Tootfinder

Classical algorithm inspired by the feedback-based algorithm for quantum optimization and local counterdiabatic driving
Takuya Hatomura
https://arxiv.org/abs/2506.09214

Classical algorithm inspired by the feedback-based algorithm for quantum optimization and local counterdiabatic driving
We propose a quantum-inspired classical algorithm for combinatorial optimization problems, named the counterdiabaticity-assisted classical algorithm for optimization (CACAO). In this algorithm, a solution of a given combinatorial optimization problem is heuristically searched with classical spin dynamics based on quantum Lyapunov control of local counterdiabatic driving. We compare the performance of CACAO with that of quantum time-evolution algorithms, i.e., quantum annealing, the feedback-bas…

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Junliang Xing, Yifan Zhang, Jian Cheng
https://arxiv.org/abs/2506.09404

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Combinatorial optimization problems are notoriously challenging due to their discrete structure and exponentially large solution space. Recent advances in deep reinforcement learning (DRL) have enabled the learning heuristics directly from data. However, DRL methods often suffer from limited exploration and susceptibility to local optima. On the other hand, evolutionary algorithms such as Genetic Algorithms (GAs) exhibit strong global exploration capabilities but are typically sample inefficien…

Superlinear Drift in Consensus-Based Optimization with Condensation Phenomena
Jonathan Franceschi, Lorenzo Pareschi, Mattia Zanella
https://arxiv.org/abs/2506.09001

Superlinear Drift in Consensus-Based Optimization with Condensation Phenomena
Consensus-based optimization (CBO) is a class of metaheuristic algorithms designed for global optimization problems. In the many-particle limit, classical CBO dynamics can be rigorously connected to mean-field equations that ensure convergence toward global minimizers under suitable conditions. In this work, we draw inspiration from recent extensions of the Kaniadakis--Quarati model for indistinguishable bosons to develop a novel CBO method governed by a system of SDEs with superlinear drift an…

Large Language Models for Design Structure Matrix Optimization
Shuo Jiang, Min Xie, Jianxi Luo
https://arxiv.org/abs/2506.09749 https://

Large Language Models for Design Structure Matrix Optimization
In complex engineering systems, the interdependencies among components or development activities are often modeled and analyzed using Design Structure Matrix (DSM). Reorganizing elements within a DSM to minimize feedback loops and enhance modularity or process efficiency constitutes a challenging combinatorial optimization (CO) problem in engineering design and operations. As problem sizes increase and dependency networks become more intricate, traditional optimization methods that solely use m…

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
Hiromi Ishii (Jij, Inc), Taro Shimizu (Jij, Inc), Toshiki Teramura (Jij, Inc)
https://arxiv.org/abs/2506.06495

Optimizing Optimizations: Case Study on Detecting Specific Types of Mathematical Optimization Constraints with E-Graphs in JijModeling
In solving mathematical optimization problems efficiently, it is crucial to make use of information about specific types of constraints, such as the one-hot or Special-Ordered Set (SOS) constraints. In many cases, exploiting such information gives asymptotically better execution time. JijModeling, an industrial-strength mathematical optimization modeller, achieves this by separating the symbolic representation of an optimization problem from the input data. In this paper, we will report a real-…

This https://arxiv.org/abs/2504.14730 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Optimizing Noise Distributions for Differential Privacy
We propose a unified optimization framework for designing continuous and discrete noise distributions that ensure differential privacy (DP) by minimizing Rényi DP, a variant of DP, under a cost constraint. Rényi DP has the advantage that by considering different values of the Rényi parameter $α$, we can tailor our optimization for any number of compositions. To solve the optimization problem, we reduce it to a finite-dimensional convex formulation and perform preconditioned gradient descent…

Joint Routing and Control Optimization in VANET
Chen Huang, Dingxuan Wang, Ronghui Hou
https://arxiv.org/abs/2506.08038 https://arxiv…

Joint Routing and Control Optimization in VANET
In this paper, we introduce DynaRoute, an adaptive joint optimization framework for dynamic vehicular networks that simultaneously addresses platoon control and data transmission through trajectory-aware routing and safety-constrained vehicle coordination. DynaRoute guarantees continuous vehicle movement via platoon safety control with optimizing transmission paths through real-time trajectory prediction and ensuring reliable data. Our solution achieves three key objectives: (1) maintaining pla…

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning
Songze Li, Mingxuan Zhang, Oubo Ma, Kang Wei, Shouling Ji
https://arxiv.org/abs/2506.09562

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning
Deep reinforcement learning (DRL) has achieved remarkable success in a wide range of sequential decision-making domains, including robotics, healthcare, smart grids, and finance. Recent research demonstrates that attackers can efficiently exploit system vulnerabilities during the training phase to execute backdoor attacks, producing malicious actions when specific trigger patterns are present in the state observations. However, most existing backdoor attacks rely primarily on simplistic and heu…

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…

TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
Zengjue Chen, Runliang Niu, He Kong, Qi Wang
https://arxiv.org/abs/2506.08440

TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
Recent advances in Vision-Language-Action (VLA) model have demonstrated strong generalization capabilities across diverse scenes, tasks, and robotic platforms when pretrained at large-scale datasets. However, these models still require task-specific fine-tuning in novel environments, a process that relies almost exclusively on supervised fine-tuning (SFT) using static trajectory datasets. Such approaches neither allow robot to interact with environment nor do they leverage feedback from live ex…

Efficient Fireworks Algorithm Equipped with an Explosion Mechanism based on Student's T-distribution
Cen Shipeng, Tan Ying
https://arxiv.org/abs/2506.08484

Efficient Fireworks Algorithm Equipped with an Explosion Mechanism based on Student's T-distribution
Many real-world problems can be transformed into optimization problems, which can be classified into convex and non-convex. Although convex problems are almost completely studied in theory, many related algorithms to many non-convex problems do not work well and we need more optimization techniques. As a swarm intelligence optimization algorithm, the Fireworks Algorithm(FWA) has been widely studied and applied to many real-world scenarios, even including large language model fine-tuning. But th…

Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
Daniel H. Pak, Shubh Thaker, Kyle Baylous, Xiaoran Zhang, Danny Bluestein, James S. Duncan
https://arxiv.org/abs/2506.08280

Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine. For volumetric meshing of complex medical structures, recent studies have often utilized deep learning (DL)-based template deformation approaches to enable fast test-time generation with high spatial accuracy. However, these approaches still exhibit limitations, such as limited flexibility at high-curvature areas and unrealistic inter-part distances. In this study, we …

MOSS: Multi-Objective Optimization for Stable Rule Sets
Brian Liu, Rahul Mazumder
https://arxiv.org/abs/2506.08030 https://arxiv.org/…

MOSS: Multi-Objective Optimization for Stable Rule Sets
We present MOSS, a multi-objective optimization framework for constructing stable sets of decision rules. MOSS incorporates three important criteria for interpretability: sparsity, accuracy, and stability, into a single multi-objective optimization framework. Importantly, MOSS allows a practitioner to rapidly evaluate the trade-off between accuracy and stability in sparse rule sets in order to select an appropriate model. We develop a specialized cutting plane algorithm in our framework to rapi…

Hardware Limitations and Optimization Approach in 1-Bit RIS Design at 28 GHz
Hossein Rezaei, Mehmet Emin Arslan, George Yammine, Niels Neumann, Norman Franchi
https://arxiv.org/abs/2506.08930

Hardware Limitations and Optimization Approach in 1-Bit RIS Design at 28 GHz
Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for electromagnetic (EM) wave manipulation, offering unprecedented control over wave reflections compared to traditional metallic reflectors. By utilizing an array of tunable elements, RIS can steer and shape electromagnetic waves to enhance signal quality in wireless communication and radar systems. However, practical implementations face significant challenges due to hardware limitations and phase quantizati…

ASPO: Constraint-Aware Bayesian Optimization for FPGA-based Soft Processors
Haoran Wu, Ce Guo, Wayne Luk, Robert Mullins
https://arxiv.org/abs/2506.06817 h…

ASPO: Constraint-Aware Bayesian Optimization for FPGA-based Soft Processors
Bayesian Optimization (BO) has shown promise in tuning processor design parameters. However, standard BO does not support constraints involving categorical parameters such as types of branch predictors and division circuits. In addition, optimization time of BO grows with processor complexity, which becomes increasingly significant especially for FPGA-based soft processors. This paper introduces ASPO, an approach that leverages disjunctive form to enable BO to handle constraints involving categ…

Linear programs help to find optimal solutions based on a set of constrains. I used {ompr} before, but the new package {tidyLP} looks promising and integrates with the tidyverse. #rstats #linearprograms #optimization

GitHub - colin-fraser/tidyLP: Tidy Linear Programs
Tidy Linear Programs. Contribute to colin-fraser/tidyLP development by creating an account on GitHub.

This https://arxiv.org/abs/2501.02161 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Improved adjoint lattice Boltzmann method for topology optimization of laminar convective heat transfer
Solving flow-related inverse problems such as topology optimization problems is intricate but significant in various engineering fields. The lattice Boltzmann method (LBM) and the related adjoint method are highly suitable to perform sensitivity analysis in flow-related inverse problems thanks to their strong capability to handle complex structures and excellent parallel scalability. However, the current continuous adjoint LBM shows theoretical inconsistency and poor numerical stability for ope…

Optimization and validation of charge transport simulation for hybrid pixel detectors incorporating the repulsion effect
X. Xie, A. Bergamaschi, M. Br\"uckner, M. Carulla, R. Dinapoli, S. Ebner, K. Ferjaoui, A. Francesca Mazzoleni, J. Franklin Mulvey, V. Gautam, D. Greiffenberg, S. Hasanaj, J. Heymes, V. Hinger, V. Kedych, T. King, S. Li, C. Lopez-Cuenca, D. Mezza, K. Moustakas, A. Mozzanica, M. M\"uller, K. A. Paton, C. Ruder, B. Schmitt, P. Sieberer, S. Silletta, D. Thattil…

Optimization and validation of charge transport simulation for hybrid pixel detectors incorporating the repulsion effect
For emerging applications of hybrid pixel detectors which require high spatial resolution, e.g., subpixel interpolation in X-ray imaging and deep learning-based electron localization, accurate modeling of charge transport processes in the sensor is highly demanded. To address this, two open-source, time-stepping Monte Carlo simulation methods have been developed, both explicitly incorporating charge repulsion, which are found necessary for accurate simulation when charge sharing becomes importa…

pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization
Mrinmay Sen, Chalavadi Krishna Mohan
https://arxiv.org/abs/2506.07159

pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization
Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models tailored to their individual objectives, addressing the challenge of model generalization in traditional Federated Learning (FL) due to high data heterogeneity. However, existing PFL methods often require increased communication rounds to achieve the desired performance, primarily due to slow training caused by the use of first-order optimization, which has linear convergence. Additionally, many o…

This https://arxiv.org/abs/2409.06291 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Optimization of Embedded Element Patterns of Reactively Loaded Antenna Arrays
This paper introduces a framework for synthesizing reactively loaded antennas and antenna arrays. The framework comprises two main components: computing the fundamental bound using the semi-definite relaxation and finding a realizable solution via optimization on a Riemannian manifold. The embedded element patterns are subject to the optimization with two distinct goals under study: focusing the radiation in a single direction or synthesizing patterns with desired shapes. The reactive terminati…

This https://arxiv.org/abs/2506.05317 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ProJo4D: Progressive Joint Optimization for Sparse-View Inverse Physics Estimation
Neural rendering has made significant strides in 3D reconstruction and novel view synthesis. With the integration with physics, it opens up new applications. The inverse problem of estimating physics from visual data, however, still remains challenging, limiting its effectiveness for applications like physically accurate digital twin creation in robotics and XR. Existing methods that incorporate physics into neural rendering frameworks typically require dense multi-view videos as input, making …

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Yuhui Chen, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao Lu, Yanqing Ma, Shiyin Lu, Qifeng Chen
https://arxiv.org/abs/2506.09373

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful intermediary. Despite the predominance of Supervised Fine-Tuning (SFT) methods in current GUI agents for achieving spatial localization, these methods face substantial challenges due to their limited capacity to accurately perceive positional data. Existing strategies, such as reinforcement learning, often fail to assess positional accuracy effectively, …

Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
Mengke Li, Matthew Mumpower, Nicole Vassh, William Samuel Porter, Rebecca Surman
https://arxiv.org/abs/2506.06464

Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (r-process), which in principle could inform ML models. Here we introduce a multi-objective optimization approach using the Pareto Front algorithm. We show that this technique, capable of identifying models which…

This https://arxiv.org/abs/2506.03978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Structured Pruning for Diverse Best-of-N Reasoning Optimization
Model pruning in transformer-based language models, traditionally viewed as a means of achieving computational savings, can enhance the model's reasoning capabilities. In this work, we uncover a surprising phenomenon: the selective pruning of certain attention heads leads to improvements in reasoning performance, particularly on challenging tasks. Motivated by this observation, we propose SPRINT, a novel contrastive learning framework that dynamically selects the optimal head and layer to prune…

Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks
Laksh Patel, Neel Shanbhag
https://arxiv.org/abs/2506.08024 https://

Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks
Distributed supply-chain optimization demands algorithms that can cope with unreliable communication, unbounded messaging delays, and geographically dispersed agents while still guaranteeing convergence with provable rates. In this work, we introduce DAPD-SCO (Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks), a fully asynchronous primal-dual scheme for network flow allocation over directed acyclic supply-chain graphs. Each edge agent independently updates its local f…

Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Yongpeng Zhao, Maik Pfefferkorn, Maximilian Templer, Rolf Findeisen
https://arxiv.org/abs/2506.08719

Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, making the process inefficient. We propose a multi-fidelity Bayesian optimization approach that efficiently learns optimal controller parameters by leveraging both low-fidelity simulation data and a very limited number of real-world experiments. Our approach significantly reduces the need for manual tuning and expensive fi…

Deploying SICNav in the Field: Safe and Interactive Crowd Navigation using MPC and Bilevel Optimization
Sepehr Samavi, Garvish Bhutani, Florian Shkurti, Angela P. Schoellig
https://arxiv.org/abs/2506.08851

Deploying SICNav in the Field: Safe and Interactive Crowd Navigation using MPC and Bilevel Optimization
Safe and efficient navigation in crowded environments remains a critical challenge for robots that provide a variety of service tasks such as food delivery or autonomous wheelchair mobility. Classical robot crowd navigation methods decouple human motion prediction from robot motion planning, which neglects the closed-loop interactions between humans and robots. This lack of a model for human reactions to the robot plan (e.g. moving out of the way) can cause the robot to get stuck. Our proposed …

This https://arxiv.org/abs/2502.16212 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Clustering-Based Sub-QUBO Extraction for Hybrid QUBO Solvers
Quantum Approximate Optimization Algorithm (QAOA) can be used to solve quadratic unconstrained binary optimization (QUBO) problems. However, the size of the solvable problem is limited by the number of qubits. To leverage noisy intermediate-scale quantum (NISQ) devices to solve large-scale QUBO problems, one possible way is to decompose the full problem into multiple sub-problems, which we refer to as the Sub-QUBO Formalism. In this work, we enhance this formalism by proposing a sub-QUBO extrac…

This https://arxiv.org/abs/2505.23674 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Quantum-Based Software Engineering
Quantum computing has demonstrated potential for solving computationally intensive problems more efficiently than classical methods. Many software engineering tasks, such as test case selection, static analysis, code clone detection, and defect prediction, involve complex optimization, search, or classification, making them candidates for quantum enhancement. In this paper, we propose Quantum-Based Software Engineering (QBSE), a potential research direction for applying quantum computing to cla…

Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models
Chaoyi Zhu, Zaitang Li, Renyi Yang, Robert Birke, Pin-Yu Chen, Tsung-Yi Ho, Lydia Y. Chen
https://arxiv.org/abs/2506.06018

Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models
Watermarking becomes one of the pivotal solutions to trace and verify the origin of synthetic images generated by artificial intelligence models, but it is not free of risks. Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial optimization without knowledge of the target generative model and watermark schemes. In this paper, we uncover a greater risk of an optimization-free and universal watermark forgery that harnesses existing re…

Training-Free Query Optimization via LLM-Based Plan Similarity
Nikita Vasilenko, Alexander Demin, Vladimir Boorlakov
https://arxiv.org/abs/2506.05853 https…

Training-Free Query Optimization via LLM-Based Plan Similarity
Large language model (LLM) embeddings offer a promising new avenue for database query optimization. In this paper, we explore how pre-trained execution plan embeddings can guide SQL query execution without the need for additional model training. We introduce LLM-PM (LLM-based Plan Mapping), a framework that embeds the default execution plan of a query, finds its k nearest neighbors among previously executed plans, and recommends database hintsets based on neighborhood voting. A lightweight cons…

Composite Optimization with Indicator Functions: Stationary Duality and a Semismooth Newton Method
Penghe Zhang, Naihua Xiu, Houduo Qi
https://arxiv.org/abs/2506.08374

Composite Optimization with Indicator Functions: Stationary Duality and a Semismooth Newton Method
Indicator functions of taking values of zero or one are essential to numerous applications in machine learning and statistics. The corresponding primal optimization model has been researched in several recent works. However, its dual problem is a more challenging topic that has not been well addressed. One possible reason is that the Fenchel conjugate of any indicator function is finite only at the origin. This work aims to explore the dual optimization for the sum of a strongly convex function…

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Dingyi Rong, Haotian Lu, Wenzhuo Zheng, Fan Zhang, Shuangjia Zheng, Ning Liu
https://arxiv.org/abs/2506.09496

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Designing protein sequences with optimal energetic stability is a key challenge in protein inverse folding, as current deep learning methods are primarily trained by maximizing sequence recovery rates, often neglecting the energy of the generated sequences. This work aims to overcome this limitation by developing a model that directly generates low-energy, stable protein sequences. We propose EnerBridge-DPO, a novel inverse folding framework focused on generating low-energy, high-stability prot…

This https://arxiv.org/abs/2505.10283 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Comparative Analysis of Richardson-Lucy Deconvolution and Data Unfolding with Mean Integrated Square Error Optimization
Two maximum likelihood-based algorithms for unfolding or deconvolution are considered: the Richardson-Lucy method and the Data Unfolding method with Mean Integrated Square Error (MISE) optimization [10]. Unfolding is viewed as a procedure for estimating an unknown probability density function. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a…

Optimization of Bottlenecks in Quantum Graphs Guided by Fiedler Vector-Based Spectral Derivatives
John TM Campbell, John Dooley
https://arxiv.org/abs/2506.07875

Optimization of Bottlenecks in Quantum Graphs Guided by Fiedler Vector-Based Spectral Derivatives
This paper discusses the relationships between the Fiedler vector, the Cheeger constant, and threshold behaviors in networks of quantum resource nodes represented as Quantum Directed Acyclic Graphs (QDAGs). We explore how these mathematical constructs can be applied to understand the dynamics of quantum information flow in QDAGs, especially in the context of routing problems with bottlenecks in graph signal processing, and how new eigenvalue-based rewiring techniques can optimize entanglement d…

Delay Optimization in Remote ID-Based UAV Communication via BLE and Wi-Fi Switching
Yian Zhu, Ziye Jia, Lei Zhang, Yao Wu, Qiuming Zhu, Qihui Wu
https://arxiv.org/abs/2506.07715

Delay Optimization in Remote ID-Based UAV Communication via BLE and Wi-Fi Switching
The remote identification (Remote ID) broadcast capability allows unmanned aerial vehicles (UAVs) to exchange messages, which is a pivotal technology for inter-UAV communications. Although this capability enhances the operational visibility, low delay in Remote ID-based communications is critical for ensuring the efficiency and timeliness of multi-UAV operations in dynamic environments. To address this challenge, we first establish delay models for Remote ID communications by considering packet…

This https://arxiv.org/abs/2506.03770 has been replaced.
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Multiuser Beamformig for Pinching-Antenna Systems: An Element-wise Optimization Framework
The pinching-antenna system (PASS) reconstructs wireless channels through pinching beamforming, i.e., optimizing the activated locations of pinching antennas (PAs) along the waveguide. The aim of this article is to investigate the joint design of baseband beamforming and pinching beamforming. A low-complexity element-wise sequential optimization framework is proposed to address the sum-rate maximization problem in PASS-enabled downlink and uplink channels. i) For the downlink scenario, maximum …

This https://arxiv.org/abs/2506.02916 has been replaced.
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MMM4Rec: An Transfer-Efficient Framework for Multi-modal Sequential Recommendation
Sequential Recommendation (SR) systems model user preferences by analyzing interaction histories. Although transferable multi-modal SR architectures demonstrate superior performance compared to traditional ID-based approaches, current methods incur substantial fine-tuning costs when adapting to new domains due to complex optimization requirements and negative transfer effects - a significant deployment bottleneck that hinders engineers from efficiently repurposing pre-trained models for novel a…

This https://arxiv.org/abs/2312.10290 has been replaced.
link: https://scholar.google.com/scholar?q=a

Runtime Analysis of the SMS-EMOA for Many-Objective Optimization
This paper conducts the first rigorous runtime analysis of the SMS-EMOA for many-objective optimization. To this aim, we first propose a many-objective counterpart of the bi-objective OJZJ benchmark. We prove that SMS-EMOA computes the full Pareto front of this benchmark in an expected number of $O(μM n^k)$ iterations, where $n$ denotes the problem size (length of the bit-string representation), $k$ the gap size (a difficulty parameter of the problem), $M=(2n/m-2k+3)^{m/2}$ the size of the Par…

MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion
Franck Meyer, Kyunghoon Hur, Edward Choi
https://arxiv.org/abs/2506.08357 ht…

MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion
Despite the remarkable progress of deep-learning methods generating a target vital sign waveform from a source vital sign waveform, most existing models are designed exclusively for a specific source-to-target pair. This requires distinct model architectures, optimization procedures, and pre-processing pipelines, resulting in multiple models that hinder usability in clinical settings. To address this limitation, we propose the Multi-Directional Vital-Sign Converter (MD-ViSCo), a unified framewo…

CompilerGPT: Leveraging Large Language Models for Analyzing and Acting on Compiler Optimization Reports
Peter Pirkelbauer
https://arxiv.org/abs/2506.06227 …

CompilerGPT: Leveraging Large Language Models for Analyzing and Acting on Compiler Optimization Reports
Current compiler optimization reports often present complex, technical information that is difficult for programmers to interpret and act upon effectively. This paper assesses the capability of large language models (LLM) to understand compiler optimization reports and automatically rewrite the code accordingly. To this end, the paper introduces CompilerGPT, a novel framework that automates the interaction between compilers, LLMs, and user defined test and evaluation harness. CompilerGPT's wo…

This https://arxiv.org/abs/2504.10200 has been replaced.
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Stability analysis of discrete Boltzmann simulation for supersonic flows: Influencing factors, coupling mechanisms and optimization strategies
Supersonic flow simulations face challenges in trans-scale modeling, numerical stability, and complex field analysis due to inherent nonlinear, nonequilibrium, and multiscale characteristics. The discrete Boltzmann method (DBM) provides a multiscale kinetic modeling framework and analysis tool to capture complex discrete/nonequilibrium effects. While the numerical scheme plays a fundamental role in DBM simulations, a comprehensive stability analysis remains lacking. Similar to LBM, complexity m…

This https://arxiv.org/abs/2505.12990 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

From Theory to Practice: Analyzing VQPM for Quantum Optimization of QUBO Problems
The variational quantum power method (VQPM), which adapts the classical power iteration algorithm for quantum settings, has shown promise for eigenvector estimation and optimization on quantum hardware. In this work, we provide a comprehensive theoretical and numerical analysis of VQPM by investigating its convergence, robustness, and qubit locking mechanisms. We present detailed strategies for applying VQPM to QUBO problems by leveraging these locking mechanisms. Based on the simulations for e…

Information-guided optimization of image-based sensorless adaptive optics methods
Biwei Zhang, Martin J. Booth, Qi Hu
https://arxiv.org/abs/2506.07482 http…

Information-guided optimization of image-based sensorless adaptive optics methods
Adaptive optics (AO) are reconfigurable devices that compensate for wavefront distortions or aberrations in optical systems such as microscopes, telescopes and ophthalmoscopes. Aberrations have detrimental effects that can reduce imaging quality and compromise scientific information. Sensorless AO methods were introduced to correct aberrations without a separate wavefront sensor, inferring wavefront-related information directly from phase-diverse sample images. Most sensorless AO control system…

This https://arxiv.org/abs/2505.20929 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Two-step dimensionality reduction of human mobility data: From potential landscapes to spatiotemporal insights
Understanding the spatiotemporal patterns of human mobility is crucial for addressing societal challenges, such as epidemic control and urban transportation optimization. Despite advancements in data collection, the complexity and scale of mobility data continue to pose significant analytical challenges. Existing methods often result in losing location-specific details and fail to fully capture the intricacies of human movement. This study proposes a two-step dimensionality reduction framework …

Optimization of target film materials and protective coatings for sealed neutron generator
Yingying Cao, Sijia Zhou, Pingwei Sun, Jiayu Li, Shangrui Jiang, Shiwei Jing
https://arxiv.org/abs/2506.09060

Optimization of target film materials and protective coatings for sealed neutron generator
Magnesium target film has better thermal stability and neutron yield than titanium target, making it a potential neutron generator target film material. The radiation resistance of elemental magnesium targets is relatively weak, and their radiation resistance can be improved by alloying magnesium target films. The irradiation damage of pure magnesium targets and magnesium alloy target films was studied using SRIM. The results indicate that the irradiation damage of magnesium alloy target films …

An analytical optimization of plasma density profiles for downramp injection in laser wake-field acceleration
Gaetano Fiore, Paolo Tomassini
https://arxiv.org/abs/2506.06814

An analytical optimization of plasma density profiles for downramp injection in laser wake-field acceleration
We propose and detail a multi-step analytical procedure, based on an improved fully relativistic plane model for Laser Wake Field Acceleration, to tailor the initial density of a cold diluted plasma to the laser pulse profile, so as to control and optimize the partial wave-breaking of the plasma wave and maximize the acceleration of small bunches of electrons self-injected by the first wave-breaking at the density down-ramp, at least in the first stages of their acceleration phase. We find an e…

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Zixuan Jiang, Renjing Xu
https://arxiv.org/abs/2506.07035 https://

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Deciphering protein function remains a fundamental challenge in protein representation learning. The task presents significant difficulties for protein language models (PLMs) due to the sheer volume of functional annotation categories and the highly imbalanced distribution of annotated instances across biological ontologies. Inspired by the remarkable success of reinforcement learning from human feedback (RLHF) in large language model (LLM) alignment, we propose AnnoDPO, a novel multi-modal fra…

This https://arxiv.org/abs/2410.00117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

An Overview of the Burer-Monteiro Method for Certifiable Robot Perception
This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can be used to perform global optimization for non-convex perception problems. Specifically, BM leverages the low-rank structure of typical semidefinite programs to dramatically reduce the computational cost of performing optimization. This paper discus…

This https://arxiv.org/abs/2505.24718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Reinforcing Video Reasoning with Focused Thinking
Recent advancements in reinforcement learning, particularly through Group Relative Policy Optimization (GRPO), have significantly improved multimodal large language models for complex reasoning tasks. However, two critical limitations persist: 1) they often produce unfocused, verbose reasoning chains that obscure salient spatiotemporal cues and 2) binary rewarding fails to account for partially correct answers, resulting in high reward variance and inefficient learning. In this paper, we propos…

This https://arxiv.org/abs/2409.01699 has been replaced.
initial toot: https://mastoxiv.page/@…

Quick design of feasible tensor networks for constrained combinatorial optimization
In this study, we propose a new method for constrained combinatorial optimization using tensor networks. Combinatorial optimization methods employing quantum gates, such as quantum approximate optimization algorithm, have been intensively investigated. However, their limitations in errors and the number of qubits prevent them from handling large-scale combinatorial optimization problems. Alternatively, attempts have been made to solve larger-scale problems using tensor networks that can approxi…

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
William de Vazelhes, Xiao-Tong Yuan, Bin Gu
https://arxiv.org/abs/2506.08558

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
In sparse optimization, enforcing hard constraints using the $\ell_0$ pseudo-norm offers advantages like controlled sparsity compared to convex relaxations. However, many real-world applications demand not only sparsity constraints but also some extra constraints. While prior algorithms have been developed to address this complex scenario with mixed combinatorial and convex constraints, they typically require the closed form projection onto the mixed constraints which might not exist, and/or on…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new/
[4/5]:
Evolutionary Policy Optimization
https://arxiv.o…

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Vicente P. Soloviev, Antonio M\'arquez Romero, Josh Kirsopp, Michal Krompiec
https://arxiv.org/abs/2506.08947

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Quantum Approximate Optimization Algorithms (QAOA) have demonstrated a strong potential in addressing graph-based optimization problems. However, the execution of large-scale quantum circuits remains constrained by the limitations of current quantum hardware. In this work, we introduce QuantCut, an automatic framework for circuit cutting that enables efficient execution of large quantum circuits by decomposing entangling two-qubit gates into manageable sub-circuits. Specifically, we focus on ga…

This https://arxiv.org/abs/2505.19912 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation
We present Adjacent Possible Exploration (APE), a selective fine-tuning method for adapting large language models that systematically explores parameter modifications while maintaining model stability. Inspired by evolutionary optimization principles, APE evaluates multiple candidate parameter updates through fine-tuning on small data subsets and accepts only those exceeding a performance threshold. Unlike standard fine-tuning that follows single gradient directions, APE implements a filtered s…

A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Xiaotian Chen, Hongyun Liu, Seyed Sahand Mohammadi Ziabari
https://arxiv.org/abs/2506.09204

A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
Deep Neural Networks (DNNs) have been proven to be exceptionally effective and have been applied across diverse domains within deep learning. However, as DNN models increase in complexity, the demand for reduced computational costs and memory overheads has become increasingly urgent. Sparsity has emerged as a leading approach in this area. The robustness of sparse Multi-layer Perceptrons (MLPs) for supervised feature selection, along with the application of Sparse Evolutionary Training (SET), i…

This https://arxiv.org/abs/2501.09799 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)
Accelerated MRI involves collecting partial k-space measurements to reduce acquisition time, patient discomfort, and motion artifacts, and typically uses regular undersampling patterns or hand-designed schemes. Recent works have studied population-adaptive sampling patterns that are learned from a group of patients (or scans) based on population-specific metrics. However, such a general sampling pattern can be sub-optimal for any specific scan since it may lack scan or slice adaptive details. T…

Hybrid Beamforming Optimization for MIMO ISAC Exploiting Prior Information: A PCRB-based Approach
Yizhuo Wang, Shuowen Zhang
https://arxiv.org/abs/2506.07869

Hybrid Beamforming Optimization for MIMO ISAC Exploiting Prior Information: A PCRB-based Approach
This paper considers a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, where a multi-antenna base station (BS) with transceiver hybrid analog-digital arrays transmits dual-functional signals to communicate with a multi-antenna user and simultaneously sense the unknown and random location information of a target based on the reflected echo signals and the prior distribution information on the target's location. Under transceiver hybrid arrays, we charact…

This https://arxiv.org/abs/2505.12453 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
Federated recommender system (FedRec) has emerged as a solution to protect user data through collaborative training techniques. A typical FedRec involves transmitting the full model and entire weight updates between edge devices and the server, causing significant burdens to devices with limited bandwidth and computational power. While the sparsity of embedding updates provides opportunity for payload optimization, existing sparsity-aware federated protocols generally sacrifice privacy for effi…

A Cubic Regularization Method for Multiobjective Optimization
Douglas S. Gon\c{c}alves, Max L. N. Gon\c{c}alves, Jefferson G. Melo
https://arxiv.org/abs/2506.08181

A Cubic Regularization Method for Multiobjective Optimization
This work introduces a new cubic regularization method for nonconvex unconstrained multiobjective optimization problems. At each iteration of the method, a model associated with the cubic regularization of each component of the objective function is minimized. This model allows approximations for the first- and second-order derivatives, which must satisfy suitable error conditions. One interesting feature of the proposed algorithm is that the regularization parameter of the model and the accura…

Decentralized Optimization with Amplified Privacy via Efficient Communication
Wei Huo, Changxin Liu, Kemi Ding, Karl Henrik Johansson, Ling Shi
https://arxiv.org/abs/2506.07102

Decentralized Optimization with Amplified Privacy via Efficient Communication
Decentralized optimization is crucial for multi-agent systems, with significant concerns about communication efficiency and privacy. This paper explores the role of efficient communication in decentralized stochastic gradient descent algorithms for enhancing privacy preservation. We develop a novel algorithm that incorporates two key features: random agent activation and sparsified communication. Utilizing differential privacy, we demonstrate that these features reduce noise without sacrificing…

Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
No\'emie Bergues, Arthur Carr\'e, Paul Join-Lambert, Brice Hoffmann, Arnaud Blondel, Hamza Tajmouati
https://arxiv.org/abs/2506.06305

Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
Predicting the 3D conformation of small molecules within protein binding sites is a key challenge in drug design. When a crystallized reference ligand (template) is available, it provides geometric priors that can guide 3D pose prediction. We present a two-stage method for ligand conformation generation guided by such templates. In the first stage, we introduce a molecular alignment approach based on flow-matching to generate 3D coordinates for the ligand, using the template structure as a refe…

sparseGeoHOPCA: A Geometric Solution to Sparse Higher-Order PCA Without Covariance Estimation
Renjie Xu, Chong Wu, Maolin Che, Zhuoheng Ran, Yimin Wei, Hong Yan
https://arxiv.org/abs/2506.08670

sparseGeoHOPCA: A Geometric Solution to Sparse Higher-Order PCA Without Covariance Estimation
We propose sparseGeoHOPCA, a novel framework for sparse higher-order principal component analysis (SHOPCA) that introduces a geometric perspective to high-dimensional tensor decomposition. By unfolding the input tensor along each mode and reformulating the resulting subproblems as structured binary linear optimization problems, our method transforms the original nonconvex sparse objective into a tractable geometric form. This eliminates the need for explicit covariance estimation and iterative …

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library
Weixun Wang, Shaopan Xiong, Gengru Chen, Wei Gao, Sheng Guo, Yancheng He, Ju Huang, Jiaheng Liu, Zhendong Li, Xiaoyang Li, Zichen Liu, Haizhou Zhao, Dakai An, Lunxi Cao, Qiyang Cao, Wanxi Deng, Feilei Du, Yiliang Gu, Jiahe Li, Xiang Li, Mingjie Liu, Yijia Luo, Zihe Liu, Yadao Wang, Pei Wang, Tianyuan Wu, Yanan Wu, Yuheng Zhao, Shuaibing Zhao, Jin Yang, Siran Yang, Yingshui Tan, …

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library
We introduce ROLL, an efficient, scalable, and user-friendly library designed for Reinforcement Learning Optimization for Large-scale Learning. ROLL caters to three primary user groups: tech pioneers aiming for cost-effective, fault-tolerant large-scale training, developers requiring flexible control over training workflows, and researchers seeking agile experimentation. ROLL is built upon several key modules to serve these user groups effectively. First, a single-controller architecture combin…

Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization
Renjie He, Yiqiu Wang, Meixia Tao, Shu Sun
https://arxiv.org/abs/2506.07019

Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization
This paper investigates the passive detection problem in multi-static integrated sensing and communication (ISAC) systems, where multiple sensing receivers (SRs) jointly detect a target using random unknown communication signals transmitted by a collaborative base station. Unlike traditional active detection, the considered passive detection does not require complete prior knowledge of the transmitted communication signals at each SR. First, we derive a generalized likelihood ratio test detecto…

UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments
Kaiyuan Chen, Wanpeng Zhao, Yongxi Liu, Yuanqing Xia, Wannian Liang, Shuo Wang
https://arxiv.org/abs/2506.06136

UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments
In post-disaster scenarios, rapid and efficient delivery of medical resources is critical and challenging due to severe damage to infrastructure. To provide an optimized solution, we propose a cooperative trajectory optimization and task allocation framework leveraging unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). This study integrates a Genetic Algorithm (GA) for efficient task allocation among multiple UAVs and UGVs, and employs an informed-RRT* (Rapidly-exploring Rando…

Complexity Analysis of Convex Majorization Schemes for Nonconvex Constrained Optimization
Nuozhou Wang, Junyu Zhang, Shuzhong Zhang
https://arxiv.org/abs/2506.08506

Complexity Analysis of Convex Majorization Schemes for Nonconvex Constrained Optimization
We introduce and study various algorithms for solving nonconvex minimization with inequality constraints, based on the construction of convex surrogate envelopes that majorize the objective and the constraints. In the case where the objective and constraint functions are gradient Hölderian continuous, the surrogate functions can be readily constructed and the solution method can be efficiently implemented. The surrogate envelopes are extended to the settings where the second-order information …

This https://arxiv.org/abs/2505.11862 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
We propose Q-Policy, a hybrid quantum-classical reinforcement learning (RL) framework that mathematically accelerates policy evaluation and optimization by exploiting quantum computing primitives. Q-Policy encodes value functions in quantum superposition, enabling simultaneous evaluation of multiple state-action pairs via amplitude encoding and quantum parallelism. We introduce a quantum-enhanced policy iteration algorithm with provable polynomial reductions in sample complexity for the evaluat…

Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking
Jun Chen, Lina Liu, Tianyi Zhu, Yong Liu, Guang Dai, Yunliang Jiang, Ivor W. Tsang
https://arxiv.org/abs/2506.07351

Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking
This paper considers the problem of decentralized optimization on compact submanifolds, where a finite sum of smooth (possibly non-convex) local functions is minimized by $n$ agents forming an undirected and connected graph. However, the efficiency of distributed optimization is often hindered by communication bottlenecks. To mitigate this, we propose the Quantized Riemannian Gradient Tracking (Q-RGT) algorithm, where agents update their local variables using quantized gradients. The introducti…

Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance
Kaiyuan Chen, Yuhan Suo, Shaowei Cui, Yuanqing Xia, Wannian Liang, Shuo Wang
https://arxiv.org/abs/2506.06038

Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance
This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3 degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, c…

Protein folding with an all-to-all trapped-ion quantum computer
Sebasti\'an V. Romero, Alejandro Gomez Cadavid, Pavle Nika\v{c}evi\'c, Enrique Solano, Narendra N. Hegade, Miguel Angel Lopez-Ruiz, Claudio Girotto, Masako Yamada, Panagiotis Kl. Barkoutsos, Ananth Kaushik, Martin Roetteler
https://arxiv.org/abs/2506.07866…

Protein folding with an all-to-all trapped-ion quantum computer
We experimentally demonstrate that the bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm, implemented on IonQ's fully connected trapped-ion quantum processors, offers an efficient approach to solving dense higher-order unconstrained binary optimization (HUBO) problems. Specifically, we tackle protein folding on a tetrahedral lattice for up to 12 amino acids, representing the largest quantum hardware implementations of protein folding problems reported to date. Additi…

This https://arxiv.org/abs/2505.01323 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Proven Approximation Guarantees in Multi-Objective Optimization: SPEA2 Beats NSGA-II
Together with the NSGA-II and SMS-EMOA, the strength Pareto evolutionary algorithm 2 (SPEA2) is one of the most prominent dominance-based multi-objective evolutionary algorithms (MOEAs). Different from the NSGA-II, it does not employ the crowding distance (essentially the distance to neighboring solutions) to compare pairwise non-dominating solutions but a complex system of $σ$-distances that builds on the distances to all other solutions. In this work, we give a first mathematical proof showi…

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
Haoxiang Ye, Tao Sun, Qing Ling
https://arxiv.org/abs/2506.09438

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
Decentralized learning, which facilitates joint model training across geographically scattered agents, has gained significant attention in the field of signal and information processing in recent years. While the optimization errors of decentralized learning algorithms have been extensively studied, their generalization errors remain relatively under-explored. As the generalization errors reflect the scalability of trained models on unseen data and are crucial in determining the performance of …

This https://arxiv.org/abs/2503.09409 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation
Despite the widespread adoption of industrial robots in automotive assembly, wire harness installation remains a largely manual process, as it requires precise and flexible manipulation. To address this challenge, we design a novel AI-based framework that automates cable connector mating by integrating force control with deep visuotactile learning. Our system optimizes search-and-insertion strategies using first-order optimization over a multimodal transformer architecture trained on visual, ta…

This https://arxiv.org/abs/2501.13244 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the Instability of Nesterov's ODE under Non-Conservative Vector Fields
We study the instability properties of Nesterov's ODE in non-conservative settings, where the driving term is not necessarily the gradient of a potential function. While convergence properties under Nesterov's ODE are well-characterized for optimization settings with gradient-based driving terms, we show that the presence of arbitrarily small non-conservative terms can lead to instability, a phenomenon previously observed empirically via numerical studies in optimization and game-theoretic prob…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new/
[2/2]:
The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization

Controlled Reach-avoid Set Computation for Discrete-time Polynomial Systems via Convex Optimization
Taoran Wu, Yiling Xue, Dejin Ren, Arvind Easwaran, Martin Fr\"anzle, Bai Xue
https://arxiv.org/abs/2506.06679

Controlled Reach-avoid Set Computation for Discrete-time Polynomial Systems via Convex Optimization
This paper addresses the computation of controlled reach-avoid sets (CRASs) for discrete-time polynomial systems subject to control inputs. A CRAS is a set encompassing initial states from which there exist control inputs driving the system into a target set while avoiding unsafe sets. However, efficiently computing CRASs remains an open problem, especially for discrete-time systems. In this paper, we propose a novel framework for computing CRASs which takes advantage of a probabilistic perspec…

This https://arxiv.org/abs/2505.18300 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on pa…

This https://arxiv.org/abs/2409.10283 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

ASMA: An Adaptive Safety Margin Algorithm for Vision-Language Drone Navigation via Scene-Aware Control Barrier Functions
In the rapidly evolving field of vision-language navigation (VLN), ensuring safety for physical agents remains an open challenge. For a human-in-the-loop language-operated drone to navigate safely, it must understand natural language commands, perceive the environment, and simultaneously avoid hazards in real time. Control Barrier Functions (CBFs) are formal methods that enforce safe operating conditions. Model Predictive Control (MPC) is an optimization framework that plans a sequence of futur…

This https://arxiv.org/abs/2505.01903 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

LookAlike: Consistent Distractor Generation in Math MCQs
Large language models (LLMs) are increasingly used to generate distractors for multiple-choice questions (MCQs), especially in domains like math education. However, existing approaches are limited in ensuring that the generated distractors are consistent with common student errors. We propose LookAlike, a method that improves error-distractor consistency via preference optimization. Our two main innovations are: (a) mining synthetic preference pairs from model inconsistencies, and (b) alternati…

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…

This https://arxiv.org/abs/2506.03430 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Two-Stage Bidirectional Inverter Equivalent Circuit Model for Distribution Grid Steady-State Analysis and Optimization
This paper presents a \textit{physics-based} steady-state equivalent circuit model of a two-stage bidirectional inverter. These inverters connect distributed energy resources (DERs), such as photovoltaic (PV) and battery systems, to distribution grids. Existing inverter models have technical gaps on three fronts: i) inadequate modeling of inverter losses, ii) use of mathematical abstractions for bidirectional flow of power, and iii) inability to integrate different control modes into nonlinear …

This https://arxiv.org/abs/2506.05673 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery
The development of modern Artificial Intelligence (AI) models, particularly diffusion-based models employed in computer vision and image generation tasks, is undergoing a paradigmatic shift in development methodologies. Traditionally dominated by a "Model Centric" approach, in which performance gains were primarily pursued through increasingly complex model architectures and hyperparameter optimization, the field is now recognizing a more nuanced "Data-Centric" approach. This emergent framework…

Asymmetric Perturbation in Solving Bilinear Saddle-Point Optimization
Kenshi Abe, Mitsuki Sakamoto, Kaito Ariu, Atsushi Iwasaki
https://arxiv.org/abs/2506.05747

Asymmetric Perturbation in Solving Bilinear Saddle-Point Optimization
This paper proposes an asymmetric perturbation technique for solving saddle-point optimization problems, commonly arising in min-max problems, game theory, and constrained optimization. Perturbing payoffs or values are known to be effective in stabilizing learning dynamics and finding an exact solution or equilibrium. However, it requires careful adjustment of the perturbation magnitude; otherwise, learning dynamics converge to only an equilibrium. We establish an impossibility result that it a…

Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments
Kaiyuan Chen, Zhengjie Hu, Shaolin Zhang, Yuanqing Xia, Wannian Liang, Shuo Wang
https://arxiv.org/abs/2506.06012

Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments
The rapid detection of abnormal body temperatures in urban populations is essential for managing public health risks, especially during outbreaks of infectious diseases. Multi-drone thermal screening systems offer promising solutions for fast, large-scale, and non-intrusive human temperature monitoring. However, trajectory planning for multiple drones in complex urban environments poses significant challenges, including collision avoidance, coverage efficiency, and constrained flight environmen…

This https://arxiv.org/abs/2410.03815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Single-Shot Learning of Multirotor Controller Gains: A Data-Driven Approach with Experimental Validation
This paper demonstrates the single-shot learning capabilities of retrospective cost optimization based data-driven control applied to learning multirotor controller gains for trajectory tracking. In particular, the proposed control approach is first used within a simple multirotor simulation environment to learn appropriate multirotor controller gains to follow a trajectory. Then, the gains resulting from a single simulation run are used in a more complex multirotor simulation environment based…

This https://arxiv.org/abs/2506.05831 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling
We present Heartcare Suite, a multimodal comprehensive framework for finegrained electrocardiogram (ECG) understanding. It comprises three key components: (i) Heartcare-220K, a high-quality, structured, and comprehensive multimodal ECG dataset covering essential tasks such as disease diagnosis, waveform morphology analysis, and rhythm interpretation. (ii) Heartcare-Bench, a systematic and multi-dimensional benchmark designed to evaluate diagnostic intelligence and guide the optimization of Medi…

Shape optimization under width constraint: the Cheeger constant and the torsional rigidity
Beniamin Bogosel
https://arxiv.org/abs/2506.07708 https://

Shape optimization under width constraint: the Cheeger constant and the torsional rigidity
In this article it is shown that the equilateral triangle maximizes the Cheeger constant and minimizes the torsional rigidity among shapes having a fixed minimal width. The proof techniques use direct comparisons with simpler shapes, consisting of disks with three disjoint caps. Comparison results for harmonic functions help establish that in non-equilateral configurations the shape derivative has an appropriate sign, contradicting optimality.

Continuous-Time Output Feedback Adaptive Control for Stabilization and Tracking with Experimental Results
Mohammad Mirtaba, Ankit Goel
https://arxiv.org/abs/2506.08042

Continuous-Time Output Feedback Adaptive Control for Stabilization and Tracking with Experimental Results
This paper presents a continuous-time output feedback adaptive control technique for stabilization and tracking control problems. The adaptive controller is motivated by the classical discrete-time retrospective cost adaptive control algorithm. The particle swarm optimization framework automates the adaptive algorithm's hyper-parameter tuning. The proposed controller is numerically validated in the tracking problems of a double integrator and a bicopter system and is experimentally validated in…

A 40.68-MHz, 200-ns-Settling Active Rectifier and TX-Side Load Monitoring for Minimizing Radiated Power in Biomedical Implants
Ronald Wijermars, Yi-Han Ou-Yang, Sijun Du, Dante Gabriel Muratore
https://arxiv.org/abs/2506.07710

A 40.68-MHz, 200-ns-Settling Active Rectifier and TX-Side Load Monitoring for Minimizing Radiated Power in Biomedical Implants
This letter describes a 40.68 MHz wireless power transfer receiver for implantable applications focused on minimizing tissue heating. The system features a novel power radiated efficiency optimization strategy and a fast-settling active rectifier that maintains high efficiency during load and link variations required for downlink communication. The power radiated efficiency optimization explicitly reduces tissue heating while enabling transmitter-side load monitoring for closed-loop control. Th…

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…

[2025-06-11 Wed (UTC), 20 new articles found for math.OC Optimization and Control]
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Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games
Philipp Plank, Yufei Zhang
https://arxiv.org/abs/2506.05894 https://

Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games
Multi-agent reinforcement learning, despite its popularity and empirical success, faces significant scalability challenges in large-population dynamic games. Graphon mean field games (GMFGs) offer a principled framework for approximating such games while capturing heterogeneity among players. In this paper, we propose and analyze a policy optimization framework for continuous-time, finite-horizon linear-quadratic GMFGs. Exploiting the structural properties of GMFGs, we design an efficient polic…

This https://arxiv.org/abs/2410.08850 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Learning to Stop: Deep Learning for Mean Field Optimal Stopping
Optimal stopping is a fundamental problem in optimization with applications in risk management, finance, robotics, and machine learning. We extend the standard framework to a multi-agent setting, named multi-agent optimal stopping (MAOS), where agents cooperate to make optimal stopping decisions in a finite-space, discrete-time environment. Since solving MAOS becomes computationally prohibitive as the number of agents is very large, we study the mean-field optimal stopping (MFOS) problem, obtai…

BESS Participation Planning for Provision of Grid Services in Energy and Regulation Markets
Zeenat Hameed, Chresten Traeholt
https://arxiv.org/abs/2506.08472

BESS Participation Planning for Provision of Grid Services in Energy and Regulation Markets
This paper presents a stochastic optimization model for planning the participation of battery energy storage systems (BESSs) in energy and regulation markets. The proposed model quantifies and compares the business value of single and multi-market BESS services by accounting for their bid submission and acceptance procedures, pricing mechanisms and revenue streams, and penalty payments and degradation costs. By modelling the price uncertainties using market price scenarios (MPSs) and considerin…

An Efficient Augmented Lagrangian Method for Dynamic Optimal Transport on Surfaces Based on Second-Order Cone Programming
Liang Chen, Youyicun Lin, Yuxuan Zhou
https://arxiv.org/abs/2506.08988

An Efficient Augmented Lagrangian Method for Dynamic Optimal Transport on Surfaces Based on Second-Order Cone Programming
This paper proposes an efficient numerical optimization approach for solving dynamic optimal transport (DOT) problems on general smooth surfaces, computing both the quadratic Wasserstein distance and the associated transportation path. Building on the convex DOT model of Benamou and Brenier, we first properly reformulate its dual problem, discretized on a triangular mesh for space together with a staggered grid for time, to a linear second-order cone programming. Then the resulting finite-dimen…

This https://arxiv.org/abs/2405.08485 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Doubly relaxed forward-Douglas--Rachford splitting for the sum of two nonconvex and a DC function
In this paper, we consider a class of structured nonconvex nonsmooth optimization problems whose objective function is the sum of three nonconvex functions, one of which is expressed in a difference-of-convex (DC) form. This problem class covers several important structures in the literature including the sum of three functions and the general DC program. We propose a splitting algorithm and prove the subsequential convergence to a stationary point of the problem. The full sequential convergenc…

This https://arxiv.org/abs/2412.06436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation
We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minimizer of a convex optimization problem (denoted lower-level problem). We utilize an iterative algorithm called `piggyback' to compute the gradient of the loss and minimizer of the lower-level problem. Given that the lower-level problem is solved numerically, the loss function and thus its gradient can only be computed …

This https://arxiv.org/abs/2412.12690 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Preference Robust Ordinal Priority Approach with Preference Elicitation under Incomplete Information for Multi-Attribute Robust Ranking and Selection
Ordinal Priority Approach (OPA) has recently been proposed to determine the weights of experts, attributes, and alternatives using ordinal preference without precise information for multi-attribute ranking and selection (MARS). This study extends OPA with preference elicitation under incomplete information to counter the parametric and preference uncertainty within MARS. Specifically, we propose Preference Robust Ordinal Priority Approach (OPA-PR) within a two-stage optimization framework to ge…

Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
Hao Huang, Zelda B. Zabinsky
https://arxiv.org/abs/2506.04554 https://

Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
A multiple objective simulation optimization algorithm named Multiple Objective Probabilistic Branch and Bound with Single Observation (MOPBnB(so)) is presented for approximating the Pareto optimal set and the associated efficient frontier for stochastic multi-objective optimization problems. MOPBnB(so) evaluates a noisy function exactly once at any solution and uses neighboring solutions to estimate the objective functions, in contrast to a variant that uses multiple replications at a solution…