Tootfinder

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 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…

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…

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…

#SIGBOVIK is at it again: "Factoring up to 255 “with” a Quantum Computer"
"[…] in this paper, I will appear to succeed at factoring by failing with style"
htt…

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

A Quantum Algorithm for Nonlinear Electromagnetic Fluid Dynamics via Koopman-von Neumann Linearization
Hayato Higuchi, Yuki Ito, Kazuki Sakamoto, Keisuke Fujii, Akimasa Yoshikawa
https://arxiv.org/abs/2509.22503

A Quantum Algorithm for Nonlinear Electromagnetic Fluid Dynamics via Koopman-von Neumann Linearization
To simulate plasma phenomena, large-scale computational resources have been employed in developing high-precision and high-resolution plasma simulations. One of the main obstacles in plasma simulations is the requirement of computational resources that scale polynomially with the number of spatial grids, which poses a significant challenge for large-scale modeling. To address this issue, this study presents a quantum algorithm for simulating the nonlinear electromagnetic fluid dynamics that gov…

Automated Algorithm Design via Nevanlinna-Pick Interpolation
Ibrahim K. Ozaslan, Tryphon T. Georgiou, Mihailo R. Jovanovic
https://arxiv.org/abs/2509.21416 https://

Automated Algorithm Design via Nevanlinna-Pick Interpolation
The synthesis of optimization algorithms typically follows a design-first-analyze-later approach, which often obscures fundamental performance limitations and hinders the systematic design of algorithms operating at the achievable theoretical boundaries. Recently, a framework based on frequency-domain techniques from robust control theory has emerged as a powerful tool for automating algorithm synthesis. By integrating the design and analysis stages, this framework enables the identification of…

A Multi-Objective Genetic Algorithm for Healthcare Workforce Scheduling
Vipul Patel, Anirudh Deodhar, Dagnachew Birru
https://arxiv.org/abs/2508.20953 https://

A Multi-Objective Genetic Algorithm for Healthcare Workforce Scheduling
Workforce scheduling in the healthcare sector is a significant operational challenge, characterized by fluctuating patient loads, diverse clinical skills, and the critical need to control labor costs while upholding high standards of patient care. This problem is inherently multi-objective, demanding a delicate balance between competing goals: minimizing payroll, ensuring adequate staffing for patient needs, and accommodating staff preferences to mitigate burnout. We propose a Multi-objective G…

BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT
Luis F. Abanto-Leon, Muhammad Salman, Lismer Andres Caceres-Najarro
https://arxiv.org/abs/2509.24588

BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT
Leveraging received signal strength (RSS) measurements for indoor localization is highly attractive due to their inherent availability in ubiquitous wireless protocols. However, prevailing RSS-based methods often depend on complex computational algorithms or specialized hardware, rendering them impractical for low-cost access points. To address these challenges, this paper introduces buffer-aided RMSProp (BARProp), a fast and memory-efficient localization algorithm specifically designed for RSS…

Re-Representation in Sentential Relation Extraction with Sequence Routing Algorithm
Ramazan Ali Bahrami, Ramin Yahyapour
https://arxiv.org/abs/2508.21049 https://

Re-Representation in Sentential Relation Extraction with Sequence Routing Algorithm
Sentential relation extraction (RE) is an important task in natural language processing (NLP). In this paper we propose to do sentential RE with dynamic routing in capsules. We first show that the proposed approach outperform state of the art on common sentential relation extraction datasets Tacred, Tacredrev, Retacred, and Conll04. We then investigate potential reasons for its good performance on the mentioned datasets, and yet low performance on another similar, yet larger sentential RE datas…

Passive Learning of Lattice Automata from Recurrent Neural Networks
Jaouhar Slimi, Tristan Le Gall, Augustin Lemesle
https://arxiv.org/abs/2509.22489 https://

Passive Learning of Lattice Automata from Recurrent Neural Networks
We present a passive automata learning algorithm that can extract automata from recurrent networks with very large or even infinite alphabets. Our method combines overapproximations from the field of Abstract Interpretation and passive automata learning from the field of Grammatical Inference. We evaluate our algorithm by first comparing it with the state-of-the-art automata extraction algorithm from Recurrent Neural Networks trained on Tomita grammars. Then, we extend these experiments to regu…

Real-time implementation of vibrato transfer as an audio effect
Jeremy Hyrkas
https://arxiv.org/abs/2509.21544 https://arxiv.org/pdf/2509.21544

Real-time implementation of vibrato transfer as an audio effect
An algorithm for deriving delay functions based on real examples of vibrato was recently introduced and can be used to perform a vibrato transfer, in which the vibrato pattern of a target signal is imparted onto an incoming sound using a delay line. The algorithm contains methods that computationally restrict a real-time implementation. Here, a real-time approximation is presented that incorporates an efficient fundamental frequency estimation algorithm and time-domain polyphase IIR filters tha…

Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization
Boyu Ma, Jiaxiao Shi, Yiming Ji, Zhengpu Wang
https://arxiv.org/abs/2508.20458

Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization
This article proposes the Ecological Cycle Optimizer (ECO), a novel metaheuristic algorithm inspired by energy flow and material cycling in ecosystems. ECO draws an analogy between the dynamic process of solving optimization problems and ecological cycling. Unique update strategies are designed for the producer, consumer and decomposer, aiming to enhance the balance between exploration and exploitation processes. Through these strategies, ECO is able to achieve the global optimum, simulating th…

Short-Term Guidance Algorithm on a Drone Road System
Zhouyu Qu, Andreas Willig, Xiaobing Wu
https://arxiv.org/abs/2509.23794 https://arxiv.org/pdf/2509.237…

Short-Term Guidance Algorithm on a Drone Road System
Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced expanding use in urban environments in recent years. However, the growing density of drones raises significant challenges, such as avoiding collisions and managing air traffic efficiently, especially in congested areas. To address these issues, a structured road system and an effective guidance algorithm are essential. In this paper, we introduce a markup language allowing to describe drone road systems (DRS), in which …

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.

Machine-learning based particle-flow algorithm in CMS
Farouk Mokhtar
https://arxiv.org/abs/2508.20541 https://arxiv.org/pdf/2508.20541

Machine-learning based particle-flow algorithm in CMS
The particle-flow (PF) algorithm provides a global event description by reconstructing final-state particles and is central to event reconstruction in CMS. Recently, end-to-end machine learning (ML) approaches have been proposed to directly optimize physical quantities of interest and to leverage heterogeneous computing architectures. One such approach, machine-learned particle flow (MLPF), uses a transformer model to infer particles directly from tracks and clusters in a single pass. We presen…

Efficient Cost Bounds with Linear Maps
David M Kahn, Jan Hoffmann, Thomas Reps, Jessie Grosen
https://arxiv.org/abs/2509.22982 https://arxiv.org/pdf/2509.2…

Efficient Cost Bounds with Linear Maps
The Automatic Amortized Resource Analysis (AARA) derives program-execution cost bounds using types. To do so, AARA often makes use of cost-free types, which are critical for the composition of types and cost bounds. However, inferring cost-free types using the current state-of-the-art algorithm is expensive due to recursive dependence on additional cost-free types. Furthermore, that algorithm uses a heuristic only applicable to polynomial cost bounds, and not, e.g., exponential bounds. This pap…

pdGRASS: A Fast Parallel Density-Aware Algorithm for Graph Spectral Sparsification
Tiancheng Zhao, Zekun Yin, Huihai An, Xiaoyu Yang, Zhou Jin, Jiasi Shen, Helen Xu
https://arxiv.org/abs/2508.20403

pdGRASS: A Fast Parallel Density-Aware Algorithm for Graph Spectral Sparsification
Graph Spectral Sparsification (GSS) identifies an ultra-sparse subgraph, or sparsifier, whose Laplacian matrix closely approximates the spectral properties of the original graph, enabling substantial reductions in computational complexity for computationally intensive problems in scientific computing. The state-of-the-art method for efficient GSS is feGRASS, consisting of two steps: 1) spanning tree generation and 2) off-tree edge recovery. However, feGRASS suffers from two main issues: 1) diff…

Is it talent?
https://www.instagram.com/reel/DQM0-w2jEOd/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

Duaa Izzidien - Visual Storyteller & Artist on Instagram: "I had far too much fun creating this reel and couldn’t bring myself to delete any of it to make it shorter and more algorithm friendly 🙈 If you watched it all the way to the end - well done! You’ve just demonstrated the very thing the reel is about - showing up is the only talent that matters. My arrows don’t always hit the target. My paintings don’t always turn out how I planned. And honestly? Life rarely goes the way I intend. But really that isn’t what matters. In Islam we say that actions are by intentions and that sometimes means letting go of controlling our outcomes. We can control our intentions, our effort, showing up - but we have to remember that the result doesn’t actually come from those actions. Sometimes the arrow misses because there’s a better lesson waiting or perhaps it’s to remind you to stay humble and remember that ‘you’ are not the architect of your success. Sometimes the painting goes “wrong” because it’s becoming something more beautiful than you imagined. Sometimes life doesn’t work out the way you planned because there’s something different, better, round the corner for you. A huge thank you to @thabitoon_archers and @mamluk.academy for teaching me. You’ve taught me far more than just archery - you’ve taught me a rich history and life lessons that bring peace. (any mistakes in my form are entirely mine!). Want to learn how to use art as a tool for trusting and letting go of control? DM me ‘CREATE’ and I’ll show you these techniques. #showingisenough #trusttheprocess #archery #archerygirl #traditionalarchery #archerylife #overwhelm #personalgrowth #innerstrength #growthmindset #breakthrough #findingmyself #resilience #transformation #letgoofcontrol"
24K likes, 763 comments - duaaizzidien on October 24, 2025: "I had far too much fun creating this reel and couldn’t bring myself to delete any of it to make it shorter and more algorithm friendly 🙈 If you watched it all the way to the end - well done! You’ve just demonstrated the very thing the reel is about - showing up is the only talent that matters. My arrows don’t always hit the target. My paintings don’t always turn out how I planned. And honestly? Life rarely goes the way I …

Trajectory Prediction via Bayesian Intention Inference under Unknown Goals and Kinematics
Shunan Yin, Zehui Lu, Shaoshuai Mou
https://arxiv.org/abs/2509.24928 https://

Trajectory Prediction via Bayesian Intention Inference under Unknown Goals and Kinematics
This work introduces an adaptive Bayesian algorithm for real-time trajectory prediction via intention inference, where a target's intentions and motion characteristics are unknown and subject to change. The method concurrently estimates two critical variables: the target's current intention, modeled as a Markovian latent state, and an intention parameter that describes the target's adherence to a shortest-path policy. By integrating this joint update technique, the algorithm maintains robustnes…

This does rather explain why going to the cinema to watch a real film (köln 75 5* huge recommend) last weekend was such a brilliant experience. The streamers have deadened the experience of watching at home...
https://www.theguardian.com/media/2025/aug/28/bland-easy-to-follow-for-fans-of-everything-what-has-the-netflix-algorithm-done-to-our-films

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."

Computing global Ext for complexes
Michael K. Brown, Souvik Dey, Guanyu Li, Mahrud Sayrafi
https://arxiv.org/abs/2509.25103 https://arxiv.org/pdf/2509.2510…

Computing global Ext for complexes
We give a computational algorithm for computing Ext groups between bounded complexes of coherent sheaves on a projective variety, and we describe an implementation of this algorithm in Macaulay2. In particular, our results yield methods for computing derived global sections of bounded complexes of coherent sheaves and mutations of exceptional collections.

Enhancing Semantic Document Retrieval- Employing Group Steiner Tree Algorithm with Domain Knowledge Enrichment
Apurva Kulkarni, Chandrashekar Ramanathan, Vinu E Venugopal
https://arxiv.org/abs/2508.20543

Enhancing Semantic Document Retrieval- Employing Group Steiner Tree Algorithm with Domain Knowledge Enrichment
Retrieving pertinent documents from various data sources with diverse characteristics poses a significant challenge for Document Retrieval Systems. The complexity of this challenge is further compounded when accounting for the semantic relationship between data and domain knowledge. While existing retrieval systems using semantics (usually represented as Knowledge Graphs created from open-access resources and generic domain knowledge) hold promise in delivering relevant outcomes, their precisio…

A Near-Real-Time Reduction-Based Algorithm for Coloring Massive Graphs
Chenghao Zhu, Yi Zhou
https://arxiv.org/abs/2509.23606 https://arxiv.org/pdf/2509.23…

A Near-Real-Time Reduction-Based Algorithm for Coloring Massive Graphs
The graph coloring problem is a classical combinatorial optimization problem with important applications such as register allocation and task scheduling, and it has been extensively studied for decades. However, near-real-time algorithms that can deliver high-quality solutions for very large real-world graphs within a strict time frame remain relatively underexplored. In this paper, we try to bridge this gap by systematically investigating reduction rules that shrink the problem size while pres…

An Adaptive CUR Algorithm and its Application to Reduced-Order Modeling of Random PDEs
Grishma Palkar, Hessam Babaee
https://arxiv.org/abs/2509.21480 https://

An Adaptive CUR Algorithm and its Application to Reduced-Order Modeling of Random PDEs
Certain classes of CUR algorithms, also referred to as cross or pseudoskeleton algorithms, are widely used for low-rank matrix approximation when direct access to all matrix entries is costly. Their key advantage lies in constructing a rank-r approximation by sampling only r columns and r rows of the target matrix. This property makes them particularly attractive for reduced-order modeling of nonlinear matrix differential equations, where nonlinear operations on low-rank matrices can otherwise …

ImageMM - Joint Multi-frame Image Restoration and Super-resolution: #ImageProcessing

Splitting Alternating Algorithms for Sparse Solutions of Linear Systems with Concatenated Orthogonal Matrices
Yun-Bin Zhao, Zhong-Feng Sun
https://arxiv.org/abs/2509.24558 https…

Splitting Alternating Algorithms for Sparse Solutions of Linear Systems with Concatenated Orthogonal Matrices
A class of splitting alternating algorithms is proposed for finding the sparse solution of linear systems with concatenated orthogonal matrices. Depending on the number of matrices concatenated, the proposed algorithms are classified into the two-block splitting alternating algorithm (TSAA) and the multi-block splitting alternating algorithm (MSAA). These algorithms aim to decompose a large-scale linear system into two or more coupled subsystems, each significantly smaller than the original sys…

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…

SpinGPT: A Large-Language-Model Approach to Playing Poker Correctly
Narada Maugin, Tristan Cazenave
https://arxiv.org/abs/2509.22387 https://arxiv.org/pdf/…

SpinGPT: A Large-Language-Model Approach to Playing Poker Correctly
The Counterfactual Regret Minimization (CFR) algorithm and its variants have enabled the development of pokerbots capable of beating the best human players in heads-up (1v1) cash games and competing with them in six-player formats. However, CFR's computational complexity rises exponentially with the number of players. Furthermore, in games with three or more players, following Nash equilibrium no longer guarantees a non-losing outcome. These limitations, along with others, significantly restric…

Refined bit complexity for the computation of at least onepoint per connected component of a smooth completeintersection real algebraic set
Jesse Elliott (PolSys), Mark Giesbrecht (PolSys), Edern Gillot (PolSys), Mohab Safey El Din (PolSys), \'Eric Schost
https://arxiv.org/abs/2508.20607

Refined bit complexity for the computation of at least onepoint per connected component of a smooth completeintersection real algebraic set
We refine the bit complexity analysis of an algorithm for the computation of at least one point per connected component of a smooth real algebraic set, yielding exponential speedup (with respect to the number of variables) compared to prior works. The algorithm which is analyzed is based on the critical point method, reducing the problem to computations of critical points associated to the restriction of generic projections on lines to the studied variety. Our refinement, and the subsequent imp…

Generalization of Variadic Structures with Binders: A Tool for Structural Code Comparison
Alexander Baumgartner, Temur Kutsia
https://arxiv.org/abs/2509.25023 https://

Generalization of Variadic Structures with Binders: A Tool for Structural Code Comparison
This paper introduces a novel anti-unification algorithm for the generalization of variadic structures with binders, designed as a flexible tool for structural code comparison. By combining nominal techniques for handling variable binding with support for variadic expressions (common in abstract syntax trees and programming languages), the approach addresses key challenges such as overemphasis on bound variable names and difficulty handling insertions or deletions in code fragments. The algorit…

A simple algorithm for polarized parton evolution
Stefan H\"oche, Mareen Hoppe, Daniel Reichelt
https://arxiv.org/abs/2508.19018 https://arxiv.org/pdf…

A simple algorithm for polarized parton evolution
We present an algorithm to include the correlation between the production and decay planes of gluons in a parton-shower simulation. The technique is based on identifying the charge currents responsible for the creation and annihilation of the vector field. It is applicable in both the hard-collinear and the soft wide-angle region. As a function of the number of particles, the algorithm scales linear in computing time and memory. We demonstrate agreement with fixed-order perturbative calculation…

Array-Based Monte Carlo Tree Search
James Ragan, Fred Y. Hadaegh, Soon-Jo Chung
https://arxiv.org/abs/2508.20140 https://arxiv.org/pdf/2508.20140

Array-Based Monte Carlo Tree Search
Monte Carlo Tree Search is a popular method for solving decision making problems. Faster implementations allow for more simulations within the same wall clock time, directly improving search performance. To this end, we present an alternative array-based implementation of the classic Upper Confidence bounds applied to Trees algorithm. Our method preserves the logic of the original algorithm, but eliminates the need for branch prediction, enabling faster performance on pipelined processors, and …

A theoretical guarantee for SyncRank
Yang Rao
https://arxiv.org/abs/2509.22766 https://arxiv.org/pdf/2509.22766

A theoretical guarantee for SyncRank
We present a theoretical and empirical analysis of the SyncRank algorithm for recovering a global ranking from noisy pairwise comparisons. By adopting a complex-valued data model where the true ranking is encoded in the phases of a unit-modulus vector, we establish a sharp non-asymptotic recovery guarantee for the associated semidefinite programming (SDP) relaxation. Our main theorem characterizes a critical noise threshold - scaling as sigma = O(sqrt(n / log n)) - below which SyncRank achieves…

GPU-Accelerated Loopy Belief Propagation for Program Analysis
Haoyu Feng, Xin Zhang
https://arxiv.org/abs/2509.22337 https://arxiv.org/pdf/2509.22337

GPU-Accelerated Loopy Belief Propagation for Program Analysis
Loopy Belief Propagation (LBP) is a widely used approximate inference algorithm in probabilistic graphical models, with applications in computer vision, error correction codes, protein folding, program analysis, etc. However, LBP faces significant computational challenges when applied to large-scale program analysis. While GPU (Graphics Processing Unit) parallel computing provides a promising solution, existing approaches lack support for flexible update strategies and have yet to integrate log…

Symmetry-preserving random batch Ewald method for constant-potential simulation of electrochemical systems
Weihang Gao, Qi Zhou, Qianru Zhang, Zhenli Xu
https://arxiv.org/abs/2509.24742

Symmetry-preserving random batch Ewald method for constant-potential simulation of electrochemical systems
Constant potential molecular dynamics simulation plays important role for applications of electrochemical systems, yet the calculation of charge fluctuation on electrodes remains a computational bottleneck. We propose a highly scalable, symmetry-preserving random batch Ewald (SRBE) algorithm to address this challenge. The SRBE algorithm deterministically computes the low-frequency components along the direction perpendicular to electrodes, while efficiently approximating the remaining component…

"What do you lose by using Facebook", asked a friend who is keen to keep his account there for some reason.
What made me leave was the manipulation. I mean: they started hiding your friends posts in order to show you adverts instead! Feeding you slop from their promoted posts and "viral" messages (that aren't actually boosted by anyone, just picked out by their megaphone to show to everyone).
If you let the algorithm determine what you see than you let it determine what you are, who you become.
Even if you think it's better at finding shiny things than you, even if you think it builds the parasocial relationships that you want, even if you think it's saving you time to let the robot manage your reading-list: if you are letting Facebook, or any algorithm written by advertisers, do your reading selection then you are letting Facebook decide who you are.
On behalf of the advertisers who bribe them the most.
This is why you gotta use RSS. You gotta use the chronological timelines not the "for you" feeds. You gotta build follow relationships that you choose and understand because otherwise, you are letting the corporation and it's systems determine these things. You are abdicating some control of your very self to the machine.
Not to mention that we have to stop feeding money to the evil multi-trillion dollar companies built to control and manipulate us through our relationships with our friends. They have enough money, they need less participation not more.
#fediverse #algorithm

Geodesic complexity of the octahedron, and an algorithm for cut loci on convex polyhedra
Florian Frick, Pranav Rajbhandari
https://arxiv.org/abs/2508.19362 https://

Geodesic complexity of the octahedron, and an algorithm for cut loci on convex polyhedra
The geodesic complexity of a length space $X$ quantifies the required number of case distinctions to continuously choose a shortest path connecting any given start and end point. We prove a local lower bound for the geodesic complexity of $X$ obtained by embedding simplices into $X\times X$. We additionally create and prove correctness of an algorithm to find cut loci on surfaces of convex polyhedra, as the structure of a space's cut loci is related to its geodesic complexity. We use these tech…

Counting Fourier-Mukai partners of cubic fourfolds
Christian B\"ohning, Hans-Christian Graf von Bothmer, Lisa Marquand
https://arxiv.org/abs/2509.22491 https://

Counting Fourier-Mukai partners of cubic fourfolds
We develop an algorithm to count the number of virtual Fourier-Mukai partners for a given cubic fourfold, with initial input the primitive algebraic lattice and transcendental Hodge structure. Under some mild assumptions, we prove that our virtual count is enough to recover the actual count of Fourier-Mukai partners. We apply our algorithm to examples of cubics with a symplectic automorphism. In particular, we prove that a general cubic with a symplectic involution has 1120 non-trivial Fourier-…

On W-algebras and ODE/IM correspondence
Mat\v{e}j Kudrna, Tom\'a\v{s} Proch\'azka
https://arxiv.org/abs/2508.20793 https://arxiv.org/pdf/2508.20793…

On W-algebras and ODE/IM correspondence
We study the ODE/IM correspondence for two-dimensional conformal field theories with Virasoro and $\mathcal{W}_N$ symmetry. Building on earlier work establishing the correspondence, we develop a systematic algorithm for calculating the eigenvalues of local integrals of motion in terms of the Bethe roots using formal WKB expansions of wave functions associated to the differential operators. The method is demonstrated explicitly for Virasoro, $\mathcal{W}_3$, and $\mathcal{W}_4$ algebras, yieldin…

BBC Director of Children & Education Patricia Hidalgo says YouTube predominantly directs UK children to US content, weakening their ties to British culture (Michael Savage/The Guardian)
https://www.theguardian.com/tv-and-radio/2

Numerical analysis of scattering on combinatorial graphs
Moysey Brio, Jean-Guy Caputo
https://arxiv.org/abs/2508.20781 https://arxiv.org/pdf/2508.20781

Numerical analysis of scattering on combinatorial graphs
We investigate numerically the scattering of waves on discrete graphs. An efficient algorithm is developed to compute the reflection and transmission (spectral) coefficients. We then explore various configurations of input and output leads, demonstrating how bound states arising from specific vertex-lead connections result in total reflection. The impedance of the leads is shown to influence the spectral coefficients in a predictable manner. Furthermore, for a given input lead we show that the …

Good article, regardless your opinion about GenAI , it will change society.
#ai

The Third Algorithm
ChatGPT and other generative AI isn't about to create superintelligence, but even if what we see now is it, the impact is on the scale of Google search and algorithmic social media, except maybe worse

An algorithm for accurate and simple-looking metaphorical maps
Eleni Katsanou, Tamara Mchedlidze, Antonios Symvonis, Thanos Tolias
https://arxiv.org/abs/2508.19810 https://

An algorithm for accurate and simple-looking metaphorical maps
"Metaphorical maps" or "contact representations" are visual representations of vertex-weighted graphs that rely on the geographic map metaphor. The vertices are represented by countries, the weights by the areas of the countries, and the edges by contacts/ boundaries among them. The accuracy with which the weights are mapped to areas and the simplicity of the polygons representing the countries are the two classical optimization goals for metaphorical maps. Mchedlidze and Schnorr [Metaphoric Ma…

Untangling Vascular Trees for Surgery and Interventional Radiology
Guillaume Houry, Tom Boeken, St\'ephanie Allassonni\`ere, Jean Feydy
https://arxiv.org/abs/2509.23165 http…

Untangling Vascular Trees for Surgery and Interventional Radiology
The diffusion of minimally invasive, endovascular interventions motivates the development of visualization methods for complex vascular networks. We propose a planar representation of blood vessel trees which preserves the properties that are most relevant to catheter navigation: topology, length and curvature. Taking as input a three-dimensional digital angiography, our algorithm produces a faithful two-dimensional map of the patient's vessels within a few seconds. To this end, we propose opti…

A Consensus Algorithm for Second-Order Systems Evolving on Lie Groups
Akhil B Krishna, Farshad Khorrami, Anthony Tzes
https://arxiv.org/abs/2508.17473 https://

A Consensus Algorithm for Second-Order Systems Evolving on Lie Groups
In this paper, a consensus algorithm is proposed for interacting multi-agents, which can be modeled as simple Mechanical Control Systems (MCS) evolving on a general Lie group. The standard Laplacian flow consensus algorithm for double integrator systems evolving on Euclidean spaces is extended to a general Lie group. A tracking error function is defined on a general smooth manifold for measuring the error between the configurations of two interacting agents. The stability of the desired consens…

Some Simplifications for the Expectation-Maximization (EM) Algorithm: The Linear Regression Model Case
Daniel A. Griffith
https://arxiv.org/abs/2509.19461 https://

Some Simplifications for the Expectation-Maximization (EM) Algorithm: The Linear Regression Model Case
The EM algorithm is a generic tool that offers maximum likelihood solutions when datasets are incomplete with data values missing at random or completely at random. At least for its simplest form, the algorithm can be rewritten in terms of an ANCOVA regression specification. This formulation allows several analytical results to be derived that permit the EM algorithm solution to be expressed in terms of new observation predictions and their variances. Implementations can be made with a linear r…

Formal equivalence between global optimization consistency and random search
Ga\"etan Serr\'e (ENS Paris Saclay, CB)
https://arxiv.org/abs/2508.20671 https://

Formal equivalence between global optimization consistency and random search
We formalize a proof that any stochastic and iterative global optimization algorithm is consistent over Lipschitz continuous functions if and only if it samples the whole search space. To achieve this, we use the L$\exists$$\forall$N theorem prover and the Mathlib library. The major challenge of this formalization, apart from the technical aspects of the proof itself, is to converge to a definition of a stochastic and iterative global optimization algorithm that is both general enough to encomp…

A Chebyshev--Jackson series based block SS--RR algorithm for computing partial eigenpairs of real symmetric matrices
Zhongxiao Jia, Tianhang Liu
https://arxiv.org/abs/2508.20456

A Chebyshev--Jackson series based block SS--RR algorithm for computing partial eigenpairs of real symmetric matrices
This paper considers eigenpair computations of large symmetric matrices with the desired eigenvalues lying in a given interval using the contour integral-based block SS--RR method, a Rayleigh--Ritz projection onto a certain subspace generated by moment matrices. Instead of using a numerical quadrature to approximately compute the moments by solving a number of large shifted complex linear systems at each iteration, we make use of the Chebyshev--Jackson (CJ) series expansion to approximate the m…

Adaptive Pseudo-Marginal Algorithm
Sarra Abaoubida, Myl\`ene B\'edard, Florian Maire
https://arxiv.org/abs/2509.24820 https://arxiv.org/pdf/2509.24820

Adaptive Pseudo-Marginal Algorithm
The Pseudo-Marginal (PM) algorithm is a popular Markov chain Monte Carlo (MCMC) method used to sample from a target distribution when its density is inaccessible, but can be estimated with a non-negative unbiased estimator. Its performance depends on a key parameter, N, the number of iterations (or particles) used to approximate the target density. Larger values of N yield more accurate estimates but at increased running time. Previous studies has provided guidelines for selecting an optimal va…

Fairness for niche users and providers: algorithmic choice and profile portability
Elizabeth McKinnie, Anas Buhayh, Clement Canel, Robin Burke
https://arxiv.org/abs/2509.22660 h…

Fairness for niche users and providers: algorithmic choice and profile portability
Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an existing algorithm. What has rarely been studied is structural changes in the recommendation ecosystem itself. Our work explores the fairness impact of algorithmic pluralism, the idea that the recommendation algorithm is decoupled from the platform through which u…

Multiscale 2-Mapper -- Exploratory Data Analysis Guided by the First Betti Number
Halley Fritze
https://arxiv.org/abs/2509.22816 https://arxiv.org/pdf/2509…

Multiscale 2-Mapper -- Exploratory Data Analysis Guided by the First Betti Number
The Mapper algorithm is a fundamental tool in exploratory topological data analysis for identifying connectivity and topological clustering in data. Derived from the nerve construction, Mapper graphs can contain additional information about clustering density when considering the higher-dimensional skeleta. To observe two-dimensional features, and capture one-dimensional topology, we construct 2-Mapper. A common issue in using Mapper algorithms is parameter choice. We develop tools to choose 2-…

Online Firefighting on Cactus Graphs
Max Hugen, Bob Krekelberg, Alison Hsiang-Hsuan Liu
https://arxiv.org/abs/2509.22277 https://arxiv.org/pdf/2509.22277…

Online Firefighting on Cactus Graphs
It is known that the online firefighting is 2-competitive on trees (Coupechoux et al. 2016), which suggests that the problem is relatively easy on trees. We extend the study to graphs containing cycles. We first show that the presence of cycles gives a strong advantage to the adversary: cycles create situations where the algorithm and the optimal solution operate on different game states, and the adversary can exploit the uncertainty in the firefighter sequence to trap the algorithm. Specifical…

Enhanced UAV Path Planning Using the Tangent Intersection Guidance (TIG) Algorithm
Hichem Cheriet, Khellat Kihel Badra, Chouraqui Samira
https://arxiv.org/abs/2508.18967 https:/…

Enhanced UAV Path Planning Using the Tangent Intersection Guidance (TIG) Algorithm
Efficient and safe navigation of Unmanned Aerial Vehicles (UAVs) is critical for various applications, including combat support, package delivery and Search and Rescue Operations. This paper introduces the Tangent Intersection Guidance (TIG) algorithm, an advanced approach for UAV path planning in both static and dynamic environments. The algorithm uses the elliptic tangent intersection method to generate feasible paths. It generates two sub-paths for each threat, selects the optimal route base…

High-dimensional quantum Schur transforms
Adam Burchardt, Jiani Fei, Dmitry Grinko, Martin Larocca, Maris Ozols, Sydney Timmerman, Vladyslav Visnevskyi
https://arxiv.org/abs/2509.22640

High-dimensional quantum Schur transforms
The quantum Schur transform has become a foundational quantum algorithm, yet even after two decades since the seminal 2005 paper by Bacon, Chuang, and Harrow (BCH), some aspects of the transform remain insufficiently understood. Moreover, an alternative approach proposed by Krovi in 2018 was recently found to contain a crucial error. In this paper, we present a corrected version of Krovi's algorithm along with a detailed treatment of the high-dimensional version of the BCH Schur transform. This…

What fresh computer-generated hell is this? And don't call this thing "her." It's an algorithm. It's computer code. It's pronouns are "it." And what an insult to Scarlett Johansson and Natalie Portman, to say they can be replaced by a few thousand lines of code.

AI Actress Tilly Norwood Attracts Interest From Talent Agents
AI actress Tilly Norwood has attracted the attention of multiple talent agents.

A Square-Root Free Algorithm for Computing Real Givens Rotations
Carlos F. Borges
https://arxiv.org/abs/2508.19431 https://arxiv.org/pdf/2508.19431

A Square-Root Free Algorithm for Computing Real Givens Rotations
We develop an accurate square-root-free algorithm for constructing real Givens rotations. On processors that support the fused multiply-add operation in hardware, the algorithm is competitive with square-root based algorithms using a hardware square-root. Unlike the square-root-free algorithms in \cite{Hsieh1993,GENTLEMAN1973,Barlow1987,Hammarling1974ANO,Ling1989EfficientLL,Hanson,Hanson2}, our approach will construct the Givens rotation directly and is therefore applicable to a much wider vari…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- In-Context Algorithm Emulation in Fixed-Weight Transformers
Jerry Yao-Chieh Hu, Hude Liu, Jennifer Yuntong Zhang, Han Liu

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.

A New Inexact Manifold Proximal Linear Algorithm with Adaptive Stopping Criteria
Zhong Zheng, Xin Yu, Shiqian Ma, Lingzhou Xue
https://arxiv.org/abs/2508.19234 https://

A New Inexact Manifold Proximal Linear Algorithm with Adaptive Stopping Criteria
This paper studies the nonsmooth and nonconvex composite optimization on Riemannian manifolds. We propose a new inexact manifold proximal linear algorithm (IManPL) that incorporates both high- and low-accuracy conditions. At each iteration, IManPL solves convex subproblems on tangent spaces inexactly, guided by two adaptive stopping criteria. We establish convergence guarantees and show that IManPL achieves the best iteration complexity for solving the nonsmooth manifold composite optimization.…

APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection
Tobias Vente, Michael Heep, Abdullah Abbas, Theodor Sperle, Joeran Beel, Bart Goethals
https://arxiv.org/abs/2508.19399

APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection
Dataset selection is crucial for offline recommender system experiments, as mismatched data (e.g., sparse interaction scenarios require datasets with low user-item density) can lead to unreliable results. Yet, 86\% of ACM RecSys 2024 papers provide no justification for their dataset choices, with most relying on just four datasets: Amazon (38\%), MovieLens (34\%), Yelp (15\%), and Gowalla (12\%). While Algorithm Performance Spaces (APS) were proposed to guide dataset selection, their adoption h…

Iterative Partition Search Variational Quantum Algorithm for Solving Shortest Vector Problem
Zi-Wen Huang, Xiao-Hui Ni, Jia-Cheng Fan, Su-Juan Qin, Wei Huang, Bing-Jie Xu, Fei Gao
https://arxiv.org/abs/2508.18996

Iterative Partition Search Variational Quantum Algorithm for Solving Shortest Vector Problem
The Partition Search Algorithm (PSA) and the Iterative Quantum Optimization with an Adaptive Problem (IQOAP) framework are two existing Variational Quantum Algorithms (VQAs) for solving the Shortest Vector Problem (SVP), but both suffer from certain limitations. In this work, we proposed the Iterative Partition Search Algorithm (IPSA), which is a targeted synthesis and refinement of these preceding methods. Our algorithm inherits the core idea of ``partitioning to circumvent the zero vector" fr…

Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks
Amir Ziaeddini, Yauhen Yakimenka, J\"org Kliewer
https://arxiv.org/abs/2509.21688 https://…

Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks
We propose a power-controlled differentially private decentralized learning algorithm designed for a set of clients aiming to collaboratively train a common learning model. The network is characterized by a row-stochastic adjacency matrix, which reflects different channel gains between the clients. In our privacy-preserving approach, both the transmit power for model updates and the level of injected Gaussian noise are jointly controlled to satisfy a given privacy and energy budget. We show tha…

Effective Bertini theorems and zeros of $p$-adic forms of degree 7
Lea Beneish, Christopher Keyes
https://arxiv.org/abs/2508.20192 https://arxiv.org/pdf/25…

Effective Bertini theorems and zeros of $p$-adic forms of degree 7
We establish an effective Bertini-type theorem for hypersurfaces $X_f \colon f = 0$ defined over a finite field $k$ for which $f$ has no linear factors over the algebraic closure $\overline{k}$. Given a line $L$ defined over $k$ and a nonreduced $\overline{k}$-point $x$ on $X_f \cap L$, we give an upper bound on the number of planes $P$ containing $L$ for which $X_f \cap P$ contains a line through $x$. Underlying this result is a factorization algorithm for bivariate polynomials originally due …

Autonomous Detection and Coverage of Unknown Target Areas by Multi-Agent Systems
Jie Song, Yang Bai, Mikhail Svinin, Naoki Wakamiya
https://arxiv.org/abs/2509.24399 https://

Autonomous Detection and Coverage of Unknown Target Areas by Multi-Agent Systems
This paper presents a novel coverage control algorithm for multi-agent systems, where each agent has no prior knowledge of the specific region to be covered. The proposed method enables agents to autonomously detect the target area and collaboratively achieve full coverage. Once an agent detects a part of the target region within its sensor range, a dynamically constructed density function is generated to attract nearby agents. By integrating this density-driven mechanism with Centroidal Vorono…

Binary Representation in Multicomplex and Clifford Algebras
Derek Courchesne, S\'ebastien Tremblay
https://arxiv.org/abs/2509.23369 https://arxiv.org/p…

Binary Representation in Multicomplex and Clifford Algebras
Using a binary representation for basis elements of an algebra combined with a framework of multiplier and index functions, a connection has been established between the structure of a large class of algebras and the XOR componentwise operation. This result covers both multicomplex and Clifford algebras of any dimension. Necessary conditions for the existence of a diagonal basis are derived directly from this result, with an algorithm-like method to obtain it through a change-of-coordinates mat…

Distributed Sparsest Cut via Eigenvalue Estimation
Yannic Maus, Tijn de Vos
https://arxiv.org/abs/2508.19898 https://arxiv.org/pdf/2508.19898

Distributed Sparsest Cut via Eigenvalue Estimation
We give new, improved bounds for approximating the sparsest cut value or in other words the conductance $ϕ$ of a graph in the CONGEST model. As our main result, we present an algorithm running in $O(\log^2 n/ϕ)$ rounds in which every vertex outputs a value $\tilde ϕ$ satisfying $ϕ\le \tilde ϕ\le \sqrt{2.01ϕ}$. In most regimes, our algorithm improves significantly over the previously fastest algorithm for the problem [Chen, Meierhans, Probst Gutenberg, Saranurak; SODA 25]. Additionally, ou…

White House says TikTok's new US entity would lease a copy of ByteDance's algorithm, which Oracle would retrain; US users wouldn'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.

Universal Solution to Kronecker Product Decomposition
Daizhan Cheng
https://arxiv.org/abs/2509.22373 https://arxiv.org/pdf/2509.22373

Universal Solution to Kronecker Product Decomposition
This paper provides a general solution for the Kronecker product decomposition (KPD) of vectors, matrices, and hypermatrices. First, an algorithm, namely, monic decomposition algorithm (MDA), is reviewed. It consists of a set of projections from a higher dimension Euclidian space to its factor-dimension subspaces. It is then proved that the KPD of vectors is solvable, if and only if, the project mappings provide the required decomposed vectors. Hence it provides an easily verifiable necessary a…

Uncertainty-Aware Multi-Robot Task Allocation With Strongly Coupled Inter-Robot Rewards
Ben Rossano, Jaein Lim, Jonathan P. How
https://arxiv.org/abs/2509.22469 https://

Uncertainty-Aware Multi-Robot Task Allocation With Strongly Coupled Inter-Robot Rewards
This paper proposes a task allocation algorithm for teams of heterogeneous robots in environments with uncertain task requirements. We model these requirements as probability distributions over capabilities and use this model to allocate tasks such that robots with complementary skills naturally position near uncertain tasks, proactively mitigating task failures without wasting resources. We introduce a market-based approach that optimizes the joint team objective while explicitly capturing cou…

Intelligent Algorithm Selection for Recommender Systems: Meta-Learning via in-depth algorithm feature engineering
Jarne Mathi Decker
https://arxiv.org/abs/2509.20134 https://

Intelligent Algorithm Selection for Recommender Systems: Meta-Learning via in-depth algorithm feature engineering
The "No Free Lunch" theorem dictates that no single recommender algorithm is optimal for all users, creating a significant Algorithm Selection Problem. Standard meta-learning approaches aim to solve this by selecting an algorithm based on user features, but treat the fundamentally diverse algorithms themselves as equivalent, "black-box" choices. This thesis investigates the impact of overcoming this limitation by engineering a comprehensive feature set to explicitly characterize the algorithms …

Preference-Based Dynamic Ranking Structure Recognition
Nan Lu, Jian Shi, Xin-Yu Tian
https://arxiv.org/abs/2509.24493 https://arxiv.org/pdf/2509.24493

Preference-Based Dynamic Ranking Structure Recognition
Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic…

Bundle Network: a Machine Learning-Based Bundle Method
Francesca Demelas, Joseph Le Roux, Antonio Frangioni, Mathieu Lacroix, Emiliano Traversi, Roberto Wolfler Calvo
https://arxiv.org/abs/2509.24736

Bundle Network: a Machine Learning-Based Bundle Method
This paper presents Bundle Network, a learning-based algorithm inspired by the Bundle Method for convex non-smooth minimization problems. Unlike classical approaches that rely on heuristic tuning of a regularization parameter, our method automatically learns to adjust it from data. Furthermore, we replace the iterative resolution of the optimization problem that provides the search direction-traditionally computed as a convex combination of gradients at visited points-with a recurrent neural mo…

Universal $L_2$-approximation using median lattice algorithms
Zexin Pan, Takashi Goda, Peter Kritzer
https://arxiv.org/abs/2509.24582 https://arxiv.org/pdf…

Universal $L_2$-approximation using median lattice algorithms
We study the problem of multivariate $L_2$-approximation of functions in a weighted Korobov space using a median lattice-based algorithm recently proposed by the authors. In the original work, the algorithm requires knowledge of the smoothness and weights of the Korobov space to construct the hyperbolic cross index set, where each coefficient is estimated via the median of approximations obtained from randomly shifted, randomly chosen rank-1 lattice rules. In this paper, we introduce a \emph{un…

Evaluating Massively Parallel Algorithms for DFA Minimisation, Equivalence Checking and Inclusion Checking
Jan Heemstra, Jan Martens, Anton Wijs
https://arxiv.org/abs/2508.20735

Evaluating Massively Parallel Algorithms for DFA Minimisation, Equivalence Checking and Inclusion Checking
We study parallel algorithms for the minimisation and equivalence checking of Deterministic Finite Automata (DFAs). Regarding DFA minimisation, we implement four different massively parallel algorithms on Graphics Processing Units~(GPUs). Our results confirm the expectations that the algorithm with the theoretically best time complexity is not practically suitable to run on GPUs due to the large amount of resources needed. We empirically verify that parallel partition refinement algorithms from…

Reconstruction of acoustic sources from the initial arrival time of waves
Qiuyi Li, Bo Chen, Peng Gao, Yu Sun, Yao Sun
https://arxiv.org/abs/2509.23819 https://

Reconstruction of acoustic sources from the initial arrival time of waves
In this paper, a novel time domain sampling method based on the initial arrival time of waves is proposed to reconstruct acoustic sources, including point sources, curve sources, surface sources and block sources. The uniqueness of reconstructing sources whose spatial support is a convex region is proved. Theoretical analyses are provided to demonstrate the validity of the proposed sampling method in reconstructing various types of sound sources. The proposed algorithm does not involve the time…

Unlocking the Potential of Soft Actor-Critic for Imitation Learning
Nayari Marie Lessa, Melya Boukheddimi, Frank Kirchner
https://arxiv.org/abs/2509.24539 https://

Unlocking the Potential of Soft Actor-Critic for Imitation Learning
Learning-based methods have enabled robots to acquire bio-inspired movements with increasing levels of naturalness and adaptability. Among these, Imitation Learning (IL) has proven effective in transferring complex motion patterns from animals to robotic systems. However, current state-of-the-art frameworks predominantly rely on Proximal Policy Optimization (PPO), an on-policy algorithm that prioritizes stability over sample efficiency and policy generalization. This paper proposes a novel IL f…

Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
Haodong Liang, Yanhao Jin, Krishnakumar Balasubramanian, Lifeng Lai
https://arxiv.org/abs/2509.22794

Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
We study instrumental variable regression (IVaR) under differential privacy constraints. Classical IVaR methods (like two-stage least squares regression) rely on solving moment equations that directly use sensitive covariates and instruments, creating significant risks of privacy leakage and posing challenges in designing algorithms that are both statistically efficient and differentially private. We propose a noisy two-state gradient descent algorithm that ensures $ρ$-zero-concentrated differ…

Energy minimization for skyrmions on planar thin films
Giovanni Di Fratta, Michael Innerberger, Dirk Praetorius, Valeriy Slastikov
https://arxiv.org/abs/2509.23495 https://

Energy minimization for skyrmions on planar thin films
We consider an energy functional that arises in micromagnetic and liquid crystal theory on thin films. In particular, our energy comprises a non-convex term that models anti-symmetric exchange as well as an anisotropy term. We devise an algorithm for energy minimization in the continuous case and show weak convergence of a subsequence towards a solution of the corresponding Euler--Lagrange equation. Furthermore, an algorithm for numerical energy minimization is presented. We show empirically th…

An Optimal Sorting Algorithm for Persistent Random Comparison Faults
Barbara Geissmann, Stefano Leucci, Chih-Hung Liu, Paolo Penna
https://arxiv.org/abs/2508.19785 https://

An Optimal Sorting Algorithm for Persistent Random Comparison Faults
We consider the problem of sorting $n$ elements subject to persistent random comparison errors. In this problem, each comparison between two elements can be wrong with some fixed (small) probability $p$, and comparing the same pair of elements multiple times always yields the same result. Sorting perfectly in this model is impossible, and the objective is to minimize the dislocation of each element in the output sequence, i.e., the difference between its position in the sequence and its true ra…

Simplex Frank-Wolfe: Linear Convergence and Its Numerical Efficiency for Convex Optimization over Polytopes
Haoning Wang, Houduo Qi, Liping Zhang
https://arxiv.org/abs/2509.24279

Simplex Frank-Wolfe: Linear Convergence and Its Numerical Efficiency for Convex Optimization over Polytopes
We investigate variants of the Frank-Wolfe (FW) algorithm for smoothing and strongly convex optimization over polyhedral sets, with the goal of designing algorithms that achieve linear convergence while minimizing per-iteration complexity as much as possible. Starting from the simple yet fundamental unit simplex, and based on geometrically intuitive motivations, we introduce a novel oracle called Simplex Linear Minimization Oracle (SLMO), which can be implemented with the same complexity as the…

Optimal phase change for a generalized Grover's algorithm
Christopher Cardullo, Min Kang
https://arxiv.org/abs/2509.20610 https://arxiv.org/pdf/2509.20…

Optimal phase change for a generalized Grover's algorithm
We study the generalized Grover's algorithm with an arbitrary amplitude vector to find the optimal phase change for maximizing the gain in probability for the target of each iteration. In the classic setting of Grover's algorithm with a real initial amplitude vector, we find that a phase change of $π$ stays optimal until the probability of observing the target is quite close to 1. We provide a formula for identifying this cut-off point based on the size of the data set. When the amplitude is t…

Good Weights: Proactive, Adaptive Dead Reckoning Fusion for Continuous and Robust Visual SLAM
Yanwei Du, Jing-Chen Peng, Patricio A. Vela
https://arxiv.org/abs/2509.22910 https:…

Good Weights: Proactive, Adaptive Dead Reckoning Fusion for Continuous and Robust Visual SLAM
Given that Visual SLAM relies on appearance cues for localization and scene understanding, texture-less or visually degraded environments (e.g., plain walls or low lighting) lead to poor pose estimation and track loss. However, robots are typically equipped with sensors that provide some form of dead reckoning odometry with reasonable short-time performance but unreliable long-time performance. The Good Weights (GW) algorithm described here provides a framework to adaptively integrate dead reck…

Stochastic Gradients under Nuisances
Facheng Yu, Ronak Mehta, Alex Luedtke, Zaid Harchaoui
https://arxiv.org/abs/2508.20326 https://arxiv.org/pdf/2508.2032…

Stochastic Gradients under Nuisances
Stochastic gradient optimization is the dominant learning paradigm for a variety of scenarios, from classical supervised learning to modern self-supervised learning. We consider stochastic gradient algorithms for learning problems whose objectives rely on unknown nuisance parameters, and establish non-asymptotic convergence guarantees. Our results show that, while the presence of a nuisance can alter the optimum and upset the optimization trajectory, the classical stochastic gradient algorithm …

Revisit Stochastic Gradient Descent for Strongly Convex Objectives: Tight Uniform-in-Time Bounds
Kang Chen, Yasong Feng, Tianyu Wang
https://arxiv.org/abs/2508.20823 https://

Revisit Stochastic Gradient Descent for Strongly Convex Objectives: Tight Uniform-in-Time Bounds
Stochastic optimization for strongly convex objectives is a fundamental problem in statistics and optimization. This paper revisits the standard Stochastic Gradient Descent (SGD) algorithm for strongly convex objectives and establishes tight uniform-in-time convergence bounds. We prove that with probability larger than $1 - β$, a $\frac{\log \log k + \log (1/β)}{k}$ convergence bound simultaneously holds for all $k \in \mathbb{N}_+$, and show that this rate is tight up to constants. Our resul…

Introducing the Quantum Economic Advantage Online Calculator
Frederick Mejia, Hans Gundlach, Jayson Lynch, Carl Dukatz, Andrew Lucas, Eleanor Crane, Prashant Shukla, Neil Thompson
https://arxiv.org/abs/2508.21031

Introducing the Quantum Economic Advantage Online Calculator
Developing a systematic view of where quantum computers will outperform classical ones is important for researchers, policy makers and business leaders. But developing such a view is challenging because quantum advantage analyses depend not only on algorithm properties, but also on a host of technical characteristics (error correction, gate speeds, etc.). Because various analyses make different assumptions about these technical characteristics, it can be challenging to make comparisons across t…

Efficiently Coloring the Intersection of a General Matroid and Partition Matroids
Stephen Arndt, Benjamin Moseley, Kirk Pruhs, Michael Zlatin
https://arxiv.org/abs/2508.19473 ht…

Efficiently Coloring the Intersection of a General Matroid and Partition Matroids
This paper shows a polynomial-time algorithm, that given a general matroid $M_1 = (X, \mathcal{I}_1)$ and $k-1$ partition matroids $ M_2, \ldots, M_k$, produces a coloring of the intersection $M = \cap_{i=1}^k M_i$ using at most $1+\sum_{i=1}^k \left(χ(M_i) -1\right)$ colors. This is the first polynomial-time $O(1)$-approximation algorithm for matroid intersection coloring where one of the matroids may be a general matroid. Leveraging the fact that all of the standard combinatorial matroids re…

Reducing Spatial and Temporal Dimensionality in the Multidimensional Caldeira-Leggett Model
Hongfei Zhan, Ernest W. Z. Pan, Zhenning Cai
https://arxiv.org/abs/2509.24525 https:/…

Reducing Spatial and Temporal Dimensionality in the Multidimensional Caldeira-Leggett Model
Focusing on the real-time dynamics of the reduced density matrix of the multidimensional Caldeira-Leggett model, several techniques are adopted in this paper to reduce the spatial and temporal dimensionality, combined into an efficient algorithm. From a spatial perspective, an equivalent formulation of the Dyson series is presented. With the aid of a low-rank approximation, the spatial dimensionality of open quantum system simulations is halved. From a temporal perspective, the frozen Gaussian …

An Efficient Data-Driven Framework for Linear Quadratic Output Feedback Control
Jun Xie, Yuan-hua Ni, Yiqin Yang, Feng Duan, Bo Xu
https://arxiv.org/abs/2508.20748 https://

An Efficient Data-Driven Framework for Linear Quadratic Output Feedback Control
Linear quadratic regulator with unmeasurable states and unknown system matrix parameters better aligns with practical scenarios. However, for this problem, balancing the optimality of the resulting controller and the leniency of the algorithm's feasibility conditions remains a non-trivial challenge, as no well-established general method has yet been developed to address this trade-off. To address this gap, this study first develops a comprehensive theoretical framework for state parameterizatio…

DANCeRS: A Distributed Algorithm for Negotiating Consensus in Robot Swarms with Gaussian Belief Propagation
Aalok Patwardhan, Andrew J. Davison
https://arxiv.org/abs/2508.18153 …

DANCeRS: A Distributed Algorithm for Negotiating Consensus in Robot Swarms with Gaussian Belief Propagation
Robot swarms require cohesive collective behaviour to address diverse challenges, including shape formation and decision-making. Existing approaches often treat consensus in discrete and continuous decision spaces as distinct problems. We present DANCeRS, a unified, distributed algorithm leveraging Gaussian Belief Propagation (GBP) to achieve consensus in both domains. By representing a swarm as a factor graph our method ensures scalability and robustness in dynamic environments, relying on pur…

Spectral Gaps with Quantum Counting Queries and Oblivious State Preparation
Almudena Carrera Vazquez, Aleksandros Sobczyk
https://arxiv.org/abs/2508.21002 https://

Spectral Gaps with Quantum Counting Queries and Oblivious State Preparation
Approximating the $k$-th spectral gap $Δ_k=|λ_k-λ_{k+1}|$ and the corresponding midpoint $μ_k=\frac{λ_k+λ_{k+1}}{2}$ of an $N\times N$ Hermitian matrix with eigenvalues $λ_1\geqλ_2\geq\ldots\geqλ_N$, is an important special case of the eigenproblem with numerous applications in science and engineering. In this work, we present a quantum algorithm which approximates these values up to additive error $εΔ_k$ using a logarithmic number of qubits. Notably, in the QRAM model, its…

Approximating value functions via corner Benders' cuts
Matheus Jun Ota, Ricardo Fukasawa, Aleksandr M. Kazachkov
https://arxiv.org/abs/2509.21758 https://

Approximating value functions via corner Benders' cuts
We introduce a novel technique to generate Benders' cuts from a conic relaxation ("corner") derived from a basis of a higher-dimensional polyhedron that we aim to outer approximate in a lower-dimensional space. To generate facet-defining inequalities for the epigraph associated to this corner, we develop a computationally-efficient algorithm based on a compact reverse polar formulation and a row generation scheme that handles the redundant inequalities. Via a known connection between arc-flow a…

Numerical Integration of stochastic differential equations: The Heun Algorithm Revisited and It\^o-Stratonovich Calculus
Riccardo Mannella
https://arxiv.org/abs/2508.19040 https…

Numerical Integration of stochastic differential equations: The Heun Algorithm Revisited and Itô-Stratonovich Calculus
The widely used Heun algorithm for the numerical integration of stochastic differential equations (SDEs) is critically re-examined. We discuss and evaluate several alternative implementations, motivated by the fact that the standard Heun scheme is constructed from a low-order integrator. The convergence, stability, and equilibrium properties of these alternatives are assessed through extensive numerical simulations. Our results confirm that the standard Heun scheme remains a benchmark integrati…

Automated Discovery of Gadgets in Quantum Circuits for Efficient Reinforcement Learning
Oleg M. Yevtushenko, Florian Marquardt
https://arxiv.org/abs/2509.24666 https://

Automated Discovery of Gadgets in Quantum Circuits for Efficient Reinforcement Learning
Reinforcement learning (RL) has proven itself as a powerful tool for the discovery of quantum circuits and quantum protocols. We have recently shown that including composite quantum gates -- referred to as ``gadgets'' -- in the action space of RL agents substantially enhances the RL performance in the context of quantum error correction. However, up to now the gadgets themselves had to be constructed manually. In this paper, we suggest an algorithm for the automated discovery of new gadgets and…

A regret minimization approach to fixed-point iterations
Joon Kwon
https://arxiv.org/abs/2509.21653 https://arxiv.org/pdf/2509.21653

A regret minimization approach to fixed-point iterations
We propose a conversion scheme that turns regret minimizing algorithms into fixed point iterations, with convergence guarantees following from regret bounds. The resulting iterations can be seen as a grand extension of the classical Krasnoselskii--Mann iterations, as the latter are recovered by converting the Online Gradient Descent algorithm. This approach yields new simple iterations for finding fixed points of non-self operators. We also focus on converting algorithms from the AdaGrad family…

Fast numerical derivatives based on multi-interval Fourier extension
Zhenyu Zhao, Yanfei Wang, Xinran Liu
https://arxiv.org/abs/2508.20876 https://arxiv.or…

Fast numerical derivatives based on multi-interval Fourier extension
We present a computationally efficient algorithm for stable numerical differentiation from noisy, uniformly-sampled data on a bounded interval. The method combines multi-interval Fourier extension approximations with an adaptive domain partitioning strategy: a global precomputation of local Fourier sampling matrices and their thin SVDs is reused throughout a recursive bisection procedure that selects locally-resolved Fourier fits. Each accepted subinterval stores a compact set of Fourier coeffi…

A note on the Davis-Yin three-operator splitting method
Maoran Wang, Xingju Cai
https://arxiv.org/abs/2509.22032 https://arxiv.org/pdf/2509.22032

A note on the Davis-Yin three-operator splitting method
This paper addresses the monotone inclusion problem in a real Hilbert space, with the objective of identifying a point \( x \in \mathcal{H} \) such that \( 0 \in Ax + Bx + Cx \), where \( A \) and \(C\) are maximal monotone operators and \( B \) is a \(β\)-cocoercive operator. The Davis-Yin three-operator splitting method constitutes a widely used algorithm for solving this problem. This method consists of two subproblems: the first entails a backward step, whereas the second comprises a backw…

Tikhonov regularized exterior penalty methods for hierarchical variational inequalities
Meggie Marschner, Mathias Staudigl
https://arxiv.org/abs/2508.20872 https://

Tikhonov regularized exterior penalty methods for hierarchical variational inequalities
We consider nested variational inequalities con- sisting in a (upper-level) variational inequality whose feasible set is given by the solution set of another (lower-level) variational inequality. This class of hierarchical equilibrium contains a wealth of important applications, including purely hierarchical convex bilevel optimization problems and certain multi-follower games. Working within a real Hilbert space setting, we develop a double loop prox-penalization algorithm with strong conver- …