Tootfinder

A Hierarchical Constructive Heuristic for Large-Scale Survivable Traffic Grooming Problem under Double-Link Failures
Silong Zhang, Jixuan Feng, Junyan Liu, Yu Liu, Zhou Xu, Fan Zhang
https://arxiv.org/abs/2506.13074

A Hierarchical Constructive Heuristic for Large-Scale Survivable Traffic Grooming Problem under Double-Link Failures
This paper studies a survivable traffic grooming problem in large-scale optical transport networks under double-link failures (STG2). Each communication demand must be assigned a route for every possible scenario involving zero, one, or two failed fiber links. Protection against double-link failures is crucial for ensuring reliable telecommunications services while minimizing equipment costs, making it essential for telecommunications companies today. However, this significantly complicates the…

Convergence of generalized cross-validation with applications to ill-posed integral equations
Tim Jahn, Mikhail Kirilin
https://arxiv.org/abs/2506.14558 ht…

Convergence of generalized cross-validation with applications to ill-posed integral equations
In this article, we rigorously establish the consistency of generalized cross-validation as a parameter-choice rule for solving inverse problems. We prove that the index chosen by leave-one-out GCV achieves a non-asymptotic, order-optimal error bound with high probability for polynomially ill-posed compact operators. Hereby it is remarkable that the unknown true solution need not satisfy a self-similarity condition, which is generally needed for other heuristic parameter choice rules. We quanti…

Practical colinear chaining on sequences revisited
Nicola Rizzo, Manuel C\'aceres, Veli M\"akinen
https://arxiv.org/abs/2506.11750 https://…

Practical colinear chaining on sequences revisited
Colinear chaining is a classical heuristic for sequence alignment and is widely used in modern practical aligners. Jain et al. (J. Comput. Biol. 2022) proposed an $O(n \log^3 n)$ time algorithm to chain a set of $n$ anchors so that the chaining cost matches the edit distance of the input sequences, when anchors are maximal exact matches. Moreover, assuming a uniform and sparse distribution of anchors, they provided a practical solution ($\mathtt{ChainX}$) working in $O(n \cdot \mathsf{SOL} + n …

A Hybrid Heuristic Framework for Resource-Efficient Querying of Scientific Experiments Data
Mayank Patel, Minal Bhise
https://arxiv.org/abs/2506.10422 http…

A Hybrid Heuristic Framework for Resource-Efficient Querying of Scientific Experiments Data
Scientific experiments and modern applications are generating large amounts of data every day. Most organizations utilize In-house servers or Cloud resources to manage application data and workload. The traditional database management system (DBMS) and HTAP systems spend significant time & resources to load the entire dataset into DBMS before starting query execution. On the other hand, in-situ engines may reparse required data multiple times, increasing resource utilization and data processing…

Design of A* based heuristic algorithm for efficient interdiction in multi-Layer networks
Sukanya Samanta
https://arxiv.org/abs/2506.10017 https://

Design of A* based heuristic algorithm for efficient interdiction in multi-Layer networks
Intercepting a criminal using limited police resources presents a significant challenge in dynamic crime environments, where the criminal's location continuously changes over time. The complexity is further heightened by the vastness of the transportation network. To tackle this problem, we propose a layered graph representation, in which each time step is associated with a duplicate of the transportation network. For any given set of attacker strategies, a near-optimal defender strategy is com…

Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc Teamwork
Jianhong Wang, Yang Li, Samuel Kaski, Jonathan Lawry
https://arxiv.org/abs/2506.11285

Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc Teamwork
Open multi-agent systems are increasingly important in modeling real-world applications, such as smart grids, swarm robotics, etc. In this paper, we aim to investigate a recently proposed problem for open multi-agent systems, referred to as n-agent ad hoc teamwork (NAHT), where only a number of agents are controlled. Existing methods tend to be based on heuristic design and consequently lack theoretical rigor and ambiguous credit assignment among agents. To address these limitations, we model a…

HyColor: An Efficient Heuristic Algorithm for Graph Coloring
Enqiang Zhu, Yu Zhang, Haopeng Sun, Ziqi Wei, Witold Pedrycz, Chanjuan Liu, Jin Xu
https://arxiv.org/abs/2506.07373

HyColor: An Efficient Heuristic Algorithm for Graph Coloring
The graph coloring problem (GCP) is a classic combinatorial optimization problem that aims to find the minimum number of colors assigned to vertices of a graph such that no two adjacent vertices receive the same color. GCP has been extensively studied by researchers from various fields, including mathematics, computer science, and biological science. Due to the NP-hard nature, many heuristic algorithms have been proposed to solve GCP. However, existing GCP algorithms focus on either small hard …

A Heuristic Study of Temperature: Quantum Circuitry in Thermal Systems
HongZheng Liu, YiNuo Tian, Zhiyue Wu
https://arxiv.org/abs/2506.06994 https://

A Heuristic Study of Temperature: Quantum Circuitry in Thermal Systems
Classical thermodynamic singularities at phase transitions and in negative-temperature regimes arise because the equal-a-priori probability postulate overlooks the quantum dynamical complexity required to prepare microstates. We promote bounded dynamical complexity to a postulate and develop Complexity Windowed Thermodynamics (CWT). Introducing a finite complexity budget yields the windowed entropy, an entropy of ignorance that is smooth in energy and monotonically non-increasing in the budget.…

LLM-Driven Instance-Specific Heuristic Generation and Selection
Shaofeng Zhang, Shengcai Liu, Ning Lu, Jiahao Wu, Ji Liu, Ke Tang
https://arxiv.org/abs/2506.00490

LLM-Driven Instance-Specific Heuristic Generation and Selection
Combinatorial optimization problems are widely encountered in real-world applications. Designing high-quality heuristic algorithms that efficiently approximate optimal solutions within reasonable time is a critical research challenge. In recent years, many works have explored integrating Large Language Models (LLMs) with Evolutionary Algorithms to automate heuristic algorithm design through prompt engineering. However, these approaches generally adopt a problem-specific paradigm, applying a sin…

Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation
Mir Mohammad Yousuf, Shabir Ahmad Sofi
https://arxiv.org/abs/2506.10397 h…

Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation
Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for classifying issues in quantum software repositories by bug type, category, severity, and impacted quality attributes, with additional focus on quantum-specific bug types. The framework applies keyword and heuristic-based techniques tailored to quantum computing. To assess its reliability, we manually classified a stratified sample of 4,984 issues from a …

This https://arxiv.org/abs/2504.21131 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

A Formalism for Optimal Search with Dynamic Heuristics (Extended Version)
While most heuristics studied in heuristic search depend only on the state, some accumulate information during search and thus also depend on the search history. Various existing approaches use such dynamic heuristics in $\mathrm{A}^*$-like algorithms and appeal to classic results for $\mathrm{A}^*$ to show optimality. However, doing so ignores the complexities of searching with a mutable heuristic. In this paper we formalize the idea of dynamic heuristics and use them in a generic algorithm fr…

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
Shuai Li, Azarakhsh Keipour, Sicong Zhao, Srinath Rajagopalan, Charles Swan, Kostas E. Bekris
https://arxiv.org/abs/2506.09765

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction
Warehouse automation plays a pivotal role in enhancing operational efficiency, minimizing costs, and improving resilience to workforce variability. While prior research has demonstrated the potential of machine learning (ML) models to increase picking success rates in large-scale robotic fleets by prioritizing high-probability picks and packages, these efforts primarily focused on predicting success probabilities for picks sampled using heuristic methods. Limited attention has been given, howev…

On the integrality Gap of Small Asymmetric Traveling Salesman Problems: A Polyhedral and Computational Approach
Eleonora Vercesi, Janos Barta, Luca Maria Gambardella, Stefano Gualandi, Monaldo Mastrolilli
https://arxiv.org/abs/2506.10671

On the integrality Gap of Small Asymmetric Traveling Salesman Problems: A Polyhedral and Computational Approach
In this paper, we investigate the integrality gap of the Asymmetric Traveling Salesman Problem (ATSP) with respect to the linear relaxation given by the Asymmetric Subtour Elimination Problem (ASEP) for instances with $n$ nodes, where $n$ is small. In particular, we focus on the geometric properties and symmetries of the ASEP polytope ($P^{n}_{ASEP}$) and its vertices. The polytope's symmetries are exploited to design a heuristic pivoting algorithm to search vertices where the integrality gap i…

Meta-heuristic Hypergraph-Assisted Robustness Optimization for Higher-order Complex Systems
Xilong Qu, Wenbin Pei, Haifang Li, Qiang Zhang, Bing Xue, Mengjie Zhang, del Genio Charo, Stefano Boccaletti
https://arxiv.org/abs/2505.24123

Meta-heuristic Hypergraph-Assisted Robustness Optimization for Higher-order Complex Systems
In complex systems (e.g., communication, transportation, and biological networks), high robustness ensures sustained functionality and stability even when resisting attacks. However, the inherent structure complexity and the unpredictability of attacks make robustness optimization challenging. Hypergraphs provide a framework for modeling complicated higher-order interactions in complex systems naturally, but their potential has not been systematically investigated. Therefore, we propose an effe…

This https://arxiv.org/abs/2506.00490 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

LLM-Driven Instance-Specific Heuristic Generation and Selection
Combinatorial optimization problems are widely encountered in real-world applications. Designing high-quality heuristic algorithms that efficiently approximate optimal solutions within reasonable time is a critical research challenge. In recent years, many works have explored integrating Large Language Models (LLMs) with Evolutionary Algorithms to automate heuristic algorithm design through prompt engineering. However, these approaches generally adopt a problem-specific paradigm, applying a sin…

ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
Yuki Imajuku, Kohki Horie, Yoichi Iwata, Kensho Aoki, Naohiro Takahashi, Takuya Akiba
https://arxiv.org/abs/2506.09050

ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
How well do AI systems perform in algorithm engineering for hard optimization problems in domains such as package-delivery routing, crew scheduling, factory production planning, and power-grid balancing? We introduce ALE-Bench, a new benchmark for evaluating AI systems on score-based algorithmic programming contests. Drawing on real tasks from the AtCoder Heuristic Contests, ALE-Bench presents optimization problems that are computationally hard and admit no known exact solution. Unlike short-du…

A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
Beomjoon Lee, Changjoo Nam
https://arxiv.org/abs/2506.01628

A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
We address the bin packing problem (BPP), which aims to maximize bin utilization when packing a variety of items. The offline problem, where the complete information about the item set and their sizes is known in advance, is proven to be NP-hard. The semi-online and online variants are even more challenging, as full information about incoming items is unavailable. While existing methods have tackled both 2D and 3D BPPs, the 2D BPP remains underexplored in terms of fully maximizing utilization. …

Improved belief propagation is sufficient for real-time decoding of quantum memory
Tristan M\"uller, Thomas Alexander, Michael E. Beverland, Markus B\"uhler, Blake R. Johnson, Thilo Maurer, Drew Vandeth
https://arxiv.org/abs/2506.01779

Improved belief propagation is sufficient for real-time decoding of quantum memory
We introduce a new heuristic decoder, Relay-BP, targeting real-time quantum circuit decoding for large-scale quantum computers. Relay-BP achieves high accuracy across circuit-noise decoding problems: significantly outperforming BP+OSD+CS-10 for bivariate-bicycle codes and comparable to min-weight-matching for surface codes. As a lightweight message-passing decoder, Relay-BP is inherently parallel, enabling rapid low-footprint decoding with FPGA or ASIC real-time implementations, similar to stan…

This https://arxiv.org/abs/2303.17475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Learning distributed representations with efficient SoftMax normalization
Learning distributed representations, or embeddings, that encode the relational similarity patterns among objects is a relevant task in machine learning. A popular method to learn the embedding matrices $X, Y$ is optimizing a loss function of the term ${\rm SoftMax}(XY^T)$. The complexity required to calculate this term, however, runs quadratically with the problem size, making it a computationally heavy solution. In this article, we propose a linear-time heuristic approximation to compute the …

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
Prachi Jadhav, Hongwei Jin, Ewa Deelman, Prasanna Balaprakash
https://arxiv.org/abs/2506.02025

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fairness. Traditional methods, including heuristic-based (e.g., First-Come-First-Served) or intensive optimization techniques, often lack adaptability to dynamic workloads and heterogeneous HPC systems. To address this, we propose a novel Large Language Model (LLM)-based scheduler using a ReAct-style framework (Reason +…

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance
Luciano Ribeiro Galv\~ao, Rafael de Andrade Moral
https://arxiv.org/abs/2506.05609

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance
In this study, machine learning models were tested to predict whether or not a customer of an insurance company would purchase a travel insurance product. For this purpose, secondary data provided by an open-source website that compiles databases from statistical modeling competitions were used. The dataset used presents approximately 2,700 records from an unidentified company in the tourism insurance sector. Initially, the feature engineering stage was carried out, which were selected through …

Meta-heuristic design of a light-weight homologous backup structure of the primary reflector for the Large Submillimeter Telescope
Chihiro Imamura, Yoichi Tamura, Hiroaki Kawamura, Toshiaki Kimura, Akio Taniguchi, Mikio Kurita
#toXiv_bot_toot

This https://arxiv.org/abs/2409.14202 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Mining Causality: AI-Assisted Search for Instrumental Variables
The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying its validity -- especially exclusion restrictions -- is largely rhetorical. We propose using large language models (LLMs) to search for new IVs through narratives and counterfactual reasoning, similar to how a human researcher would. The stark difference, however, is that LLMs can dramatically accelerate this process and explore an extreme…

This https://arxiv.org/abs/2410.17704 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Fermion Masses and Mixing in String-Inspired Models
We study a class of supersymmetric Froggatt-Nielsen (FN) models with multiple U(1) symmetries and Standard Model (SM) singlets inspired by heterotic string compactifications on Calabi-Yau threefolds. The string-theoretic origin imposes a particular charge pattern on the SM fields and FN singlets, dividing the latter into perturbative and non-perturbative types. Employing systematic and heuristic search strategies, such as genetic algorithms, we identify charge assignments and singlet VEVs that …

Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
Aravinda Jatavallabha, Prabhanjan Bharadwaj, Ashish Chander
https://arxiv.org/abs/2506.00048

Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
Graph Neural Networks (GNNs) are powerful tools for recommendation systems, but they often struggle under data sparsity and noise. To address these issues, we implemented LightGCL, a graph contrastive learning model that uses Singular Value Decomposition (SVD) for robust graph augmentation, preserving semantic integrity without relying on stochastic or heuristic perturbations. LightGCL enables structural refinement and captures global collaborative signals, achieving significant gains over stat…

This https://arxiv.org/abs/2502.01907 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Robust Cislunar Low-Thrust Trajectory Optimization under Uncertainties via Sequential Covariance Steering
Spacecraft operations are influenced by uncertainties such as dynamics modeling, navigation, and maneuver execution errors. Although mission design has traditionally incorporated heuristic safety margins to mitigate the effect of uncertainties, particularly before/after crucial events, it is yet unclear whether this practice will scale in the cislunar region, which features locally chaotic nonlinear dynamics and involves frequent lunar flybys. This paper applies chance-constrained covariance st…

Pairwise similarity method for majority domination problem
N. I. Shushko, D. V. Lemtyuzhnikova
https://arxiv.org/abs/2506.08886 https://

Pairwise similarity method for majority domination problem
The paper considers the problem of finding the number of dominant voters in two-level voting procedures. At the first stage, voting is conducted among local groups of voters, and at the second stage, the results are aggregated to form a final decision. The goal is to determine the minimum proportion of voters supporting a proposal for it to be accepted. The paper uses the method of pairwise comparisons to analyze the structure of the problem and develop heuristic algorithms with guaranteed accu…

Exploiting Euclidean Distance Field Properties for Fast and Safe 3D planning with a modified Lazy Theta*
Jose A. Cobano, L. Merino, F. Caballero
https://arxiv.org/abs/2505.24024

Exploiting Euclidean Distance Field Properties for Fast and Safe 3D planning with a modified Lazy Theta*
Graph search planners have been widely used for 3D path planning in the literature, and Euclidean Distance Fields (EDFs) are increasingly being used as a representation of the environment. However, to the best of our knowledge, the integration of EDFs into heuristic planning has been carried out in a loosely coupled fashion, dismissing EDF properties that can be used to accelerate/improve the planning process and enhance the safety margins of the resultant trajectories. This paper presents a fa…

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Kuo Yang, Xingjie Yang, Linhui Yu, Qing Xu, Yan Fang, Xu Wang, Zhengyang Zhou, Yang Wang
https://arxiv.org/abs/2506.08507

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Large Language Model (LLM)-driven Multi-agent systems (Mas) have recently emerged as a powerful paradigm for tackling complex real-world tasks. However, existing Mas construction methods typically rely on manually crafted interaction mechanisms or heuristic rules, introducing human biases and constraining the autonomous ability. Even with recent advances in adaptive Mas construction, existing systems largely remain within the paradigm of semi-autonomous patterns. In this work, we propose MasHos…

Bayesian inference of neutron star crust properties using an ab initio-benchmarked meta-model
S. Burrello, F. Gulminelli, M. Antonelli, M. Colonna, A. Fantina
https://arxiv.org/abs/2506.05603

Bayesian inference of neutron star crust properties using an ab initio-benchmarked meta-model
Accurate modeling of the neutron star crust is essential for interpreting multimessenger observations and constraining the nuclear equation of state (EoS). However, standard phenomenological EoS models often rely on heuristic extrapolations in the low-density regime, which are inconsistent with microscopic predictions. In this work, we refine a unified meta-modeling framework for the EoS by incorporating low-density corrections based on energy density functionals constrained by ab initio neutro…

VISCA: Inferring Component Abstractions for Automated End-to-End Testing
Parsa Alian, Martin Tang, Ali Mesbah
https://arxiv.org/abs/2506.04161 https://

VISCA: Inferring Component Abstractions for Automated End-to-End Testing
Providing optimal contextual input presents a significant challenge for automated end-to-end (E2E) test generation using large language models (LLMs), a limitation that current approaches inadequately address. This paper introduces Visual-Semantic Component Abstractor (VISCA), a novel method that transforms webpages into a hierarchical, semantically rich component abstraction. VISCA starts by partitioning webpages into candidate segments utilizing a novel heuristic-based segmentation method. Th…

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Shuohao Ping, Naren Sathishkumar, Wan-Hsuan Lin, Hanyu Wang, Jason Cong
https://arxiv.org/abs/2505.24169

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Quantum Layout Synthesis (QLS) is a critical compilation stage that adapts quantum circuits to hardware constraints with an objective of minimizing the SWAP overhead. While heuristic tools demonstrate good efficiency, they often produce suboptimal solutions, and exact methods suffer from limited scalability. In this work, we propose ML-SABRE, a high-performance multilevel framework for QLS that improves both solution quality and compilation time through a hierarchical optimization approach. We …

The symbolic partition with generalized Koopman analysis
Haipeng Li, Pengfei Guo, Yueheng Lan
https://arxiv.org/abs/2506.06973 https://

The symbolic partition with generalized Koopman analysis
Symbolic dynamics serves as a crucial tool in the study of chaotic systems, prompting extensive research into various methods for symbolic partitioning. The limitations of these methods are heuristic and empirical for the partition the multivariate chaotic state space. Notably, the use of operator theory in partitioning the multivariable chaotic series into precise symbolic cells has been underexplored. In this paper, we propose a novel symbolic partition method, referred to as Koopman Analysis…

This https://arxiv.org/abs/2505.18377 has been replaced.
initial toot: https://mastoxiv.page/@ar…

SP2RINT: Spatially-Decoupled Physics-Inspired Progressive Inverse Optimization for Scalable, PDE-Constrained Meta-Optical Neural Network Training
DONNs harness the physics of light propagation for efficient analog computation, with applications in AI and signal processing. Advances in nanophotonic fabrication and metasurface-based wavefront engineering have opened new pathways to realize high-capacity DONNs across various spectral regimes. Training such DONN systems to determine the metasurface structures remains challenging. Heuristic methods are fast but oversimplify metasurfaces modulation, often resulting in physically unrealizable d…

This https://arxiv.org/abs/2506.02576 has been replaced.
link: https://scholar.google.com/scholar?q=a

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively …

Approximating the Particle Distribution in Rotating and Tandem Mirror Traps
Greta X. Li, Elijah J. Kolmes, Ian E. Ochs, Nathaniel J. Fisch
https://arxiv.org/abs/2505.19483

Approximating the Particle Distribution in Rotating and Tandem Mirror Traps
Steady state distribution functions can be used to calculate stability conditions for modes, radiation energy losses, and particle loss rates. Heuristic analytic approximations to these distributions can capture key behaviors of the true distributions while possessing computational advantages over their numerical counterparts. In this paper, we motivate and present a closed-form analytic model for a distribution of particles in a centrifugal or tandem mirror. We find that our model outperforms …

An Improved Grey Wolf Optimizer Inspired by Advanced Cooperative Predation for UAV Shortest Path Planning
Zuhao Teng, Qian Dong, Ze Zhang, Shuangyao Huang, Wenzhang Zhang, Jingchen Wang, Ji Li, Xi Chen
https://arxiv.org/abs/2506.03663

An Improved Grey Wolf Optimizer Inspired by Advanced Cooperative Predation for UAV Shortest Path Planning
With the widespread application of Unmanned Aerial Vehicles (UAVs) in domains like military reconnaissance, emergency rescue, and logistics delivery, efficiently planning the shortest flight path has become a critical challenge. Traditional heuristic-based methods often suffer from the inability to escape from local optima, which limits their effectiveness in finding the shortest path. To address these issues, a novel Improved Grey Wolf Optimizer (IGWO) is presented in this study. The proposed …

Greedy recursion parameter selection for the One-Way Navier-Stokes (OWNS) equations
Michael K. Sleeman, Tim Colonius
https://arxiv.org/abs/2506.02320 https…

Greedy recursion parameter selection for the One-Way Navier-Stokes (OWNS) equations
The One-Way Navier-Stokes (OWNS) equations use recursive filtering to construct efficient, well-posed one-way approximations to linear hyperbolic systems. The recursion parameters are critical to the accuracy and stability of the method, and have previously been chosen based on heuristic estimates of key eigenvalues (or their branches), which converges slowly and requires trial-and-error tuning for new systems. We review the projection and recursive OWNS formulations (OWNS-P and OWNS-R) for inh…

This https://arxiv.org/abs/2403.18213 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Long-Term Open-Pit Mine Planning with Large Neighbourhood Search
We present a Large Neighbourhood Search based approach for solving complex long-term open-pit mine planning problems. An initial feasible solution, generated by a sliding windows heuristic, is improved through repeated solves of a restricted mixed-integer program. Each iteration leaves only a subset of the variables in the planning model free to take on new values. We form these subsets through the use of neighbourhood formation strategies that exploit model structure. We show that our approach…

Adaptive tuning of Hamiltonian Monte Carlo methods
Elena Akhmatskaya, Lorenzo Nagar, Jose Antonio Carrillo, Leonardo Gavira Balmacz, Hristo Inouzhe, Mart\'in Parga Pazos, Mar\'ia Xos\'e Rodr\'iguez \'Alvarez
https://arxiv.org/abs/2506.04082

Adaptive tuning of Hamiltonian Monte Carlo methods
With the recently increased interest in probabilistic models, the efficiency of an underlying sampler becomes a crucial consideration. A Hamiltonian Monte Carlo (HMC) sampler is one popular option for models of this kind. Performance of HMC, however, strongly relies on a choice of parameters associated with an integration method for Hamiltonian equations, which up to date remains mainly heuristic or introduce time complexity. We propose a novel computationally inexpensive and flexible approach …

This https://arxiv.org/abs/2504.09546 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

A simulation-heuristics dual-process model for intuitive physics
The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary lies remains unclear. Using a pouring-marble task, our human study revealed two distinct error patterns when predicting pouring angles, differentiated by simulation time. While mental simulation accurately captured human judgments in simpler scenarios, a linear heuristic model better matched human predictions when simu…

Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
Abderrahim Bendahi, Benjamin Doerr, Adrien Fradin, Johannes F. Lutzeyer
https://arxiv.org/abs/2506.01107

Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
The move-acceptance hyper-heuristic was recently shown to be able to leave local optima with astonishing efficiency (Lissovoi et al., Artificial Intelligence (2023)). In this work, we propose two modifications to this algorithm that demonstrate impressive performances on a large class of benchmarks including the classic Cliff$_d$ and Jump$_m$ function classes. (i) Instead of randomly choosing between the only-improving and any-move acceptance operator, we take this choice via a simple two-state…

Quantum Field Theory Between Rigor and Pragmatism
Johannes Branahl
https://arxiv.org/abs/2505.15577 https://arxiv.org/pdf/2505.15577

Quantum Field Theory Between Rigor and Pragmatism
Quantum Field Theory (QFT), the foundational framework of particle physics, has long existed in a state of tension between empirical success and mathematical rigor. Conventional QFT (CQFT), which underpins the Standard Model, offers unparalleled predictive accuracy but relies on inconsistent and ad hoc methods. In contrast, axiomatic QFT (AxQFT) aspires to a consistent, mathematically rigorous foundation, yet lacks empirical applicability. This paper introduces the heuristic vehicle of a realis…

Optimization of Robotic Liquid Handling as a Capacitated Vehicle Routing Problem
Guangqi Wu, Runzhong Wang, Connor W. Coley
https://arxiv.org/abs/2506.02795

Optimization of Robotic Liquid Handling as a Capacitated Vehicle Routing Problem
We present an optimization strategy to reduce the execution time of liquid handling operations in the context of an automated chemical laboratory. By formulating the task as a capacitated vehicle routing problem (CVRP), we leverage heuristic solvers traditionally used in logistics and transportation planning to optimize task execution times. As exemplified using an 8-channel pipette with individually controllable tips, our approach demonstrates robust optimization performance across different l…

Exact operator inference with minimal data
Henrik Rosenberger, Benjamin Sanderse, Giovanni Stabile
https://arxiv.org/abs/2506.01244 https://

Exact operator inference with minimal data
This work introduces a novel method to generate snapshot data for operator inference that guarantees the exact reconstruction of intrusive projection-based reduced-order models (ROMs). To ensure exact reconstruction, the operator inference least squares matrix must have full rank, without regularization. Existing works have achieved this full rank using heuristic strategies to generate snapshot data and a-posteriori checks on full rank, but without a guarantee of success. Our novel snapshot dat…

Modelling Nonstationary Time Series using Trend-Stationary Hypothesis
Zhandos Abdikhadir
https://arxiv.org/abs/2506.07987 https://arx…

Modelling Nonstationary Time Series using Trend-Stationary Hypothesis
This paper challenges the prevalence of unit root models by introducing the Linear Trend-Stationary Trigonometric ARMA (LTSTA), a novel framework for modelling nonstationary time series under the trend-stationary hypothesis. LTSTA decomposes series into three components: (1) a deterministic trend (modelled via continuous piecewise linear functions with structural breaks), (2) a Fourier-based deterministic seasonality component, and (3) a stochastic ARMA error term. We propose a heuristic approa…

This https://arxiv.org/abs/2502.03701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

First-ish Order Methods: Hessian-aware Scalings of Gradient Descent
Gradient descent is the primary workhorse for optimizing large-scale problems in machine learning. However, its performance is highly sensitive to the choice of the learning rate. A key limitation of gradient descent is its lack of natural scaling, which often necessitates expensive line searches or heuristic tuning to determine an appropriate step size. In this paper, we address this limitation by incorporating Hessian information to scale the gradient direction. By accounting for the curvatur…