. In convex games-where players' marginal contributions grow with coalition size-the only existing polynomial-time algorithm relies on the ellipsoid method. We re-examine a reduced game approach, refuting a previously claimed polynomial-time implementation and clarifying why it fails. By developing new algorithmic ideas and exploiting the structure of least core polyhedra, we show t…

An inside look at how Netflix's use of data led to generic "algorithm films" intended for broad appeal, with AI set to further entrench the production style (Phil Hoad/The Guardian)
https://www.theguardian.com/media/20…

An Anytime, Scalable and Complete Algorithm for Embedding a Manufacturing Procedure in a Smart Factory
Christopher Leet, Aidan Sciortino, Sven Koenig
https://arxiv.org/abs/2510.01770

An Anytime, Scalable and Complete Algorithm for Embedding a Manufacturing Procedure in a Smart Factory
Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. To embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Fa…

Green Traffic Engineering for Satellite Networks Using Segment Routing Flexible Algorithm
Jintao Liang, Pablo G. Madoery, Chung-Horng Lung, Halim Yanikomeroglu, Gunes Karabulut Kurt
https://arxiv.org/abs/2509.02149

Green Traffic Engineering for Satellite Networks Using Segment Routing Flexible Algorithm
Large-scale low-Earth-orbit (LEO) constellations demand routing that simultaneously minimizes energy, guarantees delivery under congestion, and meets latency requirements for time-critical flows. We present a segment routing over IPv6 (SRv6) flexible algorithm (Flex-Algo) framework that consists of three logical slices: an energy-efficient slice (Algo 130), a high-reliability slice (Algo 129), and a latency-sensitive slice (Algo 128). The framework provides a unified mixed-integer linear progra…

A polynomial algorithm to compute the boxicity and threshold dimension of complements of block graphs
Marco Caoduro, Will Evans, Tao Gaede
https://arxiv.org/abs/2510.02199 https…

A polynomial algorithm to compute the boxicity and threshold dimension of complements of block graphs
The boxicity of a graph $G$ is the minimum dimension $d$ that admits a representation of $G$ as the intersection graph of a family of axis-parallel boxes in $\mathbb{R}^d$. Computing boxicity is an NP-hard problem, and there are few known graph classes for which it can be computed in polynomial time. One such class is the class of block graphs. A block graph is a graph in which every maximal $2$-connected component is a clique. Since block graphs are known to have boxicity at most two, computin…

A Novel Algorithm for Representing Positive Semi-Definite Polynomials as Sums of Squares with Rational Coefficients
Zhenbing Zeng, Yong Huang, Lu Yang, Yongsheng Rao
https://arxiv.org/abs/2510.01568

A Novel Algorithm for Representing Positive Semi-Definite Polynomials as Sums of Squares with Rational Coefficients
This paper presents a novel algorithm for constructing a sum-of-squares (SOS) decomposition for positive semi-definite polynomials with rational coefficients. Unlike previous methods that typically yield SOS decompositions with floating-point coefficients, our approach ensures that all coefficients in the decomposition remain rational. This is particularly useful in formal verification and symbolic computation, where exact arithmetic is required. We introduce a stepwise reduction technique that…

Komitee Equal Shares: Choosing Together as Voters and as Groups with a Co-designed Virtual Budget Algorithm
Joshua C. Yang, Noemi Scheurer
https://arxiv.org/abs/2510.02040 https…

Komitee Equal Shares: Choosing Together as Voters and as Groups with a Co-designed Virtual Budget Algorithm
Public funding processes demand fairness, learning, and outcomes that participants can understand. We introduce Komitee Equal Shares, a priceable virtual-budget allocation framework that integrates two signals: in voter mode, participants cast point votes; in evaluator mode, small groups assess proposals against collectively defined impact fields. The framework extends the Method of Equal Shares by translating both signals into virtual spending power and producing voting receipts. We deployed t…

The Australian Identification, Nowcasting and Tracking Algorithm (A.I.N.T.)
Jordan Brook, Hamish McGowan, Matt Mason, Joshua Soderholm, Alain Protat
https://arxiv.org/abs/2509.02929

The Australian Identification, Nowcasting and Tracking Algorithm (A.I.N.T.)
We present the Australian Identification, Nowcasting and Tracking (AINT) algorithm, a radar-based system for detecting, characterising, and archiving thunderstorm activity across the Australian continent. AINT employs a two-step hysteresis-based identification scheme to separate embedded convective cells, followed by a phase-correlation and optimisation framework for storm tracking and cell matching. Post-processing routines address challenges of ground clutter using clustering and topography-a…

from my link log —
The CVM algorithm for count-distinct cardinality estimation.
https://www-cs-faculty.stanford.edu/~knuth/papers/cvm-note.pdf
saved 2025-11-02

OpenSSL Security Advisory [30th September 2025]
#openssl #vulnerability

Accuracy vs Performance: An abstraction model for deadline constrained offloading at the mobile-edge
Jamie Cotter, Ignacio Castineiras, Victor Cionca
https://arxiv.org/abs/2510.01885

Accuracy vs Performance: An abstraction model for deadline constrained offloading at the mobile-edge
In this paper, we present a solution for low-latency deadline-constrained DNN offloading on mobile edge devices. We design a scheduling algorithm with lightweight network state representation, considering device availability, communication on the network link, priority-aware pre-emption, and task deadlines. The scheduling algorithm aims to reduce latency by designing a resource availability representation, as well as a network discretisation and a dynamic bandwidth estimation mechanism. We impl…

MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking
Siyuan Cao, Hongxuan Wu, Jiabao Brad Wang, Yiliang Yuan, Mustafa Misir
https://arxiv.org/abs/2509.26377

MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking
Molecular docking is a core tool in drug discovery for predicting ligand-target interactions. Despite the availability of diverse search-based and machine learning approaches, no single docking algorithm consistently dominates, as performance varies by context. To overcome this challenge, algorithm selection frameworks such as GNNAS-Dock, built on graph neural networks, have been proposed. This study introduces an enhanced system, MC-GNNAS-Dock, with three key advances. First, a multi-criteria …

An improved algorithm for separating clock delays from ionospheric effects in radio astronomy
C. M. Cordun, M. A. Brentjens, H. K. Vedantham, M. Mevius
https://arxiv.org/abs/2510.01371

An improved algorithm for separating clock delays from ionospheric effects in radio astronomy
Context: Low-frequency radio observations are heavily impacted by the ionosphere, where dispersive delays can outpace even instrumental clock offsets, posing a serious calibration challenge. Especially below 100 MHz, phase unwrapping difficulties and higher-order dispersion effects can complicate the separation of ionospheric and clock delays. Aims: We address this challenge by introducing a method for reliably separating clock delays from ionospheric effects, even under mediocre to poor ionosp…

Kids and gun violence
The episode talked about how this mass murderer "true crime" brain-rot subculture flattens everything into just "content." They didn't talk about how algorithms play a role in this type of violence.
"The algorithm" incentivzes maximumally impactful content. When stripped of a moral framework, by capitalism and white supremacy, humans will search for a value system to inform them of what to do. A mass shooting is simply the lowest effort for the highest impact in terms of pure content generation.
This is simply the behavior incentivzed by the algorithm. The behavior incentivzed by capitalism is, of course, no less horrific but is presented as far more acceptable. It includes, of course, creating a machine that makes money off of content (without any differentiation in type) by maximizing engagement and incentivzing content generation.

I’m sure #YouTube's algorithm had its own reasons for popping this into my feed, but this timeless #YesMinister clip seems more appropriate now than ever: https://y…

FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
Florian Frommlet, Jon Lachmann, Geir Storvik, Aliaksandr Hubin
https://arxiv.org/abs/2509.00753 http…

FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
The FBMS R package facilitates Bayesian model selection and model averaging in complex regression settings by employing a variety of Monte Carlo model exploration methods. At its core, the package implements an efficient Mode Jumping Markov Chain Monte Carlo (MJMCMC) algorithm, designed to improve mixing in multi-modal posterior landscapes within Bayesian generalized linear models. In addition, it provides a genetically modified MJMCMC (GMJMCMC) algorithm that introduces nonlinear feature gener…

Improved $\ell_{p}$ Regression via Iteratively Reweighted Least Squares
Alina Ene, Ta Duy Nguyen, Adrian Vladu
https://arxiv.org/abs/2510.01729 https://arx…

Improved $\ell_{p}$ Regression via Iteratively Reweighted Least Squares
We introduce fast algorithms for solving $\ell_{p}$ regression problems using the iteratively reweighted least squares (IRLS) method. Our approach achieves state-of-the-art iteration complexity, outperforming the IRLS algorithm by Adil-Peng-Sachdeva (NeurIPS 2019) and matching the theoretical bounds established by the complex algorithm of Adil-Kyng-Peng-Sachdeva (SODA 2019, J. ACM 2024) via a simpler lightweight iterative scheme. This bridges the existing gap between theoretical and practical a…

Integrated photonic neuromorphic computing: device, architecture, chip, algorithm
Shuiying Xiang, Chengyang Yu, Yizhi Wang, Xintao Zeng, Yuna Zhang, Dianzhuang Zheng, Xinran Niu, Haowen Zhao, Hanxu Zhou, Yanan Han, Xingxing Guo, Yahui Zhang, Yue Hao
https://arxiv.org/abs/2509.01262

Integrated photonic neuromorphic computing: device, architecture, chip, algorithm
Artificial intelligence (AI) has experienced explosive growth in recent years. The large models have been widely applied in various fields, including natural language processing, image generation, and complex decision-making systems, revolutionizing technological paradigms across multiple industries. Nevertheless, the substantial data processing demands during model training and inference result in the computing power bottleneck. Traditional electronic chips based on the von Neumann architectur…

On the Perturbed Projection-Based Distributed Gradient-Descent Algorithm: A Fully-Distributed Adaptive Redesign
Tarek Bazizi, Mohamed Maghenem, Paolo Frasca, Antonio Lor\`ia, Elena Panteley
https://arxiv.org/abs/2509.03443

On the Perturbed Projection-Based Distributed Gradient-Descent Algorithm: A Fully-Distributed Adaptive Redesign
In this work, we revisit a classical distributed gradient-descent algorithm, introducing an interesting class of perturbed multi-agent systems. The state of each subsystem represents a local estimate of a solution to the global optimization problem. Thereby, the network is required to minimize local cost functions, while gathering the local estimates around a common value. Such a complex task suggests the interplay of consensus-based dynamics with gradient-descent dynamics. The latter descent d…

Quadratic Chabauty for Atkin-Lehner quotients of modular curves via weakly holomorphic modular forms: Hodge Filtrations
Isabel Rendell
https://arxiv.org/abs/2509.02291 https://

Quadratic Chabauty for Atkin-Lehner quotients of modular curves via weakly holomorphic modular forms: Hodge Filtrations
For Atkin-Lehner quotients $X_0^+(N)$, of prime level and of genus at least 2, we provide an algorithm for computing one of the main objects in the quadratic Chabauty algorithm in terms of weakly holomorphic modular forms associated to the curve. In particular, the algorithm computes a Hodge filtration on a certain unipotent vector bundle with connection related to $X_0^+(N)$, which is crucial in computing the $p$-adic height which is used to define the finite set of $p$-adic points containing …

Wasserstein normalized autoencoder for anomaly detection
CMS Collaboration
https://arxiv.org/abs/2510.02168 https://arxiv.org/pdf/2510.02168

Wasserstein normalized autoencoder for anomaly detection
A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution -- a Boltzmann distribution where the energy is the reconstruction error of the autoencoder -- and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets -- conical sprays of visible standard model particles an…

Two new NERDS papers: Algorithmic bias, street network simplification
https://nerds.itu.dk/2025/11/03/two-new-nerds-papers-algorithmic-bias-street-network-simplification/

An inside look at how Netflix's use of data led to generic "algorithm films" intended for broad appeal, with AI set to further entrench the production style (Phil Hoad/The Guardian)
https://www.theguardian.com/media/20…

PyramidStyler: Transformer-Based Neural Style Transfer with Pyramidal Positional Encoding and Reinforcement Learning
Raahul Krishna Durairaju (California State University, Fullerton), K. Saruladha (Puducherry Technological University)
https://arxiv.org/abs/2510.01715

PyramidStyler: Transformer-Based Neural Style Transfer with Pyramidal Positional Encoding and Reinforcement Learning
Neural Style Transfer (NST) has evolved from Gatys et al.'s (2015) CNN-based algorithm, enabling AI-driven artistic image synthesis. However, existing CNN and transformer-based models struggle to scale efficiently to complex styles and high-resolution inputs. We introduce PyramidStyler, a transformer framework with Pyramidal Positional Encoding (PPE): a hierarchical, multi-scale encoding that captures both local details and global context while reducing computational load. We further incorporat…

A threshold for online balancing of sparse i.i.d. vectors
Dylan J. Altschuler, Konstantin Tikhomirov
https://arxiv.org/abs/2509.02432 https://arxiv.org/pdf…

A threshold for online balancing of sparse i.i.d. vectors
Consider the task of \textit{online} vector balancing for stochastic arrivals $(X_i)_{i \in [T]}$, where the time horizon satisfies $T = Θ(n)$, and the $X_i$ are i.i.d uniform $d$--sparse $n$--dimensional binary vectors, with $2\leq d \le (\log\log n)^2/\log\log\log n$. We show that for this range of parameters, every online algorithm incurs discrepancy at least $Ω(\log \log n)$, and there is an efficient algorithm which achieves a matching discrepancy bound of $O(\log\log n)$ w.h.p. This est…

Category Theory for Supercomputing: The Tensor Product of Linear BSP Algorithms
Thomas Koopman, Rob H. Bisseling, Sven-Bodo Scholz
https://arxiv.org/abs/2510.00979 https://

Category Theory for Supercomputing: The Tensor Product of Linear BSP Algorithms
We show that a particular class of parallel algorithm for linear functions can be straightforwardly generalized to a parallel algorithm of their tensor product. The central idea is to take a model of parallel algorithms -- Bulk Synchronous Parallel (BSP) -- that decomposes parallel algorithms into so-called supersteps that are one of two types: a computation superstep that only does local computations, or a communication superstep that only communicates between processors. We connect each type …

HIV-1 protease cleavage sites detection with a Quantum convolutional neural network algorithm
Junggu Choi, Junho Lee, Kyle L. Jung, Jae U. Jung
https://arxiv.org/abs/2510.01993 …

HIV-1 protease cleavage sites detection with a Quantum convolutional neural network algorithm
In this study, we propose a quantum convolutional neural network (QCNN)-based framework with the neural quantum embedding (NQE) to predict HIV-1 protease cleavage sites in amino acid sequences from viral and human proteins. To assess the effectiveness and robustness of our framework, we compared the classification performance against classical neural networks under both noiseless and noisy simulations. Among experimental conditions, the QCNN with the angle and amplitude encoding NQE conditions …

Parallel Needleman-Wunsch on CUDA to measure word similarity based on phonetic transcriptions
Dominic Plein
https://arxiv.org/abs/2509.01654 https://arxiv.…

Parallel Needleman-Wunsch on CUDA to measure word similarity based on phonetic transcriptions
We present a method to calculate the similarity between words based on their phonetic transcription (their pronunciation) using the Needleman-Wunsch algorithm. We implement this algorithm in Rust and parallelize it on both CPU and GPU to handle large datasets efficiently. The GPU implementation leverages CUDA and the cudarc Rust library to achieve significant performance improvements. We validate our approach by constructing a fully-connected graph where nodes represent words and edges have wei…

Months ago (pre-Trump) I said the TikTok law would result in Trump weaponizing it if he got re-elected. Now with Ellison, Murdoch, and Dell (cowards and fascist enablers) it seems certain.
https://www.nbcnews.com/video/shorts/trump-jokes-…

Trump jokes he would make TikTok algorithm '100% MAGA'
After signing an executive order to preserve TikTok in the U.S, President Trump jokes he would make the algorithm "100% MAGA" if he could, and then continues on, saying, "Everyone is going to be treated fairly."

Canonical Reduction Systems in Artin-Tits groups of spherical type
Mar\'ia Cumplido, Juan Gonz\'alez-Meneses, Davide Perego
https://arxiv.org/abs/2510.00713 https://

Canonical Reduction Systems in Artin-Tits groups of spherical type
We introduce the canonical reduction system of an element in an Artin-Tits group of spherical type, which generalizes the similar notion for braids (and mapping classes) introduced by Birman, Lubotzky and McCarthy. We show its basic properties, which coincide with those satisfied in braid groups, and we provide an algorithm to compute it. We improve the algorithm in the case of braid groups, and discuss its complexity in this case. As a necessary result for obtaining the general algorithm, we p…

funOCLUST: Clustering Functional Data with Outliers
Katharine M. Clark, Paul D. McNicholas
https://arxiv.org/abs/2508.00110 https://arxiv.org/pdf/2508.0011…

funOCLUST: Clustering Functional Data with Outliers
Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the functional setting is proposed to address these issues. The approach leverages the OCLUST framework, creating a robust method to cluster curves and trim outliers. The methodology is evaluated on both simulated and real-world functional datasets, demonstrating strong performance in clustering and outlier identificatio…

Reducing Discomfort in Driving Simulators: Motion Cueing for Motion Sickness Mitigation
Varun Kotian, Vishrut Jain, Andrea Michelle Rios Lazcano, Daan Marinus Pool, Riender Happee, Barys Shyrokau
https://arxiv.org/abs/2510.01986

Reducing Discomfort in Driving Simulators: Motion Cueing for Motion Sickness Mitigation
Driving simulators are increasingly used in research and development. However, simulators often cause motion sickness due to downscaled motion and unscaled veridical visuals. In this paper, a motion cueing algorithm is proposed that reduces motion sickness as predicted by the subjective vertical conflict (SVC) model using model predictive control (MPC). Both sensory conflict and specific force errors are penalised in the cost function, allowing the algorithm to jointly optimise fidelity and com…

from my link log —
A content defined chunking algorithm testbed.
https://github.com/buildbarn/go-cdc
saved 2025-10-01 https://dotat.at/:/BZGS0.html

GitHub - buildbarn/go-cdc: Content Defined Chunking playground
Content Defined Chunking playground. Contribute to buildbarn/go-cdc development by creating an account on GitHub.

Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detection
Dalila Khettaf, Djamel Djenouri, Zeinab Rezaeifar, Youcef Djenouri
https://arxiv.org/abs/2509.23411

Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detection
This paper proposes a novel community detection method that integrates the Louvain algorithm with Graph Neural Networks (GNNs), enabling the discovery of communities without prior knowledge. Compared to most existing solutions, the proposed method does not require prior knowledge of the number of communities. It enhances the Louvain algorithm using node embeddings generated by a GNN to capture richer structural and feature information. Furthermore, it introduces a merging algorithm to refine th…

Computing weighted sheaf cohomology using noncommutative differential modules
Michael K. Brown, Daniel Erman
https://arxiv.org/abs/2510.02131 https://arxiv…

Computing weighted sheaf cohomology using noncommutative differential modules
We describe a novel method for computing sheaf cohomology over weighted projective stacks using exterior algebra and differential module techniques, generalizing an algorithm due to Eisenbud-Floystad-Schreyer over projective space.

A post from the archive 📫:
Algorithms of oppression
#musings

Efficient manifold evolution algorithm using adaptive B-Spline interpolation
Muhammad Ammad, Leevan Ling
https://arxiv.org/abs/2510.01790 https://arxiv.org…

Efficient manifold evolution algorithm using adaptive B-Spline interpolation
This paper explores an efficient Lagrangian approach for evolving point cloud data on smooth manifolds. In this preliminary study, we focus on analyzing plane curves, and our ultimate goal is to provide an alternative to the conventional radial basis function (RBF) approach for manifolds in higher dimensions. In particular, we use the B-Spline as the basis function for all local interpolations. Just like RBF and other smooth basis functions, B-Splines enable the approximation of geometric featu…

Learning to Ask: Decision Transformers for Adaptive Quantitative Group Testing
Mahdi Soleymani, Tara Javidi
https://arxiv.org/abs/2509.01723 https://arxiv.…

Learning to Ask: Decision Transformers for Adaptive Quantitative Group Testing
We consider the problem of quantitative group testing (QGT), where the goal is to recover a sparse binary vector from aggregate subset-sum queries: each query selects a subset of indices and returns the sum of those entries. Information-theoretic results suggest that adaptivity could yield up to a twofold reduction in the total number of required queries, yet no algorithm has surpassed the non-adaptive bound, leaving its practical benefit an open question. In this paper, we reduce the QGT probl…

Positive Univariate Polynomials: SOS certificates, algorithms, bit complexity, and T-systems
Mat\'ias Bender (TROPICAL), Philipp Di Dio (OURAGAN), Elias Tsigaridas (OURAGAN)
https://arxiv.org/abs/2510.01701

Positive Univariate Polynomials: SOS certificates, algorithms, bit complexity, and T-systems
We study certificates of positivity for univariate polynomials with rational coefficients that are positive over (an interval of) $\mathbb{R}$, given as weighted sums of squares (SOS) of rational polynomials. We build on the algorithm of Chevillard, Harrison, Joldes, and Lauter~\cite{chml-usos-alg-11}, which we call \usos. For a polynomial of degree~$d$ and coefficient bitsize~$τ$, we show that a rational weighted SOS representation can be computed in $\widetilde{\mathcal{O}}_B(d^3 + d^2 τ)$ …

A book excerpt details how a small team of content curators hired by ByteDance in Mexico City in 2018 shaped TikTok's For You algorithm in Latin America (Emily Baker-White/Forbes)
https://www.forbes.com/sites/emilybaker-wh

The Humble Origins Of TikTok’s Almighty Algorithm
An exclusive excerpt from Every Screen On The Planet reveals how the social media app’s powerful recommendation engine was shaped by a bunch of ordinary, twentysomething curators—including a guy named Jorge.

LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design
Matteo Boschini, Davide Gerosa, Alessandro Crespi, Matteo Falcone
https://arxiv.org/abs/2509.00159

LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design
Latin Hypercube Sampling (LHS) is a prominent tool in simulation design, with a variety of applications in high-dimensional and computationally expensive problems. LHS allows for various optimization strategies, most notably to ensure space-filling properties. However, LHS is a single-stage algorithm that requires a priori knowledge of the targeted sample size. In this work, we present LHS in LHS, a new expansion algorithm for LHS that enables the addition of new samples to an existing LHS-dist…

Efficient Dynamic Rank Aggregation
Morteza Alimi, Hourie Mehrabiun, Alireza Zarei
https://arxiv.org/abs/2509.02885 https://arxiv.org/pdf/2509.02885

Efficient Dynamic Rank Aggregation
The rank aggregation problem, which has many real-world applications, refers to the process of combining multiple input rankings into a single aggregated ranking. In dynamic settings, where new rankings arrive over time, efficiently updating the aggregated ranking is essential. This paper develops a fast, theoretically and practically efficient dynamic rank aggregation algorithm. First, we develop the LR-Aggregation algorithm, built on top of the LR-tree data structure, which is itself modeled …

Higher moment theory and learnability of bosonic states
Joseph T. Iosue, Yu-Xin Wang, Ishaun Datta, Soumik Ghosh, Changhun Oh, Bill Fefferman, Alexey V. Gorshkov
https://arxiv.org/abs/2510.01610

Higher moment theory and learnability of bosonic states
We present a sample- and time-efficient algorithm to learn any bosonic Fock state acted upon by an arbitrary Gaussian unitary. As a special case, this algorithm efficiently learns states produced in Fock state BosonSampling, thus resolving an open question put forth by Aaronson and Grewal (Aaronson, Grewal 2023). We further study a hierarchy of classes of states beyond Gaussian states that are specified by a finite number of their higher moments. Using the higher moments, we find a full spectru…

An intrusion detection system in internet of things using grasshopper optimization algorithm and machine learning algorithms
Shiva Sattarpour, Ali Barati, Hamid Barati
https://arxiv.org/abs/2509.01724 …

An intrusion detection system in internet of things using grasshopper optimization algorithm and machine learning algorithms
The Internet of Things (IoT) has emerged as a foundational paradigm supporting a range of applications, including healthcare, education, agriculture, smart homes, and, more recently, enterprise systems. However, significant advancements in IoT networks have been impeded by security vulnerabilities and threats that, if left unaddressed, could hinder the deployment and operation of IoT based systems. Detecting unwanted activities within the IoT is crucial, as it directly impacts confidentiality, …

A New Class of Linear Relations for Scalar Partitions
Boris Y. Rubinstein
https://arxiv.org/abs/2508.00677 https://arxiv.org/pdf/2508.00677

A New Class of Linear Relations for Scalar Partitions
A scalar integer partition problem asks for a number of nonnegative integer solutions to a linear Diophantine equation with integer positive coefficients. The manuscript discusses an algorithm of derivation of linear relations involving the finite number of scalar partitions. The algorithm employs the Cayley theorem about the reduction of a double partition to a sum of scalar partitions based on the variable elimination procedure.

SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images
Pushpendra Dhakara, Prachi Chachodhia, Vaibhav Kumar
https://arxiv.org/abs/2509.02287

SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images
Unstructured urban environments present unique challenges for scene understanding and generalization due to their complex and diverse layouts. We introduce SynthGenNet, a self-supervised student-teacher architecture designed to enable robust test-time domain generalization using synthetic multi-source imagery. Our contributions include the novel ClassMix++ algorithm, which blends labeled data from various synthetic sources while maintaining semantic integrity, enhancing model adaptability. We f…

Text-to-Layout: A Generative Workflow for Drafting Architectural Floor Plans Using LLMs
Jayakrishna Duggempudi, Lu Gao, Ahmed Senouci, Zhe Han, Yunpeng Zhang
https://arxiv.org/abs/2509.00543

Text-to-Layout: A Generative Workflow for Drafting Architectural Floor Plans Using LLMs
This paper presents the development of an AI-powered workflow that uses Large Language Models (LLMs) to assist in drafting schematic architectural floor plans from natural language prompts. The proposed system interprets textual input to automatically generate layout options including walls, doors, windows, and furniture arrangements. It combines prompt engineering, a furniture placement refinement algorithm, and Python scripting to produce spatially coherent draft plans compatible with design …

Benchmarking Quantum Simulation Methods
Calvin Ku, Yu-Cheng Chen, Alice Hu, Min-Hsiu Hsieh
https://arxiv.org/abs/2510.01710 https://arxiv.org/pdf/2510.0171…

Benchmarking Quantum Simulation Methods
Quantum Phase Estimation (QPE) is a cornerstone algorithm for fault-tolerant quantum computation, especially for electronic structure calculations of chemical systems. To accommodate the diverse characteristics of quantum chemical systems, numerous variants of QPE have been developed, each with distinct qubit and gate cost implications. In this paper, we quantify the impact of three key parameters on the overall quantum resource costs for the QPE algorithm: the choice between trotterization and…

Identifying All {\epsilon}-Best Arms in (Misspecified) Linear Bandits
Zhekai Li, Tianyi Ma, Cheng Hua, Ruihao Zhu
https://arxiv.org/abs/2510.00073 https://…

Identifying All ε-Best Arms in (Misspecified) Linear Bandits
Motivated by the need to efficiently identify multiple candidates in high trial-and-error cost tasks such as drug discovery, we propose a near-optimal algorithm to identify all ε-best arms (i.e., those at most ε worse than the optimum). Specifically, we introduce LinFACT, an algorithm designed to optimize the identification of all ε-best arms in linear bandits. We establish a novel information-theoretic lower bound on the sample complexity of this problem and demonstrate that LinFACT achieve…

Efficient QR-based Column Subset Selection through Randomized Sparse Embeddings
Israa Fakih, Laura Grigori
https://arxiv.org/abs/2509.03198 https://arxiv.o…

Efficient QR-based Column Subset Selection through Randomized Sparse Embeddings
In this paper, we introduce an efficient algorithm for column subset selection that combines the column-pivoted QR factorization with sparse subspace embeddings. The proposed method, SE-QRSC, is particularly effective for wide matrices with significantly more columns than rows. Starting from a matrix $A$, the algorithm selects $k$ columns from the sketched matrix $B = A Ω^T$, where $Ω$ is a sparse subspace embedding of $\mathrm{range}(A^T)$. The sparsity structure of $Ω$ is then exploited to…

Safe Memory Reclamation Techniques
Ajay Singh
https://arxiv.org/abs/2509.02457 https://arxiv.org/pdf/2509.02457

Safe Memory Reclamation Techniques
Safe memory reclamation is crucial to memory safety for optimistic and lock-free concurrent data structures in non garbage collected programming languages. However, several challenges arise in designing an ideal safe memory reclamation algorithm, including achieving high speed and scalability, easy of use for programmers, applicability to wide class of data structures, managing the large memory footprint caused by delayed freeing of memory for safety and performance, and avoiding asymmetric ove…

A Linear Programming Approach to Estimate the Core in Cooperative Games
J Camacho, JC Gon\c{c}alves-Dosantos, J S\'anchez-Soriano
https://arxiv.org/abs/2510.01766 https://…

A Linear Programming Approach to Estimate the Core in Cooperative Games
This paper proposes a novel algorithm to approximate the core of transferable utility (TU) cooperative games via linear programming. Given the computational hardness of determining the full core, our approach provides a tractable approximation by sampling extreme points through randomized linear problems (LPs). We analyze its convergence and computational complexity, and validate its effectiveness through extensive simulations on various game models. Our results show that the method is scalable…

Strata of Ecological Coexistence via Grassmannians
T\"urk\"u \"Ozl\"um \c{C}elik, Pierre A. Haas, Georgy Scholten, Kexin Wang, Giulio Zucal
https://arxiv.org/abs/2509.00165

Strata of Ecological Coexistence via Grassmannians
We study the Lotka--Volterra system from the perspective of computational algebraic geometry, focusing on equilibria that are both feasible and stable. These conditions stratifies the parameter space in $\mathbb{R}\times\mathbb{R}^{n\times n}$ with the feasible-stable semialgebraic sets. We encode them on the real Grassmannian ${\rm Gr}_{\mathbb{R}}(n,2n)$ via a parameter matrix representation, and use oriented matroid theory to develop an algorithm, combining Grassmann--Pl{ü}cker relations wi…

Closed-form Single UAV-aided Emitter Localization and Trajectory Design Using Doppler and TOA Measurements
Samaneh Motie, Hadi Zayyani, Mohammad Salman, Hasan Abu Hilal
https://arxiv.org/abs/2510.01778

Closed-form Single UAV-aided Emitter Localization and Trajectory Design Using Doppler and TOA Measurements
In this paper, a single Unmanned-Aerial-Vehicle (UAV)-aided localization algorithm which uses both Doppler and Time of Arrival (ToA) measurements is presented. In contrast to Doppler-based localization algorithms which are based on non-convex functions, exploiting ToA measurements in a Least-Square (LS) Doppler-based cost function, leads to a quadratic convex function whose minimizer lies on a line. Utilizing the ToA measurements in addition to the linear equation of minimizer, a closed form so…

A constrained optimization approach to nonlinear system identification through simulation error minimization
Vito Cerone, Sophie M. Fosson, Simone Pirrera, Diego Regruto
https://arxiv.org/abs/2509.01461

A constrained optimization approach to nonlinear system identification through simulation error minimization
This paper proposes a novel approach to system identification for nonlinear input-output models by minimizing the simulation error and formulating it as a constrained optimization problem. This method addresses vanishing gradient issues, enabling faster convergence than traditional gradient-based methods. We present an algorithm that utilizes feedback-linearization controlled multipliers optimization and provide a theoretical analysis of its performance. We prove that the algorithm converges to…

Understanding Quantum Imaginary Time Evolution and its Variational form
Angl\'es-Castillo Andreu, Ion Luca, Pandit Tanmoy, Gomez-Lurbe Rafael, Mart\'inez Rodrigo, Garcia-March Miguel Angel
https://arxiv.org/abs/2510.02015

Understanding Quantum Imaginary Time Evolution and its Variational form
Many computationally hard problems can be encoded in quantum Hamiltonians. The solution to these problems is given by the ground states of these Hamiltonians. A state-of-the-art algorithm for finding the ground state of a Hamiltonian is the so-called Quantum Imaginary Time Evolution (QITE) which approximates imaginary time evolution by a unitary evolution that can be implemented in quantum hardware. In this paper, we review the original algorithm together with a comprehensive computer program, …

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
Ziteng Gao, Jiaqi Qu, Chaoyu Chen
https://arxiv.org/abs/2509.01611 https:/…

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change decisions and lane change actions for autonomous vehicles within traffic flow. Firstly, a surrounding vehicles trajectory prediction method is proposed to reduce the risk of future behavior of surrounding vehicles to ego vehicle, and the prediction results are input into the rein…

Floquet-informed Learning of Periodically Driven Hamiltonians
Keren Li
https://arxiv.org/abs/2509.02331 https://arxiv.org/pdf/2509.02331

Floquet-informed Learning of Periodically Driven Hamiltonians
Characterizing time-periodic Hamiltonians is pivotal for validating and controlling driven quantum platforms, yet prevailing and unadjusted reconstruction methods demand dense time-domain sampling and heavy post-processing. We introduce a scalable Floquet-informed learning algorithm that represents the Hamiltonian as a truncated Fourier series and recasts parameter estimation as a compact linear inverse problem in the Floquet band picture. The algorithm is well-suited to problems satisfying mil…

On Generalized Forward-Reflected-Backward Method for Monotone Inclusion Problems
Santanu Soe, V. Vetrivel, Jen-Chih Yao
https://arxiv.org/abs/2509.02005 https://

On Generalized Forward-Reflected-Backward Method for Monotone Inclusion Problems
In this article, we analyze the generalized forward-reflected-backward (GFRB) method, which is an extension of the forward-reflected-backward (FRB) method due to Malitsky and Tam, for solving monotone inclusion problems in real Hilbert spaces. In particular, we study GFRB with an increasing step-size update that does not require prior knowledge of the Lipschitz constant to run the algorithm. In this sequel, we propose an extended version of the primal-dual twice-reflected algorithm (PDTR), whic…

White House says TikTok's new US entity would lease a copy of the algorithm from ByteDance, which Oracle would retrain; users won't need to re-download the app (Axios)
https://www.axios.com/2025/09/22/tiktok-bytedance-trump-algorithm-lease

Inside Trump's deal to save TikTok
TikTok's new U.S. entity would lease its algorithm from Chinese owner ByteDance.

Non-Linear Counterfactual Aggregate Optimization
Benjamin Heymann, Otmane Sakhi
https://arxiv.org/abs/2509.03438 https://arxiv.org/pdf/2509.03438

Non-Linear Counterfactual Aggregate Optimization
We consider the problem of directly optimizing a non-linear function of an outcome, where this outcome itself is the sum of many small contributions. The non-linearity of the function means that the problem is not equivalent to the maximization of the expectation of the individual contribution. By leveraging the concentration properties of the sum of individual outcomes, we derive a scalable descent algorithm that directly optimizes for our stated objective. This allows for instance to maximize…

Beyond the Algorithm: A Field Guide to Deploying AI Agents in Clinical Practice
Jack Gallifant, Katherine C. Kellogg, Matt Butler, Amanda Centi, Patrick F. Doyle, Sayon Dutta, Joyce Guo, Matthew J. Hadfield, Esther H. Kim, David E. Kozono, Hugo JWL Aerts, Adam B. Landman, Raymond H. Mak, Rebecca G. Mishuris, Tanna L. Nelson, Guergana K. Savova, Elad Sharon, Benjamin C. Silverman, Umit Topaloglu, Jeremy L. Warner, Danielle S. Bitterman

Beyond the Algorithm: A Field Guide to Deploying AI Agents in Clinical Practice
Large language models (LLMs) integrated into agent-driven workflows hold immense promise for healthcare, yet a significant gap exists between their potential and practical implementation within clinical settings. To address this, we present a practitioner-oriented field manual for deploying generative agents that use electronic health record (EHR) data. This guide is informed by our experience deploying the "irAE-Agent", an automated system to detect immune-related adverse events from clinical …

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Exponential convergence of a distributed divide-and-conquer algorithm for constrained convex opti...
Nazar Emirov, Guohui Song, Qiyu Sun

Time-marching multi-level variational multiscale tensor decomposition algorithm for heat conduction with moving heat source
Xinyi Guan, Jiayi Hu, Lei Zhang, Shaoqiang Tang, Wing Kam Liu
https://arxiv.org/abs/2510.00516

Time-marching multi-level variational multiscale tensor decomposition algorithm for heat conduction with moving heat source
In this paper, we propose a time-marching multi-level Variational Multiscale-Tensor Decomposition (VMS-TD) algorithm to solve the heat equation with a moving heat source model that arises from additive manufacturing. First, we take a second-order centered difference for time semi-discretization. The temperature field is decomposed according to multiple space resolution levels, each represented by the TD method. Then we adopt the VMS formulation [T.J.R. Hughes, G.R. Feijoo, L. Mazzei, J.B. Quinc…

A quantum analogue of convex optimization
Eunou Lee
https://arxiv.org/abs/2510.02151 https://arxiv.org/pdf/2510.02151…

A quantum analogue of convex optimization
Convex optimization is the powerhouse behind the theory and practice of optimization. We introduce a quantum analogue of unconstrained convex optimization: computing the minimum eigenvalue of a Schrödinger operator $h = -Δ+ V $ with convex potential $V:\mathbb R^n \rightarrow \mathbb R_{\ge 0}$ such that $V(x)\rightarrow\infty $ as $\|x\|\rightarrow\infty$. For this problem, we present an efficient quantum algorithm, called the Fundamental Gap Algorithm (FGA), that computes the mini…

Triangle Detection in Worst-Case Sparse Graphs via Local Sketching
Hongyi Duan, Jian'an Zhang
https://arxiv.org/abs/2509.03215 https://arxiv.org/pdf/25…

Triangle Detection in Worst-Case Sparse Graphs via Local Sketching
We present a non-algebraic, locality-preserving framework for triangle detection in worst-case sparse graphs. Our algorithm processes the graph in $O(\log n)$ independent layers and partitions incident edges into prefix-based classes where each class maintains a 1-sparse triple over a prime field. Potential witnesses are surfaced by pair-key (PK) alignment, and every candidate is verified by a three-stage, zero-false-positive pipeline: a class-level 1-sparse consistency check, two slot-level de…

An experience-based classification of quantum bugs in quantum software
Nils Quetschlich, Olivia Di Matteo
https://arxiv.org/abs/2509.03280 https://arxiv.or…

An experience-based classification of quantum bugs in quantum software
As quantum computers continue to improve in quality and scale, there is a growing need for accessible software frameworks for programming them. However, the unique behavior of quantum systems means specialized approaches, beyond traditional software development, are required. This is particularly true for debugging due to quantum bugs, i.e., bugs that occur precisely because an algorithm is a quantum algorithm. Pinpointing a quantum bug's root cause often requires significant developer time, as…

Robotic 3D Flower Pose Estimation for Small-Scale Urban Farms
Harsh Muriki, Hong Ray Teo, Ved Sengupta, Ai-Ping Hu
https://arxiv.org/abs/2509.02870 https://

Robotic 3D Flower Pose Estimation for Small-Scale Urban Farms
The small scale of urban farms and the commercial availability of low-cost robots (such as the FarmBot) that automate simple tending tasks enable an accessible platform for plant phenotyping. We have used a FarmBot with a custom camera end-effector to estimate strawberry plant flower pose (for robotic pollination) from acquired 3D point cloud models. We describe a novel algorithm that translates individual occupancy grids along orthogonal axes of a point cloud to obtain 2D images corresponding …

A primal-dual splitting algorithm with convex combination and larger step sizes for composite monotone inclusion problems
Xiaokai Chang, Junfeng Yang, Jianchao Bai, Jianxiong Cao
https://arxiv.org/abs/2510.00437

A primal-dual splitting algorithm with convex combination and larger step sizes for composite monotone inclusion problems
The primal-dual splitting algorithm (PDSA) by Chambolle and Pock is efficient for solving structured convex optimization problems. It adopts an extrapolation step and achieves convergence under certain step size condition. Chang and Yang recently proposed a modified PDSA for bilinear saddle point problems, integrating a convex combination step to enable convergence with extended step sizes. In this paper, we focus on composite monotone inclusion problems (CMIPs), a generalization of convex opti…

Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data
Anastasis Kratsios, Tin Sum Cheng, Daniel Roy
https://arxiv.org/abs/2509.00924

Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data
At its core, machine learning seeks to train models that reliably generalize beyond noisy observations; however, the theoretical vacuum in which state-of-the-art universal approximation theorems (UATs) operate isolates them from this goal, as they assume noiseless data and allow network parameters to be chosen freely, independent of algorithmic realism. This paper bridges that gap by introducing an architecture-specific randomized training algorithm that constructs a uniform approximator from $…

Decidability of polynomial equations over function fields in positive characteristic
Nicolas Daans
https://arxiv.org/abs/2509.02290 https://arxiv.org/pdf/2…

Decidability of polynomial equations over function fields in positive characteristic
Let $K$ be a field of positive characteristic with no algebraically closed subfield. Let $F$ be a function field over $K$ and $t \in F$ transcendental over $K$. Refining a result of Eisentr{ä}ger and Shlapentokh, we show that there is no algorithm which, on input a polynomial $f \in \mathbb{Z}[t][X_1, \ldots, X_n]$, determines whether $f$ has a zero in $F^n$. To this end, we revisit and partially extend several recent results from the literature on existential definability in function fields.

Tetris: Efficient Intra-Datacenter Calls Packing for Large Conferencing Services
Rohan Gandhi, Ankur Mallick, Ken Sueda, Rui Liang
https://arxiv.org/abs/2508.00426 https://

Tetris: Efficient Intra-Datacenter Calls Packing for Large Conferencing Services
Conference services like Zoom, Microsoft Teams, and Google Meet facilitate millions of daily calls, yet ensuring high performance at low costs remains a significant challenge. This paper revisits the problem of packing calls across Media Processor (MP) servers that host the calls within individual datacenters (DCs). We show that the algorithm used in Teams -- a large scale conferencing service as well as other state-of-art algorithms are prone to placing calls resulting in some of the MPs becom…

Performance-Guided Refinement for Visual Aerial Navigation using Editable Gaussian Splatting in FalconGym 2.0
Yan Miao, Ege Yuceel, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto, Sayan Mitra
https://arxiv.org/abs/2510.02248

Performance-Guided Refinement for Visual Aerial Navigation using Editable Gaussian Splatting in FalconGym 2.0
Visual policy design is crucial for aerial navigation. However, state-of-the-art visual policies often overfit to a single track and their performance degrades when track geometry changes. We develop FalconGym 2.0, a photorealistic simulation framework built on Gaussian Splatting (GSplat) with an Edit API that programmatically generates diverse static and dynamic tracks in milliseconds. Leveraging FalconGym 2.0's editability, we propose a Performance-Guided Refinement (PGR) algorithm, which con…

Global Optimization Algorithm for Mixed-Integer Nonlinear Programs with Trigonometric Functions
Christopher Montez, Sujeevraja Sanjeevi, Kaarthik Sundar
https://arxiv.org/abs/2509.26516

Global Optimization Algorithm for Mixed-Integer Nonlinear Programs with Trigonometric Functions
This article presents the first mixed-integer linear programming (MILP)-based iterative algorithm to solve factorable mixed-integer nonlinear programs (MINLPs) with bounded, differentiable periodic functions to global optimality with an emphasis on trigonometric functions. At each iteration, the algorithm solves a MILP relaxation of the original MINLP to obtain a bound on the optimal objective value. The relaxations are constructed using partitions of variables involved in each nonlinear term a…

The lattice packing problem in dimension 9 by Voronoi's algorithm
Mathieu Dutour Sikiri\'c, Wessel van Woerden
https://arxiv.org/abs/2508.20719 https://

The lattice packing problem in dimension 9 by Voronoi's algorithm
In 1908, Voronoi introduced an algorithm that solves the lattice packing problem in any dimension in finite time. Voronoi showed that any lattice with optimal packing density must be a so-called perfect lattice, and his algorithm enumerates the finitely many perfect lattices up to similarity in a fixed dimension. However, due to the high complexity of the algorithm this enumeration had, until now, only been completed up to dimension 8. In this work we compute all 2237251040 perfect lattices i…

Beyond asymptotic reasoning: a practical ground state projector based on the wall-Chebyshev expansion
Maria-Andreea Filip, Nathan Fitzpatrick
https://arxiv.org/abs/2508.00533 ht…

Beyond asymptotic reasoning: a practical ground state projector based on the wall-Chebyshev expansion
We introduce a quantum algorithm for ground-state preparation based on a Chebyshev series approximation to the wall function. This projector can be efficiently implemented as a product of Hamiltonian operators, enabling a straightforward realization via the linear combinations of unitaries method. We analyze the asymptotic scaling and provide numerical benchmarks, demonstrating that the wall-Chebyshev projector achieves competitive performance with leading methods based on imaginary time evolut…

A Block-Activated Decomposition Algorithm for Multi-Stage Stochastic Variational Inequalities
Minh N. B\`ui
https://arxiv.org/abs/2509.26198 https://arxiv.…

A Block-Activated Decomposition Algorithm for Multi-Stage Stochastic Variational Inequalities
We develop a block-activated decomposition algorithm for multi-stage stochastic variational inequalities with nonanticipativity constraints, which offers two computational novelties: (i) At each iteration, our method activates only a user-chosen block of scenarios. (ii) For each activated scenario, it employs the resolvent of the cost operator and the projector onto the constraint set separately. These features enhance computational tractability, in contrast with existing approaches, which ofte…

Efficient and Explicit Block Encoding of Finite Difference Discretizations of the Laplacian
Andreas Sturm, Niclas Schillo
https://arxiv.org/abs/2509.02429 https://

Efficient and Explicit Block Encoding of Finite Difference Discretizations of the Laplacian
The data input model is a fundamental component of every quantum algorithm, as its efficiency is crucial for achieving potential speed-ups over classical methods. For quantum linear algebra tasks that utilize quantum eigenvalue or singular value transformations, block encoding is the established technique for accessing matrix data. A key application of this is solving partial differential equations, where the Laplacian operator and its finite difference discretization serve as foundational exam…

Progressively Sampled Equality-Constrained Optimization
Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
https://arxiv.org/abs/2510.00417 https://arxiv.org…

Progressively Sampled Equality-Constrained Optimization
An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the constraints are defined by an expectation or an average over a large (finite) number of terms. The main idea of the algorithm is to solve a sequence of equality-constrained problems, each involving a finite sample of constraint-function terms, over which the sample set grows progressively. Under assumptions about the constraint functions and their first- and secon…

SPAM Tolerance for Pauli Error Estimation
Ryan O'Donnell, Samvitti Sharma
https://arxiv.org/abs/2510.00230 https://arxiv.org/pdf/2510.00230

SPAM Tolerance for Pauli Error Estimation
The Pauli channel is a fundamental model of noise in quantum systems, motivating the task of Pauli error estimation. We present an algorithm that builds on the reduction to Population Recovery introduced in [FO21]. Addressing an open question from that work, our algorithm has the key advantage of robustness against even severe state preparation and measurement (SPAM) errors. To tolerate SPAM, we must analyze Population Recovery on a combined erasure/bit-flip channel, which necessitates extendin…

Exponential Quantum Advantage for Message Complexity in Distributed Algorithms
Fran\c{c}ois Le Gall, Ma\"el Luce, Joseph Marchand, Mathieu Roget
https://arxiv.org/abs/2510.01657

Exponential Quantum Advantage for Message Complexity in Distributed Algorithms
We investigate how much quantum distributed algorithms can outperform classical distributed algorithms with respect to the message complexity (the overall amount of communication used by the algorithm). Recently, Dufoulon, Magniez and Pandurangan (PODC 2025) have shown a polynomial quantum advantage for several tasks such as leader election and agreement. In this paper, we show an exponential quantum advantage for a fundamental task: routing information between two specified nodes of a network.…

A Sensitivity-Based Method for Bilevel Optimization Problems: Theoretical Analysis and Computational Performance
Eduardo Nolasco, Ross D. King, Vassilios S. Vassiliadis
https://arxiv.org/abs/2510.01487

A Sensitivity-Based Method for Bilevel Optimization Problems: Theoretical Analysis and Computational Performance
Bilevel optimization provides a powerful framework for modeling hierarchical decision-making systems. This work presents a novel sensitivity-based algorithm that directly addresses the bilevel structure by treating the lower-level optimal solution as an implicit function of the upper-level variables, thus avoiding classical single-level reformulations. This implicit problem is solved within a robust Augmented Lagrangian framework, where the inner subproblems are managed by a quasi-Newton (L-BFG…

Beyond Belief Propagation: Cluster-Corrected Tensor Network Contraction with Exponential Convergence
Siddhant Midha, Yifan F. Zhang
https://arxiv.org/abs/2510.02290 https://

Beyond Belief Propagation: Cluster-Corrected Tensor Network Contraction with Exponential Convergence
Tensor network contraction on arbitrary graphs is a fundamental computational challenge with applications ranging from quantum simulation to error correction. While belief propagation (BP) provides a powerful approximation algorithm for this task, its accuracy limitations are poorly understood and systematic improvements remain elusive. Here, we develop a rigorous theoretical framework for BP in tensor networks, leveraging insights from statistical mechanics to devise a \emph{cluster expansion}…

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[2/2]:
- AdaSwitch: An Adaptive Switching Meta-Algorithm for Learning-Augmented Bounded-Influence Problems
Xi Chen, Yuze Chen, Yuan Zhou

A linesearch-based derivative-free method for noisy black-box problems
Alberto De Santis, Giampaolo Liuzzi, Stefano Lucidi
https://arxiv.org/abs/2508.00495 https://

A linesearch-based derivative-free method for noisy black-box problems
In this work we consider unconstrained optimization problems. The objective function is known through a zeroth order stochastic oracle that gives an estimate of the true objective function. To solve these problems, we propose a derivative-free algorithm based on extrapolation techniques. Under reasonable assumptions we are able to prove convergence properties for the proposed algorithms. Furthermore, we also give a worst-case complexity result stating that the total number of iterations where t…

Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold
Sayan Manna, Mahesh Mohan M R
https://arxiv.org/abs/2509.02374 https://arxiv…

Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold
Quantum algorithms operate on quantum states through unitary transformations in high dimensional complex Hilbert space. In this work, we propose a machine learning approach to create the quantum circuit using a single-layer complex-valued neural network. The input and ouput quantum states are provided to the network, which is trained to approximate the output state of a given quantum algorithm. To ensure that the fundamental property of unitarity is preserved throughout the training process, we…

A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization
Pierluigi Mansueto
https://arxiv.org/abs/2509.02409 https://arxiv.org…

A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization
We introduce a nonmonotone extension of the Front Descent framework for multiobjective optimization. The method uses novel nonmonotone line searches that allow temporary increases in some objective functions. To our knowledge, this is the first descent algorithm employing nonmonotone strategies to generate point sets approximating the Pareto front. We establish convergence properties for the resulting sequences of sets, analogous to the original framework, and present numerical results confirmi…

Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Adri\'an Mar{\i}n, Mauricio Soto-Gomez, Giorgio Valentini, Elena Casiraghi, Carlos Cano, Daniel Manzano
https://arxiv.org/abs/2510.01918

Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Graph Representation Learning (GRL) has emerged as a cornerstone technique for analysing complex, networked data across diverse domains, including biological systems, social networks, and data analysis. Traditional GRL methods often struggle to capture intricate relationships within complex graphs, particularly those exhibiting non-trivial structural properties such as power-law distributions or hierarchical structures. This paper introduces a novel quantum-inspired algorithm for GRL, utilizing…

A Proximal Descent Method for Minimizing Weakly Convex Optimization
Feng-Yi Liao, Yang Zheng
https://arxiv.org/abs/2509.02804 https://arxiv.org/pdf/2509.02…

A Proximal Descent Method for Minimizing Weakly Convex Optimization
We study the problem of minimizing a $m$-weakly convex and possibly nonsmooth function. Weak convexity provides a broad framework that subsumes convex, smooth, and many composite nonconvex functions. In this work, we propose a $\textit{proximal descent method}$, a simple and efficient first-order algorithm that combines the inexact proximal point method with classical convex bundle techniques. Our analysis establishes explicit non-asymptotic convergence rates in terms of $(η,ε)$-inexact stati…

Smooth Quasar-Convex Optimization with Constraints
David Mart\'inez-Rubio
https://arxiv.org/abs/2510.01943 https://arxiv.org/pdf/2510.01943

Smooth Quasar-Convex Optimization with Constraints
Quasar-convex functions form a broad nonconvex class with applications to linear dynamical systems, generalized linear models, and Riemannian optimization, among others. Current nearly optimal algorithms work only in affine spaces due to the loss of one degree of freedom when working with general convex constraints. Obtaining an accelerated algorithm that makes nearly optimal $\widetilde{O}(1/(γ\sqrtε))$ first-order queries to a $γ$-quasar convex smooth function \emph{with constraints} was i…

Study of nuclear magnetic resonance spectra with the multi-model multi-level quantum complex exponential least squares algorithm
Antonio Marquez Romero, Josh J. M. Kirsopp, Giuseppe Buonaiuto, Michal Krompiec
https://arxiv.org/abs/2509.24664

Study of nuclear magnetic resonance spectra with the multi-model multi-level quantum complex exponential least squares algorithm
We present a novel application of the multi-modal, multi-level quantum complex exponential least squares (MM-QCELS) algorithm, a state-of-the-art, early fault-tolerant quantum phase estimation (QPE) technique, to the simulation and analysis of nuclear magnetic resonance (NMR) of spin systems. By leveraging the robustness and precision of MM-QCELS, we demonstrate enhanced phase resolution in quantum simulations of spin dynamics, also in systems with complex coupling topologies. Our approach enab…

Deterministic Structure of Vertical Configurations in Minimal Picker Tours for Rectangular Warehouses
George Dunn, Elizabeth Stojanovski, Bishnu Lamichhane, Hadi Charkhgard, Ali Eshragh
https://arxiv.org/abs/2508.00365

Deterministic Structure of Vertical Configurations in Minimal Picker Tours for Rectangular Warehouses
The picker routing problem involves finding the shortest length tour of a warehouse that collects all items in a given pick-list. In this work, we demonstrate that in a rectangular warehouse, the horizontal structure of a minimal tour subgraph can be used to determine the required vertical edges. This result directly reduces the number of stages in the dynamic programming algorithm for warehouses with one or two blocks.