Tootfinder

Stiefel optimization is NP-hard
Zehua Lai, Lek-Heng Lim, Tianyun Tang
https://arxiv.org/abs/2507.02839 https://arxiv.org/pdf/2507.028…

Stiefel optimization is NP-hard
We show that linearly constrained linear optimization over a Stiefel or Grassmann manifold is NP-hard in general. We show that the same is true for unconstrained quadratic optimization over a Stiefel manifold. We will establish the nonexistence of FPTAS for these optimization problems over a Stiefel manifold. As an aside we extend our results to flag manifolds. Combined with earlier findings, this shows that manifold optimization is a difficult endeavor -- even the simplest problems like LP and…

Trajectory Optimization for Differential Drive Mobile Manipulators via Topological Paths Search and Arc Length-Yaw Parameterization
Long Xu, Choilam Wong, Mengke Zhang, Junxiao Lin, Fei Gao
https://arxiv.org/abs/2507.02761

Trajectory Optimization for Differential Drive Mobile Manipulators via Topological Paths Search and Arc Length-Yaw Parameterization
We present an efficient hierarchical motion planning pipeline for differential drive mobile manipulators. Our approach first searches for multiple collisionfree and topologically distinct paths for the mobile base to extract the space in which optimal solutions may exist. Further sampling and optimization are then conducted in parallel to explore feasible whole-body trajectories. For trajectory optimization, we employ polynomial trajectories and arc length-yaw parameterization, enabling efficie…

This https://arxiv.org/abs/2505.20734 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Adversarial bandit optimization for approximately linear functions
We consider a bandit optimization problem for nonconvex and non-smooth functions, where in each trial the loss function is the sum of a linear function and a small but arbitrary perturbation chosen after observing the player's choice. We give both expected and high probability regret bounds for the problem. Our result also implies an improved high-probability regret bound for the bandit linear optimization, a special case with no perturbation. We also give a lower bound on the expected regret.

Running-time Analysis of ($\mu \lambda$) Evolutionary Combinatorial Optimization Based on Multiple-gain Estimation
Min Huang, Pengxiang Chen, Han Huang, Tongli He, Yushan Zhang, Zhifeng Hao
https://arxiv.org/abs/2507.02381

Running-time Analysis of ($μ+λ$) Evolutionary Combinatorial Optimization Based on Multiple-gain Estimation
The running-time analysis of evolutionary combinatorial optimization is a fundamental topic in evolutionary computation. However, theoretical results regarding the $(μ+λ)$ evolutionary algorithm (EA) for combinatorial optimization problems remain relatively scarce compared to those for simple pseudo-Boolean problems. This paper proposes a multiple-gain model to analyze the running time of EAs for combinatorial optimization problems. The proposed model is an improved version of the average gai…

Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models
Chengao Li, Hanyu Zhang, Yunkun Xu, Hongyan Xue, Xiang Ao, Qing He
https://arxiv.org/abs/2507.01915

Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models
Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful technique for aligning large language models (LLMs) with human preferences. However, effectively aligning LLMs with diverse human preferences remains a significant challenge, particularly when they are conflict. To address this issue, we frame human value alignment as a multi-objective optimization problem, aiming to maximize a set of potentially conflicting objectives. We introduce Gradient-Adaptive Policy Optimization…

Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
Seongmin Kim, In-Saeng Suh
https://arxiv.org/abs/2506.03329

Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning
The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-…

TUC-PPO: Team Utility-Constrained Proximal Policy Optimization for Spatial Public Goods Games
Zhaoqilin Yang, Xin Wang, Ruichen Zhang, Chanchan Li, Youliang Tian
https://arxiv.org/abs/2507.02675

TUC-PPO: Team Utility-Constrained Proximal Policy Optimization for Spatial Public Goods Games
We introduce Team Utility-Constrained Proximal Policy Optimization (TUC-PPO), a new deep reinforcement learning framework. It extends Proximal Policy Optimization (PPO) by integrating team welfare objectives specifically for spatial public goods games. Unlike conventional approaches where cooperation emerges indirectly from individual rewards, TUC-PPO instead optimizes a bi-level objective integrating policy gradients and team utility constraints. Consequently, all policy updates explicitly inc…

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

Second-Order-Cone Formulations of Power Flow for Topology Optimization
Noah Rhodes, James Luedkte, Line Roald
https://arxiv.org/abs/2506.02234 https://

Second-Order-Cone Formulations of Power Flow for Topology Optimization
Optimization problems that involve topology optimization in scenarios with large scale outages, such as post-disaster restoration or public safety power shutoff planning, are very challenging to solve. Using simple power flow representations such as DC power flow or network flow models results in low quality solutions which requires significantly higher-than-predicted load shed to become AC feasible. Recent work has shown that formulations based on the Second Order Cone (SOC) power flow formula…

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Gopichand Bandarupalli
https://arxiv.org/abs/2506.02090 https://

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
Modern software systems complexity challenges efficient testing, as traditional machine learning (ML) struggles with large test suites. This research presents a hybrid framework integrating Quantum Annealing with ML to optimize test case prioritization in CI/CD pipelines. Leveraging quantum optimization, it achieves a 25 percent increase in defect detection efficiency and a 30 percent reduction in test execution time versus classical ML, validated on the Defects4J dataset. A simulated CI/CD env…

AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models
Chenhao Xue, Kezhi Li, Jiaxing Zhang, Yi Ren, Zhengyuan Shi, Chen Zhang, Yibo Lin, Lining Zhang, Qiang Xu, Guangyu Sun
https://arxiv.org/abs/2507.02598

AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models
Arithmetic circuits, such as adders and multipliers, are fundamental components of digital systems, directly impacting the performance, power efficiency, and area footprint. However, optimizing these circuits remains challenging due to the vast design space and complex physical constraints. While recent deep learning-based approaches have shown promise, they struggle to consistently explore high-potential design variants, limiting their optimization efficiency. To address this challenge, we pro…

Multiuser Beamformig for Pinching-Antenna Systems: An Element-wise Optimization Framework
Mingjun Sun, Chongjun Ouyang, Shaochuan Wu, Yuanwei Liu
https://arxiv.org/abs/2506.03770 …

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/2501.08512 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Ensuring Truthfulness in Distributed Aggregative Optimization
Distributed aggregative optimization methods are gaining increased traction due to their ability to address cooperative control and optimization problems, where the objective function of each agent depends not only on its own decision variable but also on the aggregation of other agents' decision variables. Nevertheless, existing distributed aggregative optimization methods implicitly assume all agents to be truthful in information sharing, which can be unrealistic in real-world scenarios, wher…

PDE-Constrained High-Order Mesh Optimization
Tzanio Kolev, Boyan Lazarov, Ketan Mittal, Mathias Schmidt, Vladimir Tomov
https://arxiv.org/abs/2507.01917 ht…

PDE-Constrained High-Order Mesh Optimization
We present a novel framework for PDE-constrained $r$-adaptivity of high-order meshes. The proposed method formulates mesh movement as an optimization problem, with an objective function defined as a convex combination of a mesh quality metric and a measure of the accuracy of the PDE solution obtained via finite element discretization. The proposed formulation achieves optimized, well-defined high-order meshes by integrating mesh quality control, PDE solution accuracy, and robust gradient regula…

A novel exact approach to polynomial optimization
Dimitris Bertsimas, Dick den Hertog, Thodoris Koukouvinos
https://arxiv.org/abs/2507.02120 https://

A novel exact approach to polynomial optimization
Polynomial optimization problems represent a wide class of optimization problems, with a large number of real-world applications. Current approaches for polynomial optimization, such as the sum of squares (SOS) method, rely on large-scale semidefinite programs, and therefore the scale of problems to which they can be applied is limited and an optimality guarantee is not always provided. Moreover, the problem can have other convex nonlinear parts, that cannot be handled by these approaches. In t…

SAT-BO: Verification Rule Learning and Optimization for FraudTransaction Detection
Mao Luo, Zhi Wang, Yiwen Huang, Qingyun Zhang, Zhouxing Su, Zhipeng Lv, Wen Hu, Jianguo Li
https://arxiv.org/abs/2507.02635

SAT-BO: Verification Rule Learning and Optimization for FraudTransaction Detection
Electronic payment platforms are estimated to process billions oftransactions daily, with the cumulative value of these transactionspotentially reaching into the trillions. Even a minor error within thishigh-volume environment could precipitate substantial financiallosses. To mitigate this risk, manually constructed verification rules,developed by domain experts, are typically employed to identifyand scrutinize transactions in production environments. However,due to the absence of a systematic …

Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
Ziye Jia, Can Cui, Chao Dong, Qihui Wu, Zhuang Ling, Dusit Niyato, Zhu Han
https://arxiv.org/abs/2506.01972

Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
With an extensive increment of computation demands, the aerial multi-access edge computing (MEC), mainly based on unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), plays significant roles in future network scenarios. In detail, UAVs can be flexibly deployed, while HAPs are characterized with large capacity and stability. Hence, in this paper, we provide a hierarchical model composed of an HAP and multi-UAVs, to provide aerial MEC services. Moreover, considering the errors of c…

Accelerated Portfolio Optimization and Option Pricing with Reinforcement Learning
Hadi Keramati, Samaneh Jazayeri
https://arxiv.org/abs/2507.01972 https://…

Accelerated Portfolio Optimization and Option Pricing with Reinforcement Learning
We present a reinforcement learning (RL)-driven framework for optimizing block-preconditioner sizes in iterative solvers used in portfolio optimization and option pricing. The covariance matrix in portfolio optimization or the discretization of differential operators in option pricing models lead to large linear systems of the form $\mathbf{A}\textbf{x}=\textbf{b}$. Direct inversion of high-dimensional portfolio or fine-grid option pricing incurs a significant computational cost. Therefore, ite…

Joint Optimization based on Two-phase GNN in RIS- and DF-assisted MISO Systems with Fine-grained Rate Demands
Huijun Tang, Jieling Zhang, Zhidong Zhao, Huaming Wu, Hongjian Sun, Pengfei Jiao
https://arxiv.org/abs/2506.02642

Joint Optimization based on Two-phase GNN in RIS- and DF-assisted MISO Systems with Fine-grained Rate Demands
Reconfigurable intelligent Surfaces (RIS) and half-duplex decoded and forwarded (DF) relays can collaborate to optimize wireless signal propagation in communication systems. Users typically have different rate demands and are clustered into groups in practice based on their requirements, where the former results in the trade-off between maximizing the rate and satisfying fine-grained rate demands, while the latter causes a trade-off between inter-group competition and intra-group cooperation wh…

Generative flow-based warm start of the variational quantum eigensolver
Hang Zou, Martin Rahm, Anton Frisk Kockum, Simon Olsson
https://arxiv.org/abs/2507.01726

Generative flow-based warm start of the variational quantum eigensolver
Hybrid quantum-classical algorithms like the variational quantum eigensolver (VQE) show promise for quantum simulations on near-term quantum devices, but are often limited by complex objective functions and expensive optimization procedures. Here, we propose Flow-VQE, a generative framework leveraging conditional normalizing flows with parameterized quantum circuits to efficiently generate high-quality variational parameters. By embedding a generative model into the VQE optimization loop throug…

Listwise Preference Alignment Optimization for Tail Item Recommendation
Zihao Li, Chao Yang, Tong Zhang, Yakun Chen, Xianzhi Wang, Guandong Xu, Daoyi Dong
https://arxiv.org/abs/2507.02255

Listwise Preference Alignment Optimization for Tail Item Recommendation
Preference alignment has achieved greater success on Large Language Models (LLMs) and drawn broad interest in recommendation research. Existing preference alignment methods for recommendation either require explicit reward modeling or only support pairwise preference comparison. The former directly increases substantial computational costs, while the latter hinders training efficiency on negative samples. Moreover, no existing effort has explored preference alignment solutions for tail-item rec…

Ultrahigh-Q Torsional Nanomechanics through Bayesian Optimization
Atkin D. Hyatt, Aman R. Agrawal, Christian M. Pluchar, Charles A. Condos, Dalziel J. Wilson
https://arxiv.org/abs/2506.02325

Ultrahigh-Q Torsional Nanomechanics through Bayesian Optimization
Recently it was discovered that torsion modes of strained nanoribbons exhibit dissipation dilution, giving a route to enhanced torque sensing and quantum optomechanics experiments. As with all strained nanomechanical resonators, an important limitation is bending loss due to mode curvature at the clamps. Here we use Bayesian optimization to design nanoribbons with optimal dissipation dilution of the fundamental torsion mode. Applied to centimeter-scale Si$_3$N$_4$ nanoribbons, we realize $Q$ fa…

"Eventually, I stopped responding to my body. I was responding instead to a dashboard." — @…
This is a great point and very much translates to so many other parts of life/work where people stop listening to their "body" (or to their org/product/offering), outsourcing/numbing/dumbing down their decision making based on dashboards of col…

Why I Gave Up My Smartwatch
Somewhere between the first time I tapped my wrist to skip a song and the three hundredth time I anxiously checked my resting heart rate, I started to hate my Apple Watch. The promise of the smartwatch was elegance, convenience, optimization. What I got was constant data drip, subtle panic,

Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization
Zhiwei Zhang, Samy Wu Fung, Anastasios Kyrillidis, Stanley Osher, Moshe Y. Vardi
https://arxiv.org/abs/2506.00674

Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization
The Boolean satisfiability (SAT) problem lies at the core of many applications in combinatorial optimization, software verification, cryptography, and machine learning. While state-of-the-art solvers have demonstrated high efficiency in handling conjunctive normal form (CNF) formulas, numerous applications require non-CNF (hybrid) constraints, such as XOR, cardinality, and Not-All-Equal constraints. Recent work leverages polynomial representations to represent such hybrid constraints, but it re…

An Experimental Approach for Running-Time Estimation of Multi-objective Evolutionary Algorithms in Numerical Optimization
Han Huang, Tianyu Wang, Chaoda Peng, Tongli He, Zhifeng Hao
https://arxiv.org/abs/2507.02372

An Experimental Approach for Running-Time Estimation of Multi-objective Evolutionary Algorithms in Numerical Optimization
Multi-objective evolutionary algorithms (MOEAs) have become essential tools for solving multi-objective optimization problems (MOPs), making their running time analysis crucial for assessing algorithmic efficiency and guiding practical applications. While significant theoretical advances have been achieved for combinatorial optimization, existing studies for numerical optimization primarily rely on algorithmic or problem simplifications, limiting their applicability to real-world scenarios. To …

This https://arxiv.org/abs/2502.12012 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csET_…

Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms
Variational quantum algorithms, such as the Recursive Quantum Approximate Optimization Algorithm (RQAOA), have become increasingly popular, offering promising avenues for employing Noisy Intermediate-Scale Quantum devices to address challenging combinatorial optimization tasks like the maximum cut problem. In this study, we utilize an evolutionary algorithm equipped with a unique fitness function. This approach targets hard maximum cut instances within the latent space of a Graph Autoencoder, i…

This https://arxiv.org/abs/2505.00812 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization
Recent studies indicate that deep neural networks degrade in generalization performance under noisy supervision. Existing methods focus on isolating clean subsets or correcting noisy labels, facing limitations such as high computational costs, heavy hyperparameter tuning process, and coarse-grained optimization. To address these challenges, we propose a novel two-stage noisy learning framework that enables instance-level optimization through a dynamically weighted loss function, avoiding hyperp…

Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
Djamel Rassem Lamouri, Iheb Nassim Aouadj, Smail Kourta, Riyadh Baghdadi
https://arxiv.org/abs/2506.01880

Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
Compilers are crucial in optimizing programs and accelerating their execution. However, optimizing programs automatically using compilers is not trivial. Recent work has attempted to use reinforcement learning (RL) to solve this problem. It has limitations though. Current methods either do not support the optimization of general loop nests or can only be used to optimize loop nests seen during training. In this paper, we propose Pearl, a novel framework that uses deep reinforcement learning to …

DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization
Geonyoung Lee, Geonhee Han, Paul Hongsuck Seo
https://arxiv.org/abs/2506.02858

DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization
Language-queried Audio Source Separation (LASS) enables open-vocabulary sound separation via natural language queries. While existing methods rely on task-specific training, we explore whether pretrained diffusion models, originally designed for audio generation, can inherently perform separation without further training. In this study, we introduce a training-free framework leveraging generative priors for zero-shot LASS. Analyzing naïve adaptations, we identify key limitations arising from m…

A Bi-Level Optimization Method for Redundant Dual-Arm Minimum Time Problems
Jonathan Fried, Santiago Paternain
https://arxiv.org/abs/2506.03982 https://

A Bi-Level Optimization Method for Redundant Dual-Arm Minimum Time Problems
In this work, we present a method for minimizing the time required for a redundant dual-arm robot to follow a desired relative Cartesian path at constant path speed by optimizing its joint trajectories, subject to position, velocity, and acceleration limits. The problem is reformulated as a bi-level optimization whose lower level is a convex, closed-form subproblem that maximizes path speed for a fixed trajectory, while the upper level updates the trajectory using a single-chain kinematic formu…

Robust feedback-based quantum optimization: analysis of coherent control errors
Mirko Legnini, Julian Berberich
https://arxiv.org/abs/2507.02532 https://…

Robust feedback-based quantum optimization: analysis of coherent control errors
The Feedback-based Algorithm for Quantum Optimization (FALQON) is a Lyapunov inspired quantum algorithm proposed to tackle combinatorial optimization problems. In this paper, we examine the robustness of FALQON against coherent control errors, a class of multiplicative errors that affect the control input. We show that the algorithm is asymptotically robust with respect to systematic errors, and we derive robustness bounds for independent errors. Finally, we propose a robust version of FALQON w…

This https://arxiv.org/abs/2503.18092 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Ergodic Optimization for Open Expanding Multi-valued Topological Dynamical Systems
We study the optimization of ergodic averages for multi-valued dynamical systems, i.e. where points may have multiple different forward orbits. Under upper semi-continuity assumptions, we show that the maximum space average with respect to invariant probability measures for such systems can be characterised in terms of maximum time averages on an auxiliary shift space. For all multi-valued expanding systems that are open mappings, we show that every Hölder continuous real-valued function can b…

Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions
Bo Yang, Ruihuai Liang, Weixin Li, Han Wang, Xuelin Cao, Zhiwen Yu, Samson Lasaulce, M\'erouane Debbah, Mohamed-Slim Alouini, H. Vincent Poor, Chau Yuen
https://arxiv.org/abs/2507.01773

Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions
While interest in the application of generative AI (GenAI) in network optimization has surged in recent years, its rapid progress has often overshadowed critical limitations intrinsic to generative models that remain insufficiently examined in existing literature. This survey provides a comprehensive review and critical analysis of GenAI in network optimization. We focus on the two dominant paradigms of GenAI including generative diffusion models (GDMs) and large pre-trained models (LPTMs), and…

A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors
Zheng Gong, Ziyi Jiang, Weihao Gao, Deng Zhuo, Lan Ma
https://arxiv.org/abs/2505.23862

A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors
The mRNA optimization is critical for therapeutic and biotechnological applications, since sequence features directly govern protein expression levels and efficacy. However, current methods face significant challenges in simultaneously achieving three key objectives: (1) fidelity (preventing unintended amino acid changes), (2) computational efficiency (speed and scalability), and (3) the scope of optimization variables considered (multi-objective capability). Furthermore, existing methods often…

This https://arxiv.org/abs/2505.23387 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…

Antenna Q-Factor Topology Optimization with Auxiliary Edge Resistivities
Stepan Bosak, Miloslav Capek, Jiri Matas
https://arxiv.org/abs/2506.00595 https://…

Antenna Q-Factor Topology Optimization with Auxiliary Edge Resistivities
This paper presents a novel bi-level topology optimization strategy within the method-of-moments paradigm. The proposed approach utilizes an auxiliary variables called edge resistivities related to the Rao-Wilton-Glisson method-of-moments basis functions, for a definition of a fast local optimization algorithm. The local algorithm combines automatic differentiation with adaptive gradient descent. A Bayesian optimization scheme is applied on top of the local algorithm to search for an optimum po…

This https://arxiv.org/abs/2501.12261 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

A Framework for the Design of Efficient Diversification Algorithms to NP-Hard Problems
There has been considerable recent interest in computing a diverse collection of solutions to a given optimization problem, both in the AI and theory communities. Given a classical optimization problem $Π$ (e.g., spanning tree, minimum cuts, maximum matching, minimum vertex cover) with input size $n$ and an integer $k\geq 1$, the goal is to generate a collection of $k$ maximally diverse solutions to $Π$. This diverse-X paradigm not only allows the user to generate very different solutions, bu…

High degree simple partial fractions in the Bergman space: Approximation and Optimization
Nikiforos Biehler
https://arxiv.org/abs/2506.02901 https://

High degree simple partial fractions in the Bergman space: Approximation and Optimization
We consider the class of standard weighted Bergman spaces $A^2_α(\mathbb{D})$ and the set $SF^N(\mathbb{T})$ of simple partial fractions of degree $N$ with poles on the unit circle. We prove that under certain conditions, the simple partial fractions of order $N$, with $n$ poles on the unit circle attain minimal norm if and only if the points are equidistributed on the unit circle. We show that this is not the case if the conditions we impose are not met, exhibiting a new interesting phenomeno…

On the Benefits of Accelerated Optimization in Robust and Private Estimation
Laurentiu Andrei Marchis, Po-Ling Loh
https://arxiv.org/abs/2506.03044 https:/…

On the Benefits of Accelerated Optimization in Robust and Private Estimation
We study the advantages of accelerated gradient methods, specifically based on the Frank-Wolfe method and projected gradient descent, for privacy and heavy-tailed robustness. Our approaches are as follows: For the Frank-Wolfe method, our technique is based on a tailored learning rate and a uniform lower bound on the gradient of the $\ell_2$-norm over the constraint set. For accelerating projected gradient descent, we use the popular variant based on Nesterov's momentum, and we optimize our obje…

Lions and Muons: Optimization via Stochastic Frank-Wolfe
Maria-Eleni Sfyraki, Jun-Kun Wang
https://arxiv.org/abs/2506.04192 https://a…

Lions and Muons: Optimization via Stochastic Frank-Wolfe
Stochastic Frank-Wolfe is a classical optimization method for solving constrained optimization problems. On the other hand, recent optimizers such as Lion and Muon have gained quite significant popularity in deep learning. In this work, we provide a unifying perspective by interpreting these seemingly disparate methods through the lens of Stochastic Frank-Wolfe. Specifically, we show that Lion and Muon with weight decay can be viewed as special instances of a Stochastic Frank-Wolfe, and we esta…

Passive Multi-Target Visible Light Positioning Based on Multi-Camera Joint Optimization
Wenxuan Pan, Yang Yang, Dong Wei, Meng Zhang, Zhiyu Zhu
https://arxiv.org/abs/2506.02418

Passive Multi-Target Visible Light Positioning Based on Multi-Camera Joint Optimization
Camera-based visible light positioning (VLP) has emerged as a promising indoor positioning technique. However, the need for dedicated LED infrastructure and on-target cameras in existing algorithms limits their scalability and increases deployment costs. To address these limitations, this letter proposes a passive VLP algorithm based on Multi-Camera Joint Optimization (MCJO). In the considered system, multiple pre-calibrated cameras mounted on the ceiling continuously capture images of position…

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…

MC-PDFT Nuclear Gradients and L-PDFT Energies with Meta and Hybrid Meta On-Top Functionals for Ground- and Excited-State Geometry Optimization and Vertical Excitation Energies
Matthew R. Hennefarth, Younghwan Kim, Bhavnesh Jangid, Jacob Wardzala, Matthew R. Hermes, Donald G. Truhlar, Laura Gagliardi
https://arxiv.org/abs/2506.03…

MC-PDFT Nuclear Gradients and L-PDFT Energies with Meta and Hybrid Meta On-Top Functionals for Ground- and Excited-State Geometry Optimization and Vertical Excitation Energies
Multiconfiguration pair-density functional theory (MC-PDFT) is a post-MCSCF multireference electronic-structure method that explicitly models strong electron correlation, and linearized pair-density functional theory (L-PDFT) is a recently developed multi-state extension that can accurately model conical intersections and locally-avoided crossings. Because MC-PDFT and L-PDFT rely on an on-top energy functional, their accuracy depends on the quality of the on-top functional used. Recent work has…

Data Diversification Methods In Alignment Enhance Math Performance In LLMs
Berkan Dokmeci, Qingyang Wu, Ben Athiwaratkun, Ce Zhang, Shuaiwen Leon Song, James Zou
https://arxiv.org/abs/2507.02173

Data Diversification Methods In Alignment Enhance Math Performance In LLMs
While recent advances in preference learning have enhanced alignment in human feedback, mathematical reasoning remains a persistent challenge. We investigate how data diversification strategies in preference optimization can improve the mathematical reasoning abilities of large language models (LLMs). We evaluate three common data generation methods: temperature sampling, Chain-of-Thought prompting, and Monte Carlo Tree Search (MCTS), and introduce Diversified-ThinkSolve (DTS), a novel structur…

Auto-optimization of Energy Generation for Wave Energy Converters with Active Learning
Siyang Tang, Wen-Hua Chen, Cunjia Liu
https://arxiv.org/abs/2507.01727

Auto-optimization of Energy Generation for Wave Energy Converters with Active Learning
This paper presents an auto-optimization control framework for wave energy converters (WECs) to maximize energy generation under unknown and changing ocean conditions. The proposed control framework consists of two levels. The high-level controller operating at a longer time scale aims to maximize the average energy generation over several wave periods. The generated Power Take-Off (PTO) profile as the reference for the low-level physical system to follow. The new auto-optimization process leve…

This https://arxiv.org/abs/2504.07884 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

CatCMA with Margin: Stochastic Optimization for Continuous, Integer, and Categorical Variables
This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencies among these different types of variables, requiring efficient methods to optimize them simultaneously. Recently, stochastic optimization methods leveraging the mechanism of the covariance matrix adaptation evolution strategy have shown promising results in mixed-integer or mixed-category optimization. However, such …

A trust-region framework for optimization using Hermite kernel surrogate models
Sven Ullmann, Tobias Ehring, Robin Herkert, Bernard Haasdonk
https://arxiv.org/abs/2507.01729

A trust-region framework for optimization using Hermite kernel surrogate models
In this work, we present a trust-region optimization framework that employs Hermite kernel surrogate models. The method targets optimization problems with computationally demanding objective functions, for which direct optimization is often impractical due to expensive function evaluations. To address these challenges, we leverage a trust-region strategy, where the objective function is approximated by an efficient surrogate model within a local neighborhood of the current iterate. In particula…

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…

Extremely Large-Scale Movable Antenna-Enabled Multiuser Communications: Modeling and Optimization
Min Fu, Lipeng Zhu, Rui Zhang
https://arxiv.org/abs/2506.02735

Extremely Large-Scale Movable Antenna-Enabled Multiuser Communications: Modeling and Optimization
Movable antenna (MA) has been recognized as a promising technology to improve communication performance in future wireless networks such as 6G. To unleash its potential, this paper proposes a novel architecture, namely extremely large-scale MA (XL-MA), which allows flexible antenna/subarray positioning over an extremely large spatial region for effectively enhancing near-field effects and spatial multiplexing performance. In particular, this paper studies an uplink XL-MA-enabled multiuser syste…

In-Training Multicalibrated Survival Analysis for Healthcare via Constrained Optimization
Thiti Suttaket, Stanley Kok
https://arxiv.org/abs/2507.02807 http…

In-Training Multicalibrated Survival Analysis for Healthcare via Constrained Optimization
Survival analysis is an important problem in healthcare because it models the relationship between an individual's covariates and the onset time of an event of interest (e.g., death). It is important for survival models to be well-calibrated (i.e., for their predicted probabilities to be close to ground-truth probabilities) because badly calibrated systems can result in erroneous clinical decisions. Existing survival models are typically calibrated at the population level only, and thus run the…

Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes
Vojt\v{e}ch Nov\'ak, Ivan Zelinka, V\'aclav Sn\'a\v{s}el
https://arxiv.org/abs/2506.01715

Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes
Variational Quantum Algorithms (VQAs) are a promising tool in the NISQ era, leveraging quantum computing across diverse fields. However, their performance is hindered by optimization challenges like local minima, barren plateaus, and noise from current quantum hardware. Variational Quantum Eigensolver (VQE), a key subset of VQAs, approximates molecular ground-state energies by minimizing a Hamiltonian, enabling quantum chemistry applications. Beyond this, VQE contributes to condensed matter phy…

HEC: Equivalence Verification Checking for Code Transformation via Equality Saturation
Jiaqi Yin, Zhan Song, Nicolas Bohm Agostini, Antonino Tumeo, Cunxi Yu
https://arxiv.org/abs/2506.02290

HEC: Equivalence Verification Checking for Code Transformation via Equality Saturation
In modern computing systems, compilation employs numerous optimization techniques to enhance code performance. Source-to-source code transformations, which include control flow and datapath transformations, have been widely used in High-Level Synthesis (HLS) and compiler optimization. While researchers actively investigate methods to improve performance with source-to-source code transformations, they often overlook the significance of verifying their correctness. Current tools cannot provide…

Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold
Yilong Song, Peijin Li, Bin Gao, Kun Yuan
https://arxiv.org/abs/2506.02879

Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold
Optimization problems on the Stiefel manifold, ranging from principal component analysis to enhancing neural network robustness, are ubiquitous in machine learning. The Landing algorithm avoids computationally expensive retraction operations on manifolds, making it highly competitive for large-scale problems. This paper extends this method to distributed settings, introducing *EF-Landing*, the first retraction-free and communication-efficient algorithm for distributed stochastic optimization on…

K-order Ranking Preference Optimization for Large Language Models
Shihao Cai, Chongming Gao, Yang Zhang, Wentao Shi, Jizhi Zhang, Keqin Bao, Qifan Wang, Fuli Feng
https://arxiv.org/abs/2506.00441

K-order Ranking Preference Optimization for Large Language Models
To adapt large language models (LLMs) to ranking tasks, existing list-wise methods, represented by list-wise Direct Preference Optimization (DPO), focus on optimizing partial-order or full-order list ranking consistency for LLMs to enhance their ranking abilities. However, we argue that optimizing top-K ranking consistency could be more appropriate for real-world applications. There are two main reasons: (1) users are typically concerned with only the top-K results, making top-K ranking more im…

This https://arxiv.org/abs/2505.23671 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
Developing high-performance software is a complex task that requires specialized expertise. We introduce GSO, a benchmark for evaluating language models' capabilities in developing high-performance software. We develop an automated pipeline that generates and executes performance tests to analyze repository commit histories to identify 102 challenging optimization tasks across 10 codebases, spanning diverse domains and programming languages. An agent is provided with a codebase and performance …

High-speed control and navigation for quadrupedal robots on complex and discrete terrain
Hyeongjun Kim, Hyunsik Oh, Jeongsoo Park, Yunho Kim, Donghoon Youm, Moonkyu Jung, Minho Lee, Jemin Hwangbo
https://arxiv.org/abs/2506.02835

High-speed control and navigation for quadrupedal robots on complex and discrete terrain
High-speed legged navigation in discrete and geometrically complex environments is a challenging task because of the high-degree-of-freedom dynamics and long-horizon, nonconvex nature of the optimization problem. In this work, we propose a hierarchical navigation pipeline for legged robots that can traverse such environments at high speed. The proposed pipeline consists of a planner and tracker module. The planner module finds physically feasible foothold plans by sampling-based optimization wi…

Derivative-Free Optimization-Empowered Wireless Channel Reconfiguration for 6G
Peilan Wang, Jun Fang, Xianlong Zeng, Bin Wang, Zhi Chen, Yonina C. Eldar
https://arxiv.org/abs/2507.02243

Derivative-Free Optimization-Empowered Wireless Channel Reconfiguration for 6G
Reconfigurable antennas, including reconfigurable intelligent surface (RIS), movable antenna (MA), fluid antenna (FA), and other advanced antenna techniques, have been studied extensively in the context of reshaping wireless propagation environments for 6G and beyond wireless communications. Nevertheless, how to reconfigure/optimize the real-time controllable coefficients to achieve a favorable end-to-end wireless channel remains a substantial challenge, as it usually requires accurate modeling…

Human-in-the-loop: Real-time Preference Optimization
Wenbin Wang, Wenjie Xu, Colin N. Jones
https://arxiv.org/abs/2506.02225 https://…

Human-in-the-loop: Real-time Preference Optimization
Human-aware controllers play an important role in engineering systems for improving productivity, efficiency, and sustainability. It is essential to design such a controller that optimizes user utility while adhering to plant dynamics. While most online optimization algorithms rely on first-order or zeroth-order oracles, human feedback often appears as pairwise comparisons. In this work, we propose an online feedback optimization algorithm that leverages such preference feedback. We design a co…

This https://arxiv.org/abs/2503.09977 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Quadratic Transform for Fractional Programming in Signal Processing and Machine Learning
Fractional programming (FP) is a branch of mathematical optimization that deals with the optimization of ratios. It is an invaluable tool for signal processing and machine learning, because many key metrics in these fields are fractionally structured, e.g., the signal-to-interference-plus-noise ratio (SINR) in wireless communications, the Cramér-Rao bound (CRB) in radar sensing, the normalized cut in graph clustering, and the margin in support vector machine (SVM). This article provides a comp…

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs
Purbesh Mitra, Sennur Ulukus
https://arxiv.org/abs/2507.02851 https://a…

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs
Recent advancements in the reasoning capabilities of large language models (LLMs) show that employing group relative policy optimization (GRPO) algorithm for reinforcement learning (RL) training allows the models to use more thinking/reasoning tokens for generating better responses. However, LLMs can generate only a finite amount of tokens while maintaining attention to the previously generated tokens. This limit, also known as the context size of an LLM, is a bottleneck in LLM reasoning with a…

This https://arxiv.org/abs/2505.19381 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i.e. perception, prediction and planing, which enables optimization in pursuit of the ultimate goal. Despite the great potential of the end-to-end paradigm, existing methods suffer from several aspects including expensive BEV (bird's eye view) computation, action diversity, and sub-optimal decision in complex real-world scenarios. To address these challenges, we prop…

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Gjorgjina Cenikj, Ga\v{s}per Petelin, Tome Eftimov
https://arxiv.org/abs/2507.02337

ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Metaheuristic Optimization Algorithms
Understanding the behavior of numerical metaheuristic optimization algorithms is critical for advancing their development and application. Traditional visualization techniques, such as convergence plots, trajectory mapping, and fitness landscape analysis, often fall short in illustrating the structural dynamics of the search process, especially in high-dimensional or complex solution spaces. To address this, we propose a novel representation and visualization methodology that clusters solution …

hqQUBO: A Hybrid-querying Quantum Optimization Model Validated with 16-qubits on an Ion Trap Quantum Computer for Life Science Applications
Rong Chen, Quan-Xin Mei, Wen-Ding Zhao, Lin Yao, Hao-Xiang Yang, Shun-Yao Zhang, Jiao Chen, Hong-Lin Li
https://arxiv.org/abs/2506.01559

hqQUBO: A Hybrid-querying Quantum Optimization Model Validated with 16-qubits on an Ion Trap Quantum Computer for Life Science Applications
AlphaFold has achieved groundbreaking advancements in protein structure prediction, exerting profound influence across biology, medicine, and drug discovery. However, its reliance on multiple sequence alignment (MSA) is inherently time-consuming due to the NP-hard nature of constructing MSAs. Quantum computing emerges as a promising alternative, compared to classical computers, offering the potentials for exponential speedup and improved accuracy on such complex optimization challenges. This wo…

A Preference-Driven Methodology for High-Quality Solidity Code Generation
Zhiyuan Peng, Xin Yin, Chenhao Ying, Chao Ni, Yuan Luo
https://arxiv.org/abs/2506.03006

A Preference-Driven Methodology for High-Quality Solidity Code Generation
While Large Language Models (LLMs) have demonstrated remarkable progress in generating functionally correct Solidity code, they continue to face critical challenges in producing gas-efficient and secure code, which are critical requirements for real-world smart contract deployment. Although recent advances leverage Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for code preference alignment, existing approaches treat functional correctness, gas optimization, and security …

Joint Radiation Power, Antenna Position, and Beamforming Optimization for Pinching-Antenna Systems with Motion Power Consumption
Yiming Xu, Dongfang Xu, Xianghao Yu, Shenghui Song, Zhiguo Ding, Robert Schober
https://arxiv.org/abs/2507.02348

Joint Radiation Power, Antenna Position, and Beamforming Optimization for Pinching-Antenna Systems with Motion Power Consumption
Pinching-antenna systems (PASS) have been recently proposed to improve the performance of wireless networks by reconfiguring both the large-scale and small-scale channel conditions. However, existing studies ignore the physical constraints of antenna placement and assume fixed antenna radiation power. To fill this research gap, this paper investigates the design of PASS taking into account the motion power consumption of pinching-antennas (PAs) and the impact of adjustable antenna radiation pow…

This https://arxiv.org/abs/2504.20618 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Statistical Channel Based Low-Complexity Rotation and Position Optimization for 6D Movable Antennas Enabled Wireless Communication
Six-dimensional movable antenna (6DMA) is a promising technology to fully exploit spatial variation in wireless channels by allowing flexible adjustment of three-dimensional (3D) positions and rotations of antennas at the transceiver. In this paper, we investigate the practical low-complexity design of 6DMA-enabled communication systems, including transmission protocol, statistical channel information (SCI) acquisition, and joint position and rotation optimization of 6DMA surfaces based on the …

Parallel Transmission Aware Co-Design: Enhancing Manipulator Performance Through Actuation-Space Optimization
Rohit Kumar, Melya Boukheddimi, Dennis Mronga, Shivesh Kumar, Frank Kirchner
https://arxiv.org/abs/2507.00644

Parallel Transmission Aware Co-Design: Enhancing Manipulator Performance Through Actuation-Space Optimization
In robotics, structural design and behavior optimization have long been considered separate processes, resulting in the development of systems with limited capabilities. Recently, co-design methods have gained popularity, where bi-level formulations are used to simultaneously optimize the robot design and behavior for specific tasks. However, most implementations assume a serial or tree-type model of the robot, overlooking the fact that many robot platforms incorporate parallel mechanisms. In t…

Two-Stage Bidirectional Inverter Equivalent Circuit Model for Distribution Grid Steady-State Analysis and Optimization
Emmanuel O. Badmus, Amritanshu Pandey
https://arxiv.org/abs/2506.03430

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 …

Learning Parametric Convex Functions
Maximilian Schaller, Alberto Bemporad, Stephen Boyd
https://arxiv.org/abs/2506.04183 https://arx…

Learning Parametric Convex Functions
A parametrized convex function depends on a variable and a parameter, and is convex in the variable for any valid value of the parameter. Such functions can be used to specify parametrized convex optimization problems, i.e., a convex optimization family, in domain specific languages for convex optimization. In this paper we address the problem of fitting a parametrized convex function that is compatible with disciplined programming, to some given data. This allows us to fit a function arising i…

On the Convergence of Large Language Model Optimizer for Black-Box Network Management
Hoon Lee, Wentao Zhou, Merouane Debbah, Inkyu Lee
https://arxiv.org/abs/2507.02689

On the Convergence of Large Language Model Optimizer for Black-Box Network Management
Future wireless networks are expected to incorporate diverse services that often lack general mathematical models. To address such black-box network management tasks, the large language model (LLM) optimizer framework, which leverages pretrained LLMs as optimization agents, has recently been promoted as a promising solution. This framework utilizes natural language prompts describing the given optimization problems along with past solutions generated by LLMs themselves. As a result, LLMs can ob…

Multi-Revolution Low-Thrust Trajectory Optimization With Very Sparse Mesh Pseudospectral Method
Yilin Zou, Fanghua Jiang
https://arxiv.org/abs/2507.01450 h…

Multi-Revolution Low-Thrust Trajectory Optimization With Very Sparse Mesh Pseudospectral Method
Multi-revolution low-thrust trajectory optimization problems are important and challenging in space mission design. In this paper, an efficient, accurate, and widely applicable pseudospectral method is proposed to solve multi-revolution low-thrust trajectory optimization problems with various objective functions and perturbations. The method is based on the Sundman transformation and pseudospectral method, together with a sparse mesh that is monotonic, near-uniformly spaced, and uniformly scatt…

This https://arxiv.org/abs/2504.19583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning
This paper proposes a parameter collaborative optimization algorithm for large language models, enhanced with graph spectral analysis. The goal is to improve both fine-tuning efficiency and structural awareness during training. In the proposed method, the parameters of a pre-trained language model are treated as nodes in a graph. A weighted graph is constructed, and Laplacian spectral decomposition is applied to enable frequency-domain modeling and structural representation of the parameter spa…

Quality Diversity Genetic Programming for Learning Scheduling Heuristics
Meng Xu, Frank Neumann, Aneta Neumann, Yew Soon Ong
https://arxiv.org/abs/2507.02235

Quality Diversity Genetic Programming for Learning Scheduling Heuristics
Real-world optimization often demands diverse, high-quality solutions. Quality-Diversity (QD) optimization is a multifaceted approach in evolutionary algorithms that aims to generate a set of solutions that are both high-performing and diverse. QD algorithms have been successfully applied across various domains, providing robust solutions by exploring diverse behavioral niches. However, their application has primarily focused on static problems, with limited exploration in the context of dynami…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Low-rank optimization methods based on projected-projected gradient descent that accumulate at Bo...
Guillaume Olikier, Kyle A. Gallivan, P. -A. Absil

Random-key genetic algorithms
Mariana A. Londe, Luciana S. Pessoa, Carlos E. Andrade, Jos\'e F. Gon\c{c}alves, Mauricio G. C. Resende
https://arxiv.org/abs/2506.02120

Random-key genetic algorithms
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regar…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[2/2]:
- optimizn: a Python Library for Developing Customized Optimization Algorithms
Akshay Sathiya, Rohit Pandey

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/6]:
- Non-Convex Optimization with Spectral Radius Regularization
Adam Sandler, Diego Klabjan, Yuan Luo

Multi-Objective Neural Network Assisted Design Optimization of Soft Fin-Ray Grippers for Enhanced Grasping Performance
Ali Ghanizadeh, Ali Ahmadi, Arash Bahrami
https://arxiv.org/abs/2506.00494

Multi-Objective Neural Network Assisted Design Optimization of Soft Fin-Ray Grippers for Enhanced Grasping Performance
Soft Fin-Ray grippers can perform delicate and careful manipulation, which has caused notable attention in different fields. These grippers can handle objects of various forms and sizes safely. The internal structure of the Fin-Ray finger plays a significant role in its adaptability and grasping performance. However, modeling the non-linear grasp force and deformation behaviors for design purposes is challenging. Moreover, when the Fin-Ray finger becomes more rigid and capable of exerting highe…

Bi-Level optimization for parameter estimation of differential equations using interpolation
Siddharth Prabhu, Srinivas Rangarajan, Mayuresh Kothare
https://arxiv.org/abs/2506.00720

Bi-Level optimization for parameter estimation of differential equations using interpolation
Inverse problem or parameter estimation of ordinary differential equations is a process of obtaining the best parameters using experimental measurements of the states. Single (Multiple)-shooting is a type of sequential optimization method that minimizes the error in the measured and numerically integrated states. However, this requires computing sensitivities i.e. the derivatives of states with respect to the parameters over the numerical integrator, which can get computationally expensive. To …

This https://arxiv.org/abs/2506.02120 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Random-key genetic algorithms
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regar…

Online Convex Optimization for Coordinated Long-Term and Short-Term Isolated Microgrid Dispatch
Ning Qi, Yousuf Baker, Bolun Xu
https://arxiv.org/abs/2507.02636

Online Convex Optimization for Coordinated Long-Term and Short-Term Isolated Microgrid Dispatch
This paper proposes a novel non-anticipatory long-short-term coordinated dispatch framework for isolated microgrid with hybrid short-long-duration energy storages (LDES). We introduce a convex hull approximation model for nonconvex LDES electrochemical dynamics, facilitating computational tractability and accuracy. To address temporal coupling in SoC dynamics and long-term contracts, we generate hindsight-optimal state-of-charge (SoC) trajectories of LDES and netloads for offline training. In t…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- optimizn: a Python Library for Developing Customized Optimization Algorithms
Akshay Sathiya, Rohit Pandey

A primal-dual price-optimization method for computing equilibrium prices in mean-field games models
Xu Wang, Samy Wu Fung, Levon Nurbekyan
https://arxiv.org/abs/2506.04169

A primal-dual price-optimization method for computing equilibrium prices in mean-field games models
We develop a simple yet efficient Lagrangian method for computing equilibrium prices in a mean-field game price-formation model. We prove that equilibrium prices are optimal in terms of a suitable criterion and derive a primal-dual gradient-based algorithm for computing them. One of the highlights of our computational framework is the efficient, simple, and flexible implementation of the algorithm using modern automatic differentiation techniques. Our implementation is modular and admits a seam…

Smooth Logic Constraints in Nonlinear Optimization and Optimal Control Problems
J. Wehbeh, E. C. Kerrigan
https://arxiv.org/abs/2506.01742 https://

Smooth Logic Constraints in Nonlinear Optimization and Optimal Control Problems
In some optimal control problems, complex relationships between states and inputs cannot be easily represented using continuous constraints, necessitating the use of discrete logic instead. This paper presents a method for incorporating such logic constraints directly within continuous optimization frameworks, eliminating the need for binary variables or specialized solvers. Our approach reformulates arbitrary logic constraints under minimal assumptions as max-min constraints, which are then sm…

This https://arxiv.org/abs/2505.01892 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

OODTE: A Differential Testing Engine for the ONNX Optimizer
With over 700 stars on GitHub and being part of the official ONNX repository, the ONNX Optimizer is the default tool for applying graph-based optimizations to ONNX models. Despite its widespread use, its ability to maintain model accuracy during optimization has not been thoroughly investigated. In this work, we present OODTE, a utility designed to automatically and comprehensively evaluate the correctness of the ONNX Optimizer. OODTE adopts a straightforward yet powerful differential testing a…

Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis
Gjorgjina Cenikj, Ga\v{s}per Petelin, Tome Eftimov
https://arxiv.org/abs/2507.01668

Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis
The field of numerical optimization has recently seen a surge in the development of "novel" metaheuristic algorithms, inspired by metaphors derived from natural or human-made processes, which have been widely criticized for obscuring meaningful innovations and failing to distinguish themselves from existing approaches. Aiming to address these concerns, we investigate the applicability of statistical tests for comparing algorithms based on their search behavior. We utilize the cross-match statis…

Multilevel Bregman Proximal Gradient Descent
Yara Elshiaty, Stefania Petra
https://arxiv.org/abs/2506.03950 https://arxiv.org/pdf/250…

Multilevel Bregman Proximal Gradient Descent
We present the Multilevel Bregman Proximal Gradient Descent (ML-BPGD) method, a novel multilevel optimization framework tailored to constrained convex problems with relative Lipschitz smoothness. Our approach extends the classical multilevel optimization framework (MGOPT) to handle Bregman-based geometries and constrained domains. We provide a rigorous analysis of ML BPGD for multiple coarse levels and establish a global linear convergence rate. We demonstrate the effectiveness of ML BPGD in th…

Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance
Ana Nikolikj, Gabriela Ochoa, Tome Eftimov
https://arxiv.org/abs/2507.01638

Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance
We present an analysis of landscape features for predicting the performance of multi-objective combinatorial optimization algorithms. We consider features from the recently proposed compressed Pareto Local Optimal Solutions Networks (C-PLOS-net) model of combinatorial landscapes. The benchmark instances are a set of rmnk-landscapes with 2 and 3 objectives and various levels of ruggedness and objective correlation. We consider the performance of three algorithms -- Pareto Local Search (PLS), Glo…

Efficient Quadratic Corrections for Frank-Wolfe Algorithms
Jannis Halbey, Seta Rakotomandimby, Mathieu Besan\c{c}on, Sebastian Pokutta
https://arxiv.org/abs/2506.02635

Efficient Quadratic Corrections for Frank-Wolfe Algorithms
We develop a Frank-Wolfe algorithm with corrective steps, generalizing previous algorithms including blended pairwise conditional gradients and fully-corrective Frank-Wolfe, and propose a highly efficient corrective algorithm in the case of convex quadratic objectives based on linear optimization or linear system solving, akin to Wolfe's minimum-norm point. Beyond optimization problems that are directly quadratic, we revisit two algorithms, split conditional gradient for the intersection of two…

Regionalized Metric Framework: A Novel Approach for Evaluating Multimodal Multi-Objective Optimization Algorithms
Jintai Chen, Fangqing Liu, Xueming Yan, Han Huang
https://arxiv.org/abs/2506.00468

Regionalized Metric Framework: A Novel Approach for Evaluating Multimodal Multi-Objective Optimization Algorithms
This study aims to optimize the evaluation metric of multimodal multi-objective optimization problems using a Regionalized Metric Framework, which provides a certain boost to research in this field. Existing evaluation metrics usually use the reference set as the evaluation basis, which inevitably leads to reference set dependence. To optimize this problem, this study proposes an evaluation metric based on a Regionalized Metric Framework. The algorithm divides the set of solutions to be evaluat…

A learning model predictive control for virtual coupling in railroads
Miguel A. Vaquero-Serrano, Francesco Borrelli, Jesus Felez
https://arxiv.org/abs/2507.02383

A learning model predictive control for virtual coupling in railroads
The objective of this paper is to present a novel intelligent train control system for virtual coupling in railroads based on a Learning Model Predictive Control (LMPC). Virtual coupling is an emerging railroad technology that reduces the distance between trains to increase the capacity of the line, whereas LMPC is an optimization-based controller that incorporates artificial intelligence methods to improve its control policies. By incorporating data from past experiences into the optimization …

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Vu Thi Huong, Ida Litzel, Thorsten Koch
https://arxiv.org/abs/2506.04045

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive dat…

Swarm-based optimization with jumps: a kinetic BGK framework and convergence analysis
Giacomo Borghi, Hyesung Im, Lorenzo Pareschi
https://arxiv.org/abs/2507.00871

Swarm-based optimization with jumps: a kinetic BGK framework and convergence analysis
Metaheuristic algorithms are powerful tools for global optimization, particularly for non-convex and non-differentiable problems where exact methods are often impractical. Particle-based optimization methods, inspired by swarm intelligence principles, have shown effectiveness due to their ability to balance exploration and exploitation within the search space. In this work, we introduce a novel particle-based optimization algorithm where velocities are updated via random jumps, a strategy commo…

Randomized subspace correction methods for convex optimization
Boou Jiang, Jongho Park, Jinchao Xu
https://arxiv.org/abs/2507.01415 https://

Randomized subspace correction methods for convex optimization
This paper introduces an abstract framework for randomized subspace correction methods for convex optimization, which unifies and generalizes a broad class of existing algorithms, including domain decomposition, multigrid, and block coordinate descent methods. We provide a convergence rate analysis ranging from minimal assumptions to more practical settings, such as sharpness and strong convexity. While most existing studies on block coordinate descent methods focus on nonoverlapping decomposit…

This https://arxiv.org/abs/2502.17982 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Kinetic variable-sample methods for stochastic optimization problems
We discuss kinetic-based particle optimization methods and variable-sample strategies for problems where the cost function represents the expected value of a random mapping. Kinetic-based optimization methods rely on a consensus mechanism targeting the global minimizer, and they exploit tools of kinetic theory to establish a rigorous framework for proving convergence to that minimizer. Variable-sample strategies replace the expected value by an approximation at each iteration of the optimizatio…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/2]:
- Conditional Gradient Methods
Braun, Carderera, Combettes, Hassani, Karbasi, Mokhtari, Pokutta

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
Tim Roith, Leon Bungert, Philipp Wacker
https://arxiv.org/abs/2506.24048

Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
Consensus-based optimization (CBO) has established itself as an efficient gradient-free optimization scheme, with attractive mathematical properties, such as mean-field convergence results for non-convex loss functions. In this work, we study CBO in the context of closed-box adversarial attacks, which are imperceptible input perturbations that aim to fool a classifier, without accessing its gradient. Our contribution is to establish a connection between the so-called consensus hopping as introd…

Random Distributionally Robust Optimization under Phi-divergence
Guohui Guan, Zongxia Liang, Xingjian Ma
https://arxiv.org/abs/2506.23839 https://

Random Distributionally Robust Optimization under Phi-divergence
This paper introduces a novel framework, Random Distributionally Robust Optimization (RDRO), which extends classical Distributionally Robust Optimization (DRO) by allowing the decision variable to be a random variable. We formulate the RDRO problem using a bivariate utility function and $φ$-divergence ambiguity sets, enabling a more flexible and realistic treatment of uncertainty. The RDRO framework encompasses a broad range of robust decision-making applications, including portfolio optimizat…

Optimized methods for composite optimization: a reduction perspective
Jinho Bok, Jason M. Altschuler
https://arxiv.org/abs/2506.23756 https://

Optimized methods for composite optimization: a reduction perspective
Recent advances in convex optimization have leveraged computer-assisted proofs to develop optimized first-order methods that improve over classical algorithms. However, each optimized method is specially tailored for a particular problem setting, and it is a well-documented challenge to extend optimized methods to other settings due to their highly bespoke design and analysis. We provide a general framework that derives optimized methods for composite optimization directly from those for uncons…

Data-Driven Performance Guarantees for Parametric Optimization Problems
Jingyi Huang, Paul Goulart, Kostas Margellos
https://arxiv.org/abs/2506.23819 https…

Data-Driven Performance Guarantees for Parametric Optimization Problems
We propose a data-driven method to establish probabilistic performance guarantees for parametric optimization problems solved via iterative algorithms. Our approach addresses two key challenges: providing convergence guarantees to characterize the worst-case number of iterations required to achieve a predefined tolerance, and upper bounding a performance metric after a fixed number of iterations. These guarantees are particularly useful for online optimization problems with limited computationa…