Tootfinder

Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation
Thorbj{\o}rn Mosekj{\ae}r Iversen, Lars Car{\o}e S{\o}rensen, Simon Faarvang Mathiesen, Henrik Gordon Petersen
https://arxiv.org/abs/2509.09238

Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation
Many optimization problems in robotics involve the optimization of time-expensive black-box functions, such as those involving complex simulations or evaluation of real-world experiments. Furthermore, these functions are often stochastic as repeated experiments are subject to unmeasurable disturbances. Bayesian optimization can be used to optimize such methods in an efficient manner by deploying a probabilistic function estimator to estimate with a given confidence so that regions of the search…

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
Yueyang Quan, Chang Wang, Shengjie Zhai, Minghong Fang, Zhuqing Liu
https://arxiv.org/abs/2508.07505

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
Decentralized min-max optimization allows multi-agent systems to collaboratively solve global min-max optimization problems by facilitating the exchange of model updates among neighboring agents, eliminating the need for a central server. However, sharing model updates in such systems carry a risk of exposing sensitive data to inference attacks, raising significant privacy concerns. To mitigate these privacy risks, differential privacy (DP) has become a widely adopted technique for safeguarding…

Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning
Bingning Huang, Tu Nguyen, Matthieu Zimmer
https://arxiv.org/abs/2509.09284 https://

Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning
Recent advances in reasoning with large language models (LLMs) have shown the effectiveness of Monte Carlo Tree Search (MCTS) for generating high-quality intermediate trajectories, particularly in math and symbolic domains. Inspired by this, we explore how MCTS-derived trajectories, traditionally used for training value or reward models, can be repurposed to improve policy optimization in preference-based reinforcement learning (RL). Specifically, we focus on Group Relative Policy Optimization …

First-order equivalent static loads for dynamic response structural optimization
Mordechay Buzaglo, Nicol\`o Pollini
https://arxiv.org/abs/2508.07222 https://

First-order equivalent static loads for dynamic response structural optimization
A novel first-order equivalent static loads approach for optimization of structural dynamic response, F-ESL, is presented and compared to the basic equivalent static load formulation, ESL. F-ESL simplifies dynamic optimization problems by converting them into a series of static optimization sub-problems. The ESL algorithm in its original formulation does not have a guaranteed capability of reaching, or recognizing, final designs that satisfy necessary first-order optimality conditions. F-ESL ad…

Minimal Data, Maximum Clarity: A Heuristic for Explaining Optimization
Amirali Rayegan, Tim Menzies
https://arxiv.org/abs/2509.08667 https://arxiv.org/pdf/…

Minimal Data, Maximum Clarity: A Heuristic for Explaining Optimization
Efficient, interpretable optimization is a critical but underexplored challenge in software engineering, where practitioners routinely face vast configuration spaces and costly, error-prone labeling processes. This paper introduces EZR, a novel and modular framework for multi-objective optimization that unifies active sampling, learning, and explanation within a single, lightweight pipeline. Departing from conventional wisdom, our Maximum Clarity Heuristic demonstrates that using less (but more…

Qubit-Efficient QUBO Formulation for Constrained Optimization Problems
Meerzhan Kantbekova, Vincenzo De Maio, Ivona Brandic
https://arxiv.org/abs/2509.08080 https://

Qubit-Efficient QUBO Formulation for Constrained Optimization Problems
Quantum computing has emerged as a promising alternative for solving combinatorial optimization problems. The standard approach for encoding optimization problems on quantum processing units (QPUs) involves transforming them into their Quadratic Unconstrained Binary Optimization (QUBO) representation. However, encoding constraints of optimization problems, particularly inequality constraints, into QUBO requires additional variables, which results in more qubits. Considering the limited availabi…

Generative Engine Optimization: How to Dominate AI Search
Mahe Chen, Xiaoxuan Wang, Kaiwen Chen, Nick Koudas
https://arxiv.org/abs/2509.08919 https://arxiv…

Generative Engine Optimization: How to Dominate AI Search
The rapid adoption of generative AI-powered search engines like ChatGPT, Perplexity, and Gemini is fundamentally reshaping information retrieval, moving from traditional ranked lists to synthesized, citation-backed answers. This shift challenges established Search Engine Optimization (SEO) practices and necessitates a new paradigm, which we term Generative Engine Optimization (GEO). This paper presents a comprehensive comparative analysis of AI Search and traditional web search (Google). Thro…

Towards Verified Compilation of Floating-point Optimization in Scientific Computing Programs
Mohit Tekriwal, John Sarracino
https://arxiv.org/abs/2509.09019 https://

Towards Verified Compilation of Floating-point Optimization in Scientific Computing Programs
Scientific computing programs often undergo aggressive compiler optimization to achieve high performance and efficient resource utilization. While performance is critical, we also need to ensure that these optimizations are correct. In this paper, we focus on a specific class of optimizations, floating-point optimizations, notably due to fast math, at the LLVM IR level. We present a preliminary work, which leverages the Verified LLVM framework in the Rocq theorem prover, to prove the correctnes…

Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices
Kevin Pack, Shai Machnes, Frank K. Wilhelm
https://arxiv.org/abs/2509.08555 https://

Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices
We present the results of a comprehensive study of optimization algorithms for the calibration of quantum devices. As part of our ongoing efforts to automate bring-up, tune-up, and system identification procedures, we investigate a broad range of optimizers within a simulated environment designed to closely mimic the challenges of real-world experimental conditions. Our benchmark includes widely used algorithms such as Nelder-Mead and the state-of-the-art Covariance Matrix Adaptation Evolution …

Reinforcement Learning in Vision: A Survey
Weijia Wu, Chen Gao, Joya Chen, Kevin Qinghong Lin, Qingwei Meng, Yiming Zhang, Yuke Qiu, Hong Zhou, Mike Zheng Shou
https://arxiv.org/abs/2508.08189

Reinforcement Learning in Vision: A Survey
Recent advances at the intersection of reinforcement learning (RL) and visual intelligence have enabled agents that not only perceive complex visual scenes but also reason, generate, and act within them. This survey offers a critical and up-to-date synthesis of the field. We first formalize visual RL problems and trace the evolution of policy-optimization strategies from RLHF to verifiable reward paradigms, and from Proximal Policy Optimization to Group Relative Policy Optimization. We then org…

PAnDA: Rethinking Metric Differential Privacy Optimization at Scale with Anchor-Based Approximation
Ruiyao Liu, Chenxi Qiu
https://arxiv.org/abs/2509.08720 https://

PAnDA: Rethinking Metric Differential Privacy Optimization at Scale with Anchor-Based Approximation
Metric Differential Privacy (mDP) extends the local differential privacy (LDP) framework to metric spaces, enabling more nuanced privacy protection for data such as geo-locations. However, existing mDP optimization methods, particularly those based on linear programming (LP), face scalability challenges due to the quadratic growth in decision variables. In this paper, we propose Perturbation via Anchor-based Distributed Approximation (PAnDA), a scalable two-phase framework for optimizing metric…

Isogeometric Topology Optimization Based on Topological Derivatives
Guilherme Henrique Teixeira, Nepomuk Krenn, Peter Gangl, Benjamin Marussig
https://arxiv.org/abs/2509.09236 h…

Isogeometric Topology Optimization Based on Topological Derivatives
Topology optimization is a valuable tool in engineering, facilitating the design of optimized structures. However, topological changes often require a remeshing step, which can become challenging. In this work, we propose an isogeometric approach to topology optimization driven by topological derivatives. The combination of a level-set method together with an immersed isogeometric framework allows seamless geometry updates without the necessity of remeshing. At the same time, topological deriva…

Human-in-the-Loop Optimization with Model-Informed Priors
Yi-Chi Liao, Jo\~ao Belo, Hee-Seung Moon, J\"urgen Steimle, Anna Maria Feit
https://arxiv.org/abs/2510.07754 https…

Human-in-the-Loop Optimization with Model-Informed Priors
Human-in-the-loop optimization identifies optimal interface designs by iteratively observing user performance. However, it often requires numerous iterations due to the lack of prior information. While recent approaches have accelerated this process by leveraging previous optimization data, collecting user data remains costly and often impractical. We present a conceptual framework, Human-in-the-Loop Optimization with Model-Informed Priors (HOMI), which augments human-in-the-loop optimization w…

Memory Enhanced Fractional-Order Dung Beetle Optimization for Photovoltaic Parameter Identification
Yiwei Li, Zhihua Allen-Zhao, Yuncheng Xu, Sanyang Liu
https://arxiv.org/abs/2508.06841

Memory Enhanced Fractional-Order Dung Beetle Optimization for Photovoltaic Parameter Identification
Accurate parameter identification in photovoltaic (PV) models is crucial for performance evaluation but remains challenging due to their nonlinear, multimodal, and high-dimensional nature. Although the Dung Beetle Optimization (DBO) algorithm has shown potential in addressing such problems, it often suffers from premature convergence. To overcome these issues, this paper proposes a Memory Enhanced Fractional-Order Dung Beetle Optimization (MFO-DBO) algorithm that integrates three coordinated st…

Joint Beamforming Optimization for Pinching-Antenna Systems (PASS)-assisted Symbiotic Radio
Ze Wang, Guoping Zhang, Hongbo Xu
https://arxiv.org/abs/2508.07002 https://

Joint Beamforming Optimization for Pinching-Antenna Systems (PASS)-assisted Symbiotic Radio
This paper investigates a novel downlink symbiotic radio (SR) framework empowered by the pinching antenna system (PASS), aiming to enhance both primary and secondary transmissions through reconfigurable antenna positioning. PASS consists of multiple waveguides equipped with numerous low-cost pinching antennas (PAs), whose positions can be flexibly adjusted to simultaneously manipulate large-scale path loss and signal phases.We formulate a joint transmit and pinching beamforming optimization pro…

Single-frame super-resolution via Sparse Point Optimization
Xiaofeng Zhang, Yongsheng Huang, Jielong Yang, Zhili Wang, Si Chen, Linbo Liu, Xin Ge
https://arxiv.org/abs/2509.08730

Single-frame super-resolution via Sparse Point Optimization
Fluorescence microscopy is essential in biological and medical research, providing critical insights into cellular structures. However, limited by optical diffraction and background noise, a substantial amount of hidden information is still unexploited. To address these challenges, we introduce a novel computational method, termed Sparse Point Optimization Theory (SPOT), which accurately localizes fluorescent emitters by solving an optimization problem. Our results demonstrate that SPOT success…

Material Fingerprinting: A shortcut to material model discovery without solving optimization problems
Moritz Flaschel, Denisa Martonov\'a, Carina Veil, Ellen Kuhl
https://arxiv.org/abs/2508.07831

Material Fingerprinting: A shortcut to material model discovery without solving optimization problems
We propose Material Fingerprinting, a new method for the rapid discovery of mechanical material models from direct or indirect data that avoids solving potentially non-convex optimization problems. The core assumption of Material Fingerprinting is that each material exhibits a unique response when subjected to a standardized experimental setup. We can interpret this response as the material's fingerprint, essentially a unique identifier that encodes all pertinent information about the material'…

Kinetostatics and Particle-Swarm Optimization of Vehicle-Mounted Underactuated Metamorphic Loading Manipulators
Nan Mao, Guanglu Jia, Junpeng Chen, Emmanouil Spyrakos-Papastavridis, Jian S. Dai
https://arxiv.org/abs/2509.09093

Kinetostatics and Particle-Swarm Optimization of Vehicle-Mounted Underactuated Metamorphic Loading Manipulators
Fixed degree-of-freedom (DoF) loading mechanisms often suffer from excessive actuators, complex control, and limited adaptability to dynamic tasks. This study proposes an innovative mechanism of underactuated metamorphic loading manipulators (UMLM), integrating a metamorphic arm with a passively adaptive gripper. The metamorphic arm exploits geometric constraints, enabling the topology reconfiguration and flexible motion trajectories without additional actuators. The adaptive gripper, driven en…

ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System
Dong Han, Zhehong Ai, Pengxiang Cai, Shuzhou Sun, Shanya Lu, Jianpeng Chen, Ben Gao, Lingli Ge, Weida Wang, Xiangxin Zhou, Xihui Liu, Mao Su, Wanli Ouyang, Lei Bai, Dongzhan Zhou, Tao XU, Yuqiang Li, Shufei Zhang
https://arxiv.org/abs/2509.08736

ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System
The efficiency of Bayesian optimization (BO) in chemistry is often hindered by sparse experimental data and complex reaction mechanisms. To overcome these limitations, we introduce ChemBOMAS, a new framework named LLM-Enhanced Multi-Agent System for accelerating BO in chemistry. ChemBOMAS's optimization process is enhanced by LLMs and synergistically employs two strategies: knowledge-driven coarse-grained optimization and data-driven fine-grained optimization. First, in the knowledge-driven coa…

THz/RF Multi-Hop Routing Throughput: Performance, Optimization, and Application
Zhengying Lou, Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini
https://arxiv.org/abs/2508.06975

THz/RF Multi-Hop Routing Throughput: Performance, Optimization, and Application
Terahertz (THz) communication offers a promising solution for high-throughput wireless systems. However, the severe path loss of THz signals raises concerns about its effectiveness compared to radio frequency (RF) communication. In this article, we establish the first stochastic geometry (SG)-based analytical framework for routing in THz systems. We develop a stepwise optimization approach to maximize throughput, including power allocation, relay selection, and number of hops design. Analytical…

Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems
Xintong Chen, Zhenyu Jiang, Jiangbin Lyu, Liqun Fu
https://arxiv.org/abs/2508.07009

Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems
Intelligent Reflecting Surfaces (IRSs) have potential for significant performance gains in next-generation wireless networks but face key challenges, notably severe double-pathloss and complex multi-user scheduling due to hardware constraints. Active IRSs partially address pathloss but still require efficient scheduling in cell-level multi-IRS multi-user systems, whereby the overhead/delay of channel state acquisition and the scheduling complexity both rise dramatically as the user density and …

Global Nonconvex Optimization with Integer Variables
Dimitris Bertsimas, Danique de Moor, Thodoris Koukouvinos, Demetrios Kriezis
https://arxiv.org/abs/2508.07018 https://

Global Nonconvex Optimization with Integer Variables
Nonconvex optimization refers to the process of solving problems whose objective or constraints are nonconvex. Historically, this type of problems have been very difficult to solve to global optimality, with traditional solvers often relying on approximate solutions. Bertsimas et al. introduce a novel approach for solving continuous nonconvex optimization problems to provable optimality, called the Relaxation Perspectification Technique - Branch and Bound (RPT-BB). In this paper, we extend the …

Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
Haoran Wu, Can Xiao, Jiayi Nie, Xuan Guo, Binglei Lou, Jeffrey T. H. Wong, Zhiwen Mo, Cheng Zhang, Przemyslaw Forys, Wayne Luk, Hongxiang Fan, Jianyi Cheng, Timothy M. Jones, Rika Antonova, Robert Mullins, Aaron Zhao
https://arxiv.org/abs/2509.09505

Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
LLMs now form the backbone of AI agents for a diverse array of applications, including tool use, command-line agents, and web or computer use agents. These agentic LLM inference tasks are fundamentally different from chatbot-focused inference -- they often have much larger context lengths to capture complex, prolonged inputs, such as entire webpage DOMs or complicated tool call trajectories. This, in turn, generates significant off-chip memory traffic for the underlying hardware at the inferenc…

Joint Optimization of Computation Offloading and Resource Allocation in ISAC-assisted SAGIN-based IoT
Sooyeob Jung, Seongah Jeong, Jinkyu Kang
https://arxiv.org/abs/2509.08238 h…

Joint Optimization of Computation Offloading and Resource Allocation in ISAC-assisted SAGIN-based IoT
In this letters, an energy-efficient integrated sensing and communication (ISAC) for space-air-ground integrated network (SAGIN)-based Internet of Things (IoT) systems is proposed to facilitate wide coverage and real-time 6G services. For processing a sizable data collected at a IoT device, a hybrid edge computing scheme is applied with the cloudlets mounted at autonomous aerial vehicle (AAV) and low earth orbit (LEO) satellite, where the AAV with multiple antennas performs uplink sensing of th…

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework
Zitao Wang, Nian Si, Molei Liu
https://arxiv.org/abs/2509.09371 https://

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework
We propose REpresentation-Aware Distributionally Robust Estimation (READ), a novel framework for Wasserstein distributionally robust learning that accounts for predictive representations when guarding against distributional shifts. Unlike classical approaches that treat all feature perturbations equally, READ embeds a multidimensional alignment parameter into the transport cost, allowing the model to differentially discourage perturbations along directions associated with informative representa…

Nonparametric Reaction Coordinate Optimization with Histories: A Framework for Rare Event Dynamics
Polina V. Banushkina, Sergei V. Krivov
https://arxiv.org/abs/2508.07326 https:…

Nonparametric Reaction Coordinate Optimization with Histories: A Framework for Rare Event Dynamics
Rare but critical events in complex systems, such as protein folding, chemical reactions, disease progression, and extreme weather or climate phenomena, are governed by complex, high-dimensional, stochastic dynamics. Identifying an optimal reaction coordinate (RC) that accurately captures the progress of these dynamics is crucial for understanding and simulating such processes. This work introduces a nonparametric RC optimization framework that incorporates trajectory histories, enabling robust…

Optimization of geometric hypergraph embedding
Francesco Zigliotto, Desmond J. Higham
https://arxiv.org/abs/2509.08772 https://arxiv.org/pdf/2509.08772

Optimization of geometric hypergraph embedding
We consider the problem of embedding the nodes of a hypergraph into Euclidean space under the assumption that the interactions arose through closeness to unknown hyperedge centres. In this way, we tackle the inverse problem associated with the generation of geometric random hypergraphs. We propose two new spectral algorithms; both of these exploit the connection between hypergraphs and bipartite graphs. The assumption of an underlying geometric structure allows us to define a concrete measure o…

Causal PDE-Control Models: A Structural Framework for Dynamic Portfolio Optimization
Alejandro Rodriguez Dominguez
https://arxiv.org/abs/2509.09585 https://

Causal PDE-Control Models: A Structural Framework for Dynamic Portfolio Optimization
Classical portfolio models collapse under structural breaks, while modern machine-learning allocators adapt flexibly but often at the cost of transparency and interpretability. This paper introduces Causal PDE-Control Models (CPCMs), a unifying framework that integrates causal inference, nonlinear filtering, and forward-backward partial differential equations for dynamic portfolio optimization. The framework delivers three theoretical advances: (i) the existence of conditional risk-neutral meas…

Towards solving industrial integer linear programs with Decoded Quantum Interferometry
Francesc Sabater, Ouns El Harzli, Geert-Jan Besjes, Marvin Erdmann, Johannes Klepsch, Jonas Hiltrop, Jean-Francois Bobier, Yudong Cao, Carlos A. Riofrio
https://arxiv.org/abs/2509.08328

Towards solving industrial integer linear programs with Decoded Quantum Interferometry
Optimization via decoded quantum interferometry (DQI) has recently gained a great deal of attention as a promising avenue for solving optimization problems using quantum computers. In this paper, we apply DQI to an industrial optimization problem in the automotive industry: the vehicle option-package pricing problem. Our main contributions are 1) formulating the industrial problem as an integer linear program (ILP), 2) converting the ILP into instances of max-XORSAT, and 3) developing a detaile…

Symmetry-Aware Fully-Amortized Optimization with Scale Equivariant Graph Metanetworks
Bart Kuipers, Freek Byrman, Daniel Uyterlinde, Alejandro Garc\'ia-Castellanos
https://arxiv.org/abs/2510.08300 …

Symmetry-Aware Fully-Amortized Optimization with Scale Equivariant Graph Metanetworks
Amortized optimization accelerates the solution of related optimization problems by learning mappings that exploit shared structure across problem instances. We explore the use of Scale Equivariant Graph Metanetworks (ScaleGMNs) for this purpose. By operating directly in weight space, ScaleGMNs enable single-shot fine-tuning of existing models, reducing the need for iterative optimization. We demonstrate the effectiveness of this approach empirically and provide a theoretical result: the gauge …

Astra: A Multi-Agent System for GPU Kernel Performance Optimization
Anjiang Wei, Tianran Sun, Yogesh Seenichamy, Hang Song, Anne Ouyang, Azalia Mirhoseini, Ke Wang, Alex Aiken
https://arxiv.org/abs/2509.07506

Astra: A Multi-Agent System for GPU Kernel Performance Optimization
GPU kernel optimization has long been a central challenge at the intersection of high-performance computing and machine learning. Efficient kernels are crucial for accelerating large language model (LLM) training and serving, yet attaining high performance typically requires extensive manual tuning. Compiler-based systems reduce some of this burden, but still demand substantial manual design and engineering effort. Recently, researchers have explored using LLMs for GPU kernel generation, though…

from my link log —
cargo-remark: view LLVM optimization remarks for Rust.
https://github.com/Kobzol/cargo-remark
saved 2025-08-10 https://…

Online Convex Optimization with Heavy Tails: Old Algorithms, New Regrets, and Applications
Zijian Liu
https://arxiv.org/abs/2508.07473 https://arxiv.org/pd…

Online Convex Optimization with Heavy Tails: Old Algorithms, New Regrets, and Applications
In Online Convex Optimization (OCO), when the stochastic gradient has a finite variance, many algorithms provably work and guarantee a sublinear regret. However, limited results are known if the gradient estimate has a heavy tail, i.e., the stochastic gradient only admits a finite $\mathsf{p}$-th central moment for some $\mathsf{p}\in\left(1,2\right]$. Motivated by it, this work examines different old algorithms for OCO (e.g., Online Gradient Descent) in the more challenging heavy-tailed settin…

A multi-strategy improved gazelle optimization algorithm for solving numerical optimization and engineering applications
Qi Diao, Chengyue Xie, Yuchen Yin, Hoileong Lee, Haolong Yang
https://arxiv.org/abs/2509.07211

A multi-strategy improved gazelle optimization algorithm for solving numerical optimization and engineering applications
Aiming at the shortcomings of the gazelle optimization algorithm, such as the imbalance between exploration and exploitation and the insufficient information exchange within the population, this paper proposes a multi-strategy improved gazelle optimization algorithm (MSIGOA). To address these issues, MSIGOA proposes an iteration-based updating framework that switches between exploitation and exploration according to the optimization process, which effectively enhances the balance between local …

A Survey on Agentic Service Ecosystems: Measurement, Analysis, and Optimization
Xuwen Zhang, Xiao Xue, Xia Xie, Qun Ma, Xiangning Yu, Deyu Zhou, Yifan Wang, Ming Zhang
https://arxiv.org/abs/2508.07343 …

A Survey on Agentic Service Ecosystems: Measurement, Analysis, and Optimization
The Agentic Service Ecosystem consists of heterogeneous autonomous agents (e.g., intelligent machines, humans, and human-machine hybrid systems) that interact through resource exchange and service co-creation. These agents, with distinct behaviors and motivations, exhibit autonomous perception, reasoning, and action capabilities, which increase system complexity and make traditional linear analysis methods inadequate. Swarm intelligence, characterized by decentralization, self-organization, eme…

A Scalable Machine Learning Approach Enabled RIS Optimization with Implicit Channel Estimation
Bile Peng, Vahid Jamali, Eduard Jorswieck
https://arxiv.org/abs/2508.07265 https:/…

A Scalable Machine Learning Approach Enabled RIS Optimization with Implicit Channel Estimation
The reconfigurable intelligent surface (RIS) is considered as a key enabler of the next-generation mobile radio systems. While attracting extensive interest from academia and industry due to its passive nature and low cost, scalability of RIS elements and requirement for channel state information (CSI) are two major difficulties for the RIS to become a reality. In this work, we introduce an unsupervised machine learning (ML) enabled optimization approach to configure the RIS. The dedicated neur…

An Inexact Proximal Framework for Nonsmooth Riemannian Difference-of-Convex Optimization
Bo Jiang, Meng Xu, Xingju Cai, Ya-Feng Liu
https://arxiv.org/abs/2509.08561 https://

An Inexact Proximal Framework for Nonsmooth Riemannian Difference-of-Convex Optimization
Nonsmooth Riemannian optimization has attracted increasing attention, especially in problems with sparse structures. While existing formulations typically involve convex nonsmooth terms, incorporating nonsmooth difference-of-convex (DC) penalties can enhance recovery accuracy. In this paper, we study a class of nonsmooth Riemannian optimization problems whose objective is the sum of a smooth function and a nonsmooth DC term. We establish, for the first time in the manifold setting, the equivale…

Bioluminescence tomography: A new regularized shape optimization method
Qianqian Wu, Rongfang Gong, Wei Gong, Ziyi Zhang, Shengfeng Zhu
https://arxiv.org/abs/2509.09533 https://…

Bioluminescence tomography: A new regularized shape optimization method
In this paper, we investigate an inverse source problem arising in bioluminescence tomography (BLT), where the objective is to recover both the support and intensity of the light source from boundary measurements. A shape optimization framework is developed, in which the source strength and its support are decoupled through first-order optimality conditions. To enhance the stability of the reconstruction, we incorporate a parameter-dependent coupled complex boundary method(CCBM) scheme together…

Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain
Wei Zhang, Yinchuan Wang, Wangtao Lu, Pengyu Zhang, Xiang Zhang, Yue Wang, Chaoqun Wang
https://arxiv.org/abs/2508.08108

Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain
It is a challenging task for ground robots to autonomously navigate in harsh environments due to the presence of non-trivial obstacles and uneven terrain. This requires trajectory planning that balances safety and efficiency. The primary challenge is to generate a feasible trajectory that prevents robot from tip-over while ensuring effective navigation. In this paper, we propose a capsizing-aware trajectory planner (CAP) to achieve trajectory planning on the uneven terrain. The tip-over stabili…

Active Learning and Explainable AI for Multi-Objective Optimization of Spin Coated Polymers
Brendan Young, Brendan Alvey, Andreas Werbrouck, Will Murphy, James Keller, Mattias J. Young, Matthew Maschmann
https://arxiv.org/abs/2509.08988

Active Learning and Explainable AI for Multi-Objective Optimization of Spin Coated Polymers
Spin coating polymer thin films to achieve specific mechanical properties is inherently a multi-objective optimization problem. We present a framework that integrates an active Pareto front learning algorithm (PyePAL) with visualization and explainable AI techniques to optimize processing parameters. PyePAL uses Gaussian process models to predict objective values (hardness and elasticity) from the design variables (spin speed, dilution, and polymer mixture), guiding the adaptive selection of sa…

Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users
Haowei Yang, Yushang Zhao, Sitao Min, Bo Su, Chao Yao, Wei Xu
https://arxiv.org/abs/2509.09066

Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users
The cold-start user issue further compromises the effectiveness of recommender systems in limiting access to the historical behavioral information. It is an effective pipeline to optimize instructional prompts on a few-shot large language model (LLM) used in recommender tasks. We introduce a context-conditioned prompt formulation method P(u,\ Ds)\ \rightarrow\ R\widehat, where u is a cold-start user profile, Ds is a curated support set, and R\widehat is the predicted ranked list of items. Based…

Using Variable Interaction Graphs to Improve Particle Swarm Optimization
Caz L. Czworkowski, John W. Sheppard
https://arxiv.org/abs/2509.06985 https://arxi…

Using Variable Interaction Graphs to Improve Particle Swarm Optimization
This paper presents Variable Interaction Graph Particle Swarm Optimization (VIGPSO), an adaptation to Particle Swarm Optimization (PSO) that dynamically learns and exploits variable interactions during the optimization process. PSO is widely used for real-valued optimization problems but faces challenges in high-dimensional search spaces. While Variable Interaction Graphs (VIGs) have proven effective for optimization algorithms operating with known problem structure, their application to black-…

LP-Spec: Leveraging LPDDR PIM for Efficient LLM Mobile Speculative Inference with Architecture-Dataflow Co-Optimization
Siyuan He, Zhantong Zhu, Yandong He, Tianyu Jia
https://arxiv.org/abs/2508.07227 …

LP-Spec: Leveraging LPDDR PIM for Efficient LLM Mobile Speculative Inference with Architecture-Dataflow Co-Optimization
LLM inference on mobile devices faces extraneous challenges due to limited memory bandwidth and computational resources. To address these issues, speculative inference and processing-in-memory (PIM) techniques have been explored at the algorithmic and hardware levels. However, speculative inference results in more compute-intensive GEMM operations, creating new design trade-offs for existing GEMV-accelerated PIM architectures. Furthermore, there exists a significant amount of redundant draft to…

Autonomous Air-Ground Vehicle Operations Optimization in Hazardous Environments: A Multi-Armed Bandit Approach
Jimin Choi, Max Z. Li
https://arxiv.org/abs/2508.08217 https://

Autonomous Air-Ground Vehicle Operations Optimization in Hazardous Environments: A Multi-Armed Bandit Approach
Hazardous environments such as chemical spills, radiological zones, and bio-contaminated sites pose significant threats to human safety and public infrastructure. Rapid and reliable hazard mitigation in these settings often unsafe for humans, calling for autonomous systems that can adaptively sense and respond to evolving risks. This paper presents a decision-making framework for autonomous vehicle dispatch in hazardous environments with uncertain and evolving risk levels. The system integrates…

Constraint-Compliant Network Optimization through Large Language Models
Youngjin Song, Wookjin Lee, Hong Ki Kim, Sang Hyun Lee
https://arxiv.org/abs/2509.07492 https://

Constraint-Compliant Network Optimization through Large Language Models
This work develops an LLM-based optimization framework ensuring strict constraint satisfaction in network optimization. While LLMs possess contextual reasoning capabilities, existing approaches often fail to enforce constraints, causing infeasible solutions. Unlike conventional methods that address average constraints, the proposed framework integrates a natural language-based input encoding strategy to restrict the solution space and guarantee feasibility. For multi-access edge computing netwo…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Somnath Hazra, Pallab Dasgupta, Soumyajit Dey
https://arxiv.org/abs/2509.09208 https://…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Constrained Reinforcement Learning (RL) aims to maximize the return while adhering to predefined constraint limits, which represent domain-specific safety requirements. In continuous control settings, where learning agents govern system actions, balancing the trade-off between reward maximization and constraint satisfaction remains a significant challenge. Policy optimization methods often exhibit instability near constraint boundaries, resulting in suboptimal training performance. To address t…

PeTTO: Leveraging GPUs to Accelerate Topology Optimization with the Pseudo-Transient Methods
Mingyuan Yang, Qian Yu, Chao Yang
https://arxiv.org/abs/2509.06971 https://

PeTTO: Leveraging GPUs to Accelerate Topology Optimization with the Pseudo-Transient Methods
We present a Pseudo-Transient Topology Optimization (PeTTO) approach that can leverage graphics processing units (GPUs) to efficiently solve single-material and multi-material topology optimization problems. By integrating PeTTO with phase field methods, the partial differential equations (PDEs) constrained optimization problem in topology optimization is transformed into a set of time dependent PDEs, which can be analyzed using the knowledge of transient physics. The sensitivities with respect…

Optimization of a Nonlinear Acoustics -- Structure Interaction Model
Barbara Kaltenbacher, Amjad Tuffaha
https://arxiv.org/abs/2508.07728 https://arxiv.org…

Optimization of a Nonlinear Acoustics -- Structure Interaction Model
In this paper, we consider a control/shape optimization problem of a nonlinear acoustics-structure interaction model of PDEs, whereby acoustic wave propagation in a chamber is governed by the Westervelt equation, and the motion of the elastic part of the boundary is governed by a 4th order Kirchoff equation. We consider a quadratic objective functional capturing the tracking of prescribed desired states, with three types of controls: 1) An excitation control represented by prescribed Neumann da…

Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing
Yajie Hao, Qiming Ding, Xiaoting Wang, Xiao Yuan
https://arxiv.org/abs/2509.07460 ht…

Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing
Accurately and efficiently predicting the equilibrium geometries of large molecules remains a central challenge in quantum computational chemistry, even with hybrid quantum-classical algorithms. Two major obstacles hinder progress: the large number of qubits required and the prohibitive cost of conventional nested optimization. In this work, we introduce a co-optimization framework that combines Density Matrix Embedding Theory (DMET) with Variational Quantum Eigensolver (VQE) to address these l…

TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making
Kechen Jiao, Zhirui Fang, Jiahao Liu, Bei Li, Qifan Wang, Xinyu Liu, Junhao Ruan, Zhongjian Qiao, Yifan Zhu, Yaxin Xu, Jingang Wang, Xiu Li
https://arxiv.org/abs/2509.08500

TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making
Using effective generalization capabilities of vision language models (VLMs) in context-specific dynamic tasks for embodied artificial intelligence remains a significant challenge. Although supervised fine-tuned models can better align with the real physical world, they still exhibit sluggish responses and hallucination issues in dynamically changing environments, necessitating further alignment. Existing post-SFT methods, reliant on reinforcement learning and chain-of-thought (CoT) approaches,…

Joint Model-based Model-free Diffusion for Planning with Constraints
Wonsuhk Jung, Utkarsh A. Mishra, Nadun Ranawaka Arachchige, Yongxin Chen, Danfei Xu, Shreyas Kousik
https://arxiv.org/abs/2509.08775

Joint Model-based Model-free Diffusion for Planning with Constraints
Model-free diffusion planners have shown great promise for robot motion planning, but practical robotic systems often require combining them with model-based optimization modules to enforce constraints, such as safety. Naively integrating these modules presents compatibility challenges when diffusion's multi-modal outputs behave adversarially to optimization-based modules. To address this, we introduce Joint Model-based Model-free Diffusion (JM2D), a novel generative modeling framework. JM2D fo…

SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis
Jipeng Lyu, Jiahua Dong, Yu-Xiong Wang
https://arxiv.org/abs/2510.06694 https://

SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis
Persistent dynamic scene modeling for tracking and novel-view synthesis remains challenging due to the difficulty of capturing accurate deformations while maintaining computational efficiency. We propose SCas4D, a cascaded optimization framework that leverages structural patterns in 3D Gaussian Splatting for dynamic scenes. The key idea is that real-world deformations often exhibit hierarchical patterns, where groups of Gaussians share similar transformations. By progressively refining deformat…

MAESTRO: Multi-modal Adaptive Ensemble for Spectro-Temporal Robust Optimization
Hong Liu
https://arxiv.org/abs/2509.08578 https://arxiv.org/pdf/2509.08578

MAESTRO: Multi-modal Adaptive Ensemble for Spectro-Temporal Robust Optimization
Timely and robust influenza incidence forecasting is critical for public health decision-making. To address this, we present MAESTRO, a Multi-modal Adaptive Ensemble for Spectro-Temporal Robust Optimization. MAESTRO achieves robustness by adaptively fusing multi-modal inputs-including surveillance, web search trends, and meteorological data-and leveraging a comprehensive spectro-temporal architecture. The model first decomposes time series into seasonal and trend components. These are then proc…

Multi-RIS Deployment Optimization for mmWave ISAC Systems in Real-World Environments
Yueheng Li, Xueyun Long, Mario Pauli, Suheng Tian, Xiang Wan, Benjamin Nuss, Tiejun Cui, Haixia Zhang, Thomas Zwick
https://arxiv.org/abs/2508.07226

Multi-RIS Deployment Optimization for mmWave ISAC Systems in Real-World Environments
Reconfigurable intelligent surface-assisted integrated sensing and communication (RIS-ISAC) presents a promising system architecture to leverage the wide bandwidth available at millimeter-wave (mmWave) frequencies, while mitigating severe signal propagation losses and reducing infrastructure costs. To enhance ISAC functionalities in the future air-ground integrated network applications, RIS deployment must be carefully designed and evaluated, which forms the core motivation of this paper. To en…

Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem
Gustavo V. Nascimento, Ivan R. Meneghini, Val\'eria Santos, Eduardo Luz, Gladston Moreira
https://arxiv.org/abs/2508.07077

Enhancing Decision Space Diversity in Multi-Objective Evolutionary Optimization for the Diet Problem
Multi-objective evolutionary algorithms (MOEAs) are essential for solving complex optimization problems, such as the diet problem, where balancing conflicting objectives, like cost and nutritional content, is crucial. However, most MOEAs focus on optimizing solutions in the objective space, often neglecting the diversity of solutions in the decision space, which is critical for providing decision-makers with a wide range of choices. This paper introduces an approach that directly integrates a H…

An Optimization Perspective on the Monotonicity of the Multiplicative Algorithm for Optimal Experimental Design
Renbo Zhao
https://arxiv.org/abs/2508.07074 https://

An Optimization Perspective on the Monotonicity of the Multiplicative Algorithm for Optimal Experimental Design
We provide an optimization-based argument for the monotonicity of the multiplicative algorithm (MA) for a class of optimal experimental design problems considered in Yu (2010). Our proof avoids introducing auxiliary variables (or problems) and leveraging statistical arguments, and is much more straightforward and simpler compared to the proof in Yu (2010). The simplicity of our monotonicity proof also allows us to easily identify several sufficient conditions that ensure the strict monotonicity…

Robust Integrated Priority and Speed Control based on Hierarchical Stochastic Optimization to Promote Bus Schedule Adherence along Signalized Arterial
Shurui Guan, Keqiang Li, Haoyu Yang, Yihe Chen, Hanxiao Ren, Yugong Luo
https://arxiv.org/abs/2508.07749

Robust Integrated Priority and Speed Control based on Hierarchical Stochastic Optimization to Promote Bus Schedule Adherence along Signalized Arterial
In intelligent transportation systems (ITS), adaptive transit signal priority (TSP) and dynamic bus control systems have been independently developed to maintain efficient and reliable urban bus services. However, those two systems could potentially lead to conflicting decisions due to the lack of coordination. Although some studies explore the integrated control strategies along the arterial, they merely rely on signal replanning to address system uncertainties. Therefore, their performance se…

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
Mohammed Tiouti, Mohamed Bal-Ghaoui
https://arxiv.org/abs/2509.09387 https://

MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
Effective model and hyperparameter selection remains a major challenge in deep learning, often requiring extensive expertise and computation. While AutoML and large language models (LLMs) promise automation, current LLM-based approaches rely on trial and error and expensive APIs, which provide limited interpretability and generalizability. We propose MetaLLMiX, a zero-shot hyperparameter optimization framework combining meta-learning, explainable AI, and efficient LLM reasoning. By leveraging h…

Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization
Myeung Suk Oh, Zhiyao Zhang, FNU Hairi, Alvaro Velasquez, Jia Liu
https://arxiv.org/abs/2508.07001

Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization
With wireless devices increasingly forming a unified smart network for seamless, user-friendly operations, random access (RA) medium access control (MAC) design is considered a key solution for handling unpredictable data traffic from multiple terminals. However, it remains challenging to design an effective RA-based MAC protocol to minimize collisions and ensure transmission fairness across the devices. While existing multi-agent reinforcement learning (MARL) approaches with centralized traini…

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Hangyi Jia, Yuxi Qian, Hanwen Tong, Xinhui Wu, Lin Chen, Feng Wei
https://arxiv.org/abs/2509.09321

Towards Adaptive ML Benchmarks: Web-Agent-Driven Construction, Domain Expansion, and Metric Optimization
Recent advances in large language models (LLMs) have enabled the emergence of general-purpose agents for automating end-to-end machine learning (ML) workflows, including data analysis, feature engineering, model training, and competition solving. However, existing benchmarks remain limited in task coverage, domain diversity, difficulty modeling, and evaluation rigor, failing to capture the full capabilities of such agents in realistic settings. We present TAM Bench, a diverse, realistic, and st…

Convexity of Optimization Curves: Local Sharp Thresholds, Robustness Impossibility, and New Counterexamples
Le Duc Hieu
https://arxiv.org/abs/2509.08954 https://

Convexity of Optimization Curves: Local Sharp Thresholds, Robustness Impossibility, and New Counterexamples
We study when the \emph{optimization curve} of first--order methods -- the sequence \${f(x\_n)}*{n\ge0}\$ produced by constant--stepsize iterations -- is convex, equivalently when the forward differences \$f(x\_n)-f(x*{n+1})\$ are nonincreasing. For gradient descent (GD) on convex \$L\$--smooth functions, the curve is convex for all stepsizes \$η\le 1.75/L\$, and this threshold is tight. Moreover, gradient norms are nonincreasing for all \$η\le 2/L\$, and in continuous time (gradient flow) th…

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Yisong Zhang, Ran Cheng, Guoxing Yi, Kay Chen Tan
https://arxiv.org/abs/2509.08269

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Large Language Models (LLMs), with their strong understanding and reasoning capabilities, are increasingly being explored for tackling optimization problems, especially in synergy with evolutionary computation. Despite rapid progress, however, the field still lacks a unified synthesis and a systematic taxonomy. This survey addresses this gap by providing a comprehensive review of recent developments and organizing them within a structured framework. We classify existing research into two main s…

Remote ID Based UAV Collision Avoidance Optimization for Low-Altitude Airspace Safety
Ziye Jia, Yian Zhu, Qihui Wu, Lei Zhang, Sen Yang, Zhu Han
https://arxiv.org/abs/2508.07651

Remote ID Based UAV Collision Avoidance Optimization for Low-Altitude Airspace Safety
With the rapid development of unmanned aerial vehicles (UAVs), it is paramount to ensure safe and efficient operations in open airspaces. The remote identification (Remote ID) is deemed an effective real-time UAV monitoring system by the federal aviation administration, which holds potentials for enabling inter-UAV communications. This paper deeply investigates the application of Remote ID for UAV collision avoidance while minimizing communication delays. First, we propose a Remote ID based dis…

Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization
Qiushi Tian, Churong Liang, Kairan Hong, Runnan Li
https://arxiv.org/abs/2510.07943 https://

Agent-Based Genetic Algorithm for Crypto Trading Strategy Optimization
Cryptocurrency markets present formidable challenges for trading strategy optimization due to extreme volatility, non-stationary dynamics, and complex microstructure patterns that render conventional parameter optimization methods fundamentally inadequate. We introduce Cypto Genetic Algorithm Agent (CGA-Agent), a pioneering hybrid framework that synergistically integrates genetic algorithms with intelligent multi-agent coordination mechanisms for adaptive trading strategy parameter optimization…

AVO: Amortized Value Optimization for Contact Mode Switching in Multi-Finger Manipulation
Adam Hung, Fan Yang, Abhinav Kumar, Sergio Aguilera Marinovic, Soshi Iba, Rana Soltani Zarrin, Dmitry Berenson
https://arxiv.org/abs/2510.07548

AVO: Amortized Value Optimization for Contact Mode Switching in Multi-Finger Manipulation
Dexterous manipulation tasks often require switching between different contact modes, such as rolling, sliding, sticking, or non-contact contact modes. When formulating dexterous manipulation tasks as a trajectory optimization problem, a common approach is to decompose these tasks into sub-tasks for each contact mode, which are each solved independently. Optimizing each sub-task independently can limit performance, as optimizing contact points, contact forces, or other variables without informa…

Accuracy analysis and optimization of scale-independent third-order WENO-Z scheme with critical-point accuracy preservation
Yunchuan Wu, Xiuzheng Cheng, Yi Duan, Linsen Zhang, Qin Li, Pan Yan, Mengyu Wang
https://arxiv.org/abs/2509.08413

Accuracy analysis and optimization of scale-independent third-order WENO-Z scheme with critical-point accuracy preservation
To address the order degradation at critical points in the WENO3-Z scheme, some improvements have been proposed , but these approaches generally fail to consider the occurrence of critical points at arbitrary positions within grid intervals, resulting in their inability to maintain third-order accuracy when a first-order critical point (CP1) occurs. Also, most previous improved schemes suffer from a relatively large exponent p of the ratio of global to local smoothness indicators, which adverse…

Decentralized Stochastic Nonconvex Optimization under the Relaxed Smoothness
Luo Luo, Xue Cui, Tingkai Jia, Cheng Chen
https://arxiv.org/abs/2509.08726 https://

Decentralized Stochastic Nonconvex Optimization under the Relaxed Smoothness
This paper studies decentralized optimization problem $f(\mathbf{x})=\frac{1}{m}\sum_{i=1}^m f_i(\mathbf{x})$, where each local function has the form of $f_i(\mathbf{x}) = {\mathbb E}\left[F(\mathbf{x};ξ_i)\right]$ which is $(L_0,L_1)$-smooth but possibly nonconvex and the random variable $ξ_i$ follows distribution ${\mathcal D}_i$. We propose a novel algorithm called decentralized normalized stochastic gradient descent (DNSGD), which can achieve the $ε$-stationary point on each local agent.…

Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
Sebastian Macaluso, Giovanni Geraci, El\'ias F. Combarro, Sergi Abadal, Ioannis Arapakis, Sofia Vallecorsa, Eduard Alarc\'on
https://arxiv.org/abs/2509.07773

Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving …

A modified RIME algorithm with covariance learning and diversity enhancement for numerical optimization
Shangqing Shi, Luoxiao Zhang, Yuchen Yin, Xiong Yang, Hoileong Lee
https://arxiv.org/abs/2509.09529

A modified RIME algorithm with covariance learning and diversity enhancement for numerical optimization
Metaheuristics are widely applied for their ability to provide more efficient solutions. The RIME algorithm is a recently proposed physical-based metaheuristic algorithm with certain advantages. However, it suffers from rapid loss of population diversity during optimization and is prone to fall into local optima, leading to unbalanced exploitation and exploration. To address the shortcomings of RIME, this paper proposes a modified RIME with covariance learning and diversity enhancement (MRIME-C…

End-to-End Quantum Algorithm for Topology Optimization in Structural Mechanics
Leonhard H\"olscher, Oliver Ahrend, Lukas Karch, Carlotta L'Estocq, Marc Marfany Andreu, Tobias Stollenwerk, Frank K. Wilhelm, Julia Kowalski
https://arxiv.org/abs/2510.07280

End-to-End Quantum Algorithm for Topology Optimization in Structural Mechanics
Topology optimization is a key methodology in engineering design for finding efficient and robust structures. Due to the enormous size of the design space, evaluating all possible configurations is typically infeasible. In this work, we present an end-to-end, fault-tolerant quantum algorithm for topology optimization that operates on the exponential Hilbert space representing the design space. We demonstrate the algorithm on the two-dimensional Messerschmitt-Bölkow-Blohm (MBB) beam problem. By…

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy
Yitong He, Pengcheng Xie
https://arxiv.org/abs/2509.08256 https://arx…

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy
Solving large-scale optimization problems is a bottleneck and is very important for machine learning and multiple kinds of scientific problems. Subspace-based methods using the local approximation strategy are one of the most important methods. This paper discusses different and novel kinds of advanced subspaces for such methods and presents a new algorithm with such subspaces, called MD-LAMBO. Theoretical analysis including the subspaces' properties, sufficient function value decrease, and glo…

Two Sides of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
Xunkai Li, Daohan Su, Sicheng Liu, Ru Zhang, Rong-Hua Li, Guoren Wang
https://arxiv.org/abs/2509.08401

Two Sides of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
Graph foundation models, inspired by the success of LLMs, are designed to learn the optimal embedding from multi-domain TAGs for the downstream cross-task generalization capability. During our investigation, graph VQ-MAE stands out among the increasingly diverse landscape of GFM architectures. This is attributed to its ability to jointly encode topology and textual attributes from multiple domains into discrete embedding spaces with clear semantic boundaries. Despite its potential, domain gener…

GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
Alexander Du, Emre Adabag, Gabriel Bravo, Brian Plancher
https://arxiv.org/abs/2510.07625

GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear trajectory optimization (TO) problems online remains computationally demanding. Existing GPU-accelerated approaches typically (i) parallelize a single solve to meet real-time deadlines, (ii) scale to very large batches at slower-than-real-time rates, or (iii) achieve speed by restricting model generality (e.g., point-mass dynamics or a single linearization…

An improved educational competition optimizer with multi-covariance learning operators for global optimization problems
Baoqi Zhao, Xiong Yang, Hoileong Lee, Bowen Dong
https://arxiv.org/abs/2509.09552

An improved educational competition optimizer with multi-covariance learning operators for global optimization problems
The educational competition optimizer is a recently introduced metaheuristic algorithm inspired by human behavior, originating from the dynamics of educational competition within society. Nonetheless, ECO faces constraints due to an imbalance between exploitation and exploration, rendering it susceptible to local optima and demonstrating restricted effectiveness in addressing complex optimization problems. To address these limitations, this study presents an enhanced educational competition opt…

A Complete Derivation of Complex Circle Manifold (CCM) Riemannian manifold Optimization Equations
Amirreza Tabrizi, Mohammad Hadi Mirmohammadi
https://arxiv.org/abs/2508.07396 h…

A Complete Derivation of Complex Circle Manifold (CCM) Riemannian manifold Optimization Equations
After reviewing manifold optimization techniques in applications like MIMO communication systems, phased array beamforming, radar, and control theory, we observed that the Complex Circle Manifold (CCM) is widely employed, yet its foundational relations and equations lack a rigorous, self-contained derivation in the literature. This paper provides a systematic and rigorous proof of CCM's key properties, including its tangent space and Riemannian gradient operations, with explicit connections to …

A Modular Algorithm for Non-Stationary Online Convex-Concave Optimization
Qing-xin Meng, Xia Lei, Jian-wei Liu
https://arxiv.org/abs/2509.07901 https://arx…

A Modular Algorithm for Non-Stationary Online Convex-Concave Optimization
This paper investigates the problem of Online Convex-Concave Optimization, which extends Online Convex Optimization to two-player time-varying convex-concave games. The goal is to minimize the dynamic duality gap (D-DGap), a critical performance measure that evaluates players' strategies against arbitrary comparator sequences. Existing algorithms fail to deliver optimal performance, particularly in stationary or predictable environments. To address this, we propose a novel modular algorithm wit…

Decision-Dependent Distributionally Robust Optimization with Application to Dynamic Pricing
Chengrui Qu, Huiwen Jia, Pengcheng You
https://arxiv.org/abs/2508.06965 https://

Decision-Dependent Distributionally Robust Optimization with Application to Dynamic Pricing
We consider decision-making problems under decision-dependent uncertainty (DDU), where the distribution of uncertain parameters depends on the decision variables and is only observable through a finite offline dataset. To address this challenge, we formulate a decision-dependent distributionally robust optimization (DD-DRO) problem, and leverage multivariate interpolation techniques along with the Wasserstein metric to construct decision-dependent nominal distributions (thereby decision-depende…

Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators
Hassen Nigatu, Shi Gaokun, Li Jituo, Wang Jin, Lu Guodong, Howard Li
https://arxiv.org/abs/2509.07216

Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators
Optimizing high-degree of freedom robotic manipulators requires searching complex, high-dimensional configuration spaces, a task that is computationally challenging for classical methods. This paper introduces a quantum native framework that integrates quantum machine learning with Grover's algorithm to solve kinematic optimization problems efficiently. A parameterized quantum circuit is trained to approximate the forward kinematics model, which then constructs an oracle to identify optimal con…

Time Scaling Makes Accelerated Gradient Flow and Proximal Method Faster in Multiobjective Optimization
Yingdong Yin
https://arxiv.org/abs/2508.07254 https://

Time Scaling Makes Accelerated Gradient Flow and Proximal Method Faster in Multiobjective Optimization
This paper extends a class of single-objective gradient flows and accelerated proximal methods to the multiobjective optimization domain within Euclidean spaces. The proposed gradient flow is a second-order differential equation composed of a second-order term, a first-order term with asymptotic vanishing behavior, and a gradient term with time scaling. We prove the existence of trajectory solutions to the equation and, through Lyapunov analysis, demonstrate that with appropriate parameter choi…

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Morteza Kimiaei, Vyacheslav Kungurtsev, Brian Olimba
https://arxiv.org/abs/2508.06906

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Integer and mixed-integer nonlinear programming (INLP, MINLP) are central to logistics, energy, and scheduling, but remain computationally challenging. This survey examines how machine learning and reinforcement learning can enhance exact optimization methods - particularly branch-and-bound (BB), without compromising global optimality. We cover discrete, continuous, and mixed-integer formulations, and highlight applications such as crew scheduling, vehicle routing, and hydropower planning. We i…

COMPASS: A Multi-Turn Benchmark for Tool-Mediated Planning & Preference Optimization
Tian Qin, Felix Bai, Ting-Yao Hu, Raviteja Vemulapalli, Hema Swetha Koppula, Zhiyang Xu, Bowen Jin, Mert Cemri, Jiarui Lu, Zirui Wang, Meng Cao
https://arxiv.org/abs/2510.07043

COMPASS: A Multi-Turn Benchmark for Tool-Mediated Planning & Preference Optimization
Real-world large language model (LLM) agents must master strategic tool use and user preference optimization through multi-turn interactions to assist users with complex planning tasks. We introduce COMPASS (Constrained Optimization through Multi-turn Planning and Strategic Solutions), a benchmark that evaluates agents on realistic travel-planning scenarios. We cast travel planning as a constrained preference optimization problem, where agents must satisfy hard constraints while simultaneously …

AutoMLGen: Navigating Fine-Grained Optimization for Coding Agents
Shangheng Du, Xiangchao Yan, Dengyang Jiang, Jiakang Yuan, Yusong Hu, Xin Li, Liang He, Bo Zhang, Lei Bai
https://arxiv.org/abs/2510.08511

AutoMLGen: Navigating Fine-Grained Optimization for Coding Agents
Large language models (LLMs) have shown impressive performance in general programming tasks. However, in Machine Learning Engineering (MLE) scenarios such as AutoML and Kaggle competitions, achieving high performance depends heavily on expert intervention and repeated adjustments rather than simply generating correct code. When applied directly to these tasks, LLMs often lack fine-grained domain priors, and existing MLE approaches that use linear or tree-structured searches limit knowledge tran…

Reinforcing Diffusion Models by Direct Group Preference Optimization
Yihong Luo, Tianyang Hu, Jing Tang
https://arxiv.org/abs/2510.08425 https://arxiv.org/…

Reinforcing Diffusion Models by Direct Group Preference Optimization
While reinforcement learning methods such as Group Relative Preference Optimization (GRPO) have significantly enhanced Large Language Models, adapting them to diffusion models remains challenging. In particular, GRPO demands a stochastic policy, yet the most cost-effective diffusion samplers are based on deterministic ODEs. Recent work addresses this issue by using inefficient SDE-based samplers to induce stochasticity, but this reliance on model-agnostic Gaussian noise leads to slow convergenc…

Evaluation of LLMs for Process Model Analysis and Optimization
Akhil Kumar, Jianliang Leon Zhao, Om Dobariya
https://arxiv.org/abs/2510.07489 https://arxiv…

Evaluation of LLMs for Process Model Analysis and Optimization
In this paper, we report our experience with several LLMs for their ability to understand a process model in an interactive, conversational style, find syntactical and logical errors in it, and reason with it in depth through a natural language (NL) interface. Our findings show that a vanilla, untrained LLM like ChatGPT (model o3) in a zero-shot setting is effective in understanding BPMN process models from images and answering queries about them intelligently at syntactic, logic, and semantic …

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- A Minimalist Bayesian Framework for Stochastic Optimization
Kaizheng Wang
…

Mix- and MoE-DPO: A Variational Inference Approach to Direct Preference Optimization
Jason Bohne, Pawel Polak, David Rosenberg, Brian Bloniarz, Gary Kazantsev
https://arxiv.org/abs/2510.08256

Mix- and MoE-DPO: A Variational Inference Approach to Direct Preference Optimization
Direct Preference Optimization (DPO) has recently emerged as a simple and effective alternative to reinforcement learning from human feedback (RLHF) for aligning large language models (LLMs) with user preferences. However, existing DPO formulations rely on a single monolithic model, which limits their expressivity in multi-task settings and their adaptability to heterogeneous or diverse preference distributions. In this work, we propose Mix- and MoE-DPO, a framework that extends DPO with both s…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- An Accelerated Variance Reduced Extra-Point Approach to Finite-Sum VI and Optimization
Kevin Huang, Nuozhou Wang, Shuzhong Zhang

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Yuanjun Dai, Keqiang He, An Wang
https://arxiv.org/abs/2510.08522 https://

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Existing batch size selection approaches in dis- tributed machine learning rely on static allocation or simplistic heuristics that fail to adapt to heterogeneous, dynamic computing environments. We present DYNAMIX, a reinforcement learning framework that formulates batch size optimization as a sequen- tial decision-making problem using Proximal Policy Optimiza- tion (PPO). Our approach employs a multi-dimensional state representation encompassing network-level metrics, system-level resource uti…

Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization
Yuchen Zhu, Wei Guo, Jaemoo Choi, Petr Molodyk, Bo Yuan, Molei Tao, Yongxin Chen
https://arxiv.org/abs/2510.08233

Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization
Diffusion large language models (dLLMs) are promising alternatives to autoregressive large language models (AR-LLMs), as they potentially allow higher inference throughput. Reinforcement learning (RL) is a crucial component for dLLMs to achieve comparable performance with AR-LLMs on important tasks, such as reasoning. However, RL algorithms that are well-suited for dLLMs' unique characteristics have yet to be developed. This paper proposes Distribution Matching Policy Optimization (DMPO), a pri…

Decentralized Online Riemannian Optimization Beyond Hadamard Manifolds
Emre Sahinoglu, Shahin Shahrampour
https://arxiv.org/abs/2509.07779 https://arxiv.or…

Decentralized Online Riemannian Optimization Beyond Hadamard Manifolds
We study decentralized online Riemannian optimization over manifolds with possibly positive curvature, going beyond the Hadamard manifold setting. Decentralized optimization techniques rely on a consensus step that is well understood in Euclidean spaces because of their linearity. However, in positively curved Riemannian spaces, a main technical challenge is that geodesic distances may not induce a globally convex structure. In this work, we first analyze a curvature-aware Riemannian consensus …

Inertial accelerated primal-dual algorithms for non-smooth convex optimization problems with linear equality constraints
Huan Zhang, Xiangkai Sun, Shengjie Li, Kok Lay Teo
https://arxiv.org/abs/2509.07306

Inertial accelerated primal-dual algorithms for non-smooth convex optimization problems with linear equality constraints
This paper is devoted to the study of an inertial accelerated primal-dual algorithm, which is based on a second-order differential system with time scaling, for solving a non-smooth convex optimization problem with linear equality constraints. We first introduce a second-order differential system with time scaling associated with the non-smooth convex optimization problem, and then obtain fast convergence rates for the primal-dual gap, the feasibility violation, and the objective residual along…

On the optimization dynamics of RLVR: Gradient gap and step size thresholds
Joe Suk, Yaqi Duan
https://arxiv.org/abs/2510.08539 https://arxiv.org/pdf/2510.…

On the optimization dynamics of RLVR: Gradient gap and step size thresholds
Reinforcement Learning with Verifiable Rewards (RLVR), which uses simple binary feedback to post-train large language models, has shown significant empirical success. However, a principled understanding of why it works has been lacking. This paper builds a theoretical foundation for RLVR by analyzing its training process at both the full-response (trajectory) and token levels. Central to our analysis is a quantity called the Gradient Gap, which formalizes the direction of improvement from low-r…

Systematic Optimization of Open Source Large Language Models for Mathematical Reasoning
Pranav Pawar, Dhwaj Jain, Varun Gupta, Kaustav Dedhia, Dashrath Kale, Sudhir Dhekane
https://arxiv.org/abs/2509.07238

Systematic Optimization of Open Source Large Language Models for Mathematical Reasoning
This paper presents a practical investigation into fine-tuning model parameters for mathematical reasoning tasks through experimenting with various configurations including randomness control, reasoning depth, and sampling strategies, careful tuning demonstrates substantial improvements in efficiency as well as performance. A holistically optimized framework is introduced for five state-of-the-art models on mathematical reasoning tasks, exhibiting significant performance boosts while maintainin…

Trust-Region Method for Optimization of Set-Valued Maps Given by Finitely Many Functions
Suprova Ghosh, Debdas Ghosh, Christiane Tammer, Xiaopeng Zhao
https://arxiv.org/abs/2509.07836

Trust-Region Method for Optimization of Set-Valued Maps Given by Finitely Many Functions
In this article, we develop a trust-region technique to find critical points of unconstrained set optimization problems with the objective set-valued map defined by finitely many twice continuously differentiable functions. The technique is globally convergent and has the descent property. To ensure the descent property, a new rule of trust-region reduction ratio is introduced for the considered set-valued maps. In the derived method, to find the sequence of iteration points, we need to perform…

A Monte Carlo Approach to Nonsmooth Convex Optimization via Proximal Splitting Algorithms
Nicholas Di, Eric C. Chi, Samy Wu Fung
https://arxiv.org/abs/2509.07914 https://…

A Monte Carlo Approach to Nonsmooth Convex Optimization via Proximal Splitting Algorithms
Operator splitting algorithms are a cornerstone of modern first-order optimization, relying critically on proximal operators as their fundamental building blocks. However, explicit formulas for proximal operators are available only for limited classes of functions, restricting the applicability of these methods. Recent work introduced HJ-Prox, a zeroth-order Monte Carlo approximation of the proximal operator derived from Hamilton-Jacobi PDEs, which circumvents the need for closed-form solutions…

Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization
Kevin Rojas, Jiahe Lin, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Molei Tao, Wei Deng
https://arxiv.org/abs/2510.08554

Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization
Diffusion language models (DLMs) enable parallel, order-agnostic generation with iterative refinement, offering a flexible alternative to autoregressive large language models (LLMs). However, adapting reinforcement learning (RL) fine-tuning to DLMs remains an open challenge because of the intractable likelihood. Pioneering work such as diffu-GRPO estimated token-level likelihoods via one-step unmasking. While computationally efficient, this approach is severely biased. A more principled foundat…

Optimization via a Control-Centric Framework
Liraz Mudrik, Isaac Kaminer, Sean Kragelund, Abram H. Clark
https://arxiv.org/abs/2510.05455 https://arxiv.org…

Optimization via a Control-Centric Framework
Optimization plays a central role in intelligent systems and cyber-physical technologies, where the speed and reliability of convergence directly impact performance. In control theory, optimization-centric methods are standard: controllers are designed by repeatedly solving optimization problems, as in linear quadratic regulation, $H_\infty$ control, and model predictive control. In contrast, this paper develops a control-centric framework for optimization itself, where algorithms are construct…

First-order SDSOS-convex semi-algebraic optimization and exact SOCP relaxations
Chengmiao Yang, Liguo Jiao, Jae Hyoung Lee
https://arxiv.org/abs/2509.07418 https://

First-order SDSOS-convex semi-algebraic optimization and exact SOCP relaxations
In this paper, we define a new type of nonsmooth convex function, called {\em first-order SDSOS-convex semi-algebraic function}, which is an extension of the previously proposed first-order SDSOS-convex polynomials (Chuong et al. in J Global Optim 75:885--919, 2019). This class of nonsmooth convex functions contains many well-known functions, such as the Euclidean norm, the $\ell_1$-norm commonly used in compressed sensing and sparse optimization, and the least squares function frequently emplo…

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
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- Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates
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