Tootfinder

Optimizers of the Finite-Rank Hardy-Lieb-Thirring Inequality for Hardy-Schr\"odinger Operator
Bin Chen, Yujin Guo, Shuang Wu
https://arxiv.org/abs/2509.17307 https://

Optimizers of the Finite-Rank Hardy-Lieb-Thirring Inequality for Hardy-Schrödinger Operator
We study the following finite-rank Hardy-Lieb-Thirring inequality of Hardy-Schrödinger operator: \begin{equation*} \sum_{i=1}^N\left|λ_i\Big(-Δ-\frac{c}{|x|^2}-V\Big)\right|^s\leq C_{s,d}^{(N)}\int_{\mathbb R^d}V_+^{s+\frac d2}dx, \end{equation*} where $N\in\mathbb N^+$, $d\geq3$, $00$ is the best constant of Hardy's inequality, and $V\in L^{s+\frac d2}(\mathbb R^d)$ holds for $s>0$. Here $λ_i\big(-Δ-{c}{|x|^{-2}}-V\big)$…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/10]:
- A geometric framework for momentum-based optimizers for low-rank training
Steffen Schotth\"ofer, Timon Klein, Jonas Kusch

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Xin Chen, Rui Huang, Longbin Tang, Lin Zhao
https://arxiv.org/abs/2509.17340 https:/…

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Agile mapless navigation in cluttered 3D environments poses significant challenges for autonomous drones. Conventional mapping-planning-control pipelines incur high computational cost and propagate estimation errors. We present AERO-MPPI, a fully GPU-accelerated framework that unifies perception and planning through an anchor-guided ensemble of Model Predictive Path Integral (MPPI) optimizers. Specifically, we design a multi-resolution LiDAR point-cloud representation that rapidly extracts spat…

Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?
Xinru Mu, Omar M. Saad, Shaowen Wang, Tariq Alkhalifah
https://arxiv.org/abs/2509.14919

Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?
Full waveform inversion (FWI) iteratively updates the velocity model by minimizing the difference between observed and simulated data. Due to the high computational cost and memory requirements associated with global optimization algorithms, FWI is typically implemented using local optimization methods. However, when the initial velocity model is inaccurate and low-frequency seismic data (e.g., below 3 Hz) are absent, the mismatch between simulated and observed data may exceed half a cycle, a p…

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
Robiul Islam, Dmitry I. Ignatov, Karl Kaberg, Roman Nabatchikov
https://arxiv.org/abs/2509.14078

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands,…

CARPO: Leveraging Listwise Learning-to-Rank for Context-Aware Query Plan Optimization
Wenrui Zhou, Qiyu Liu, Jingshu Peng, Aoqian Zhang, Lei Chen
https://arxiv.org/abs/2509.03102

CARPO: Leveraging Listwise Learning-to-Rank for Context-Aware Query Plan Optimization
Efficient data processing is increasingly vital, with query optimizers playing a fundamental role in translating SQL queries into optimal execution plans. Traditional cost-based optimizers, however, often generate suboptimal plans due to flawed heuristics and inaccurate cost models, leading to the emergence of Learned Query Optimizers (LQOs). To address challenges in existing LQOs, such as the inconsistency and suboptimality inherent in pairwise ranking methods, we introduce CARPO, a generic fr…

From Rattle to Roar: Optimizer Showdown for MambaStock on S&P 500
Alena Chan, Maria Garmonina
https://arxiv.org/abs/2508.04707 https://arxiv.org/pdf/25…

Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
Ahmed Khaled, Satyen Kale, Arthur Douillard, Chi Jin, Rob Fergus, Manzil Zaheer
https://arxiv.org/abs/2509.10439 …

Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
Modern machine learning often requires training with large batch size, distributed data, and massively parallel compute hardware (like mobile and other edge devices or distributed data centers). Communication becomes a major bottleneck in such settings but methods like Local Stochastic Gradient Descent (Local SGD) show great promise in reducing this additional communication overhead. Local SGD consists of three parts: a local optimization process, an aggregation mechanism, and an outer optimize…

DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators
Charles Hong, Qijing Huang, Grace Dinh, Mahesh Subedar, Yakun Sophia Shao
https://arxiv.org/abs/2509.10702 …

DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators
In the hardware design space exploration process, it is critical to optimize both hardware parameters and algorithm-to-hardware mappings. Previous work has largely approached this simultaneous optimization problem by separately exploring the hardware design space and the mapspace - both individually large and highly nonconvex spaces - independently. The resulting combinatorial explosion has created significant difficulties for optimizers. In this paper, we introduce DOSA, which consists of di…

Optimizing VQE Ansatz for Studying Tight-Binding Models with \textit{sd}-Interaction and On-Site Coulomb Repulsion
Oleg Udalov
https://arxiv.org/abs/2510.08864 https://

Optimizing VQE Ansatz for Studying Tight-Binding Models with \textit{sd}-Interaction and On-Site Coulomb Repulsion
The VQE algorithm is applied to the problem of finding the ground state of a lattice model with on-site Coulomb repulsion, nearest-neighbor hopping, and on-site sd-interaction. We compare the performance of several ansatze, including cluster and generic forms. Several modifications of the standard cluster ansatz implementation are proposed, which significantly reduce the number of two-qubit gates. Different classical optimizers are employed within the VQE algorithm. The performance of the algor…

Fisher-Orthogonal Projection Methods for Natural Gradient Descent with Large Batches
Yishun Lu, Wesley Armour
https://arxiv.org/abs/2508.13898 https://arxi…

Fisher-Orthogonal Projection Methods for Natural Gradient Descent with Large Batches
Modern GPUs are equipped with large amounts of high-bandwidth memory, enabling them to support mini-batch sizes of up to tens of thousands of training samples. However, most existing optimizers struggle to perform effectively at such a large batch size. As batch size increases, gradient noise decreases due to averaging over many samples, limiting the ability of first-order methods to escape sharp or suboptimal minima and reach the global minimum. Meanwhile, second-order methods like the natural…

Maestro: Joint Graph & Config Optimization for Reliable AI Agents
Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
https://arxiv.org/abs/2509.04642 https://

Maestro: Joint Graph & Config Optimization for Reliable AI Agents
Building reliable LLM agents requires decisions at two levels: the graph (which modules exist and how information flows) and the configuration of each node (models, prompts, tools, control knobs). Most existing optimizers tune configurations while holding the graph fixed, leaving structural failure modes unaddressed. We introduce Maestro, a framework-agnostic holistic optimizer for LLM agents that jointly searches over graphs and configurations to maximize agent quality, subject to explicit rol…

Towards Reliable Neural Optimizers: Permutation-Equivariant Neural Approximation in Dynamic Data Driven Applications Systems
Meiyi Li, Javad Mohammadi
https://arxiv.org/abs/2508.19364

Towards Reliable Neural Optimizers: Permutation-Equivariant Neural Approximation in Dynamic Data Driven Applications Systems
Dynamic Data Driven Applications Systems (DDDAS) motivate the development of optimization approaches capable of adapting to streaming, heterogeneous, and asynchronous data from sensor networks. Many established optimization solvers, such as branch-and-bound, gradient descent, and Newton-Raphson methods, rely on iterative algorithms whose step-by-step convergence makes them too slow for real-time, multi-sensor environments. In our recent work, we introduced LOOP-PE (Learning to Optimize the Opti…

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Chisom Chibuike, Adeyinka Ogunsanya
https://arxiv.org/abs/2509.08499 https://

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Optimization has been an important factor and topic of interest in training deep learning models, yet less attention has been given to how we select the optimizers we use to train these models. Hence, there is a need to dive deeper into how we select the optimizers we use for training and the metrics that determine this selection. In this work, we compare the performance of 10 different optimizers in training a simple Multi-layer Perceptron model using a heart disease dataset from Kaggle. We se…

Effective Atom Theory: Gradient-Driven ab initio Materials Design
Justin Tahmassebpur, Brandon Li, Boris Barron, H\'ector Abru\~na, Peter Frazier, Tom\'as Arias
https://arxiv.org/abs/2509.07180

Effective Atom Theory: Gradient-Driven ab initio Materials Design
We introduce Effective Atom Theory (EAT), a framework that transforms combinatorial materials design into a smooth, gradient-driven optimization within density functional theory (DFT). Atoms are represented as probabilistic mixtures of elements, enabling gradient-based optimizers to converge to a physically realizable material in about 50 energy evaluations -- far fewer than combinatorial optimization methods. Applied to Co-Cr-Ni-V oxides for the alkaline oxygen evolution reaction (OER), EAT le…

Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum
Nirmal Gaud, Surej Mouli, Preeti Katiyar, Vaduguru Venkata Ramya
https://arxiv.org/abs/2508.04293

Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum
This study proposes NIRMAL (Novel Integrated Robust Multi-Adaptation Learning), a novel optimization algorithm that combines multiple strategies inspired by the movements of the chess piece. These strategies include gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We carefully evaluated NIRMAL against two widely used and successful optimizers, Adam and SGD with Momentum, on four benchmark image classification datasets: MNIST, Fashion…

Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise
Silvie Ill\'esov\'a, Tom\'a\v{s} Bezd\v{e}k, Vojt\v{e}ch Nov\'ak, Bruno Senjean, Martin Beseda
https://arxiv.org/abs/2510.08727

Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise
This work investigates the performance of numerical optimization algorithms applied to the State-Averaged Orbital-Optimized Variational Quantum Eigensolver for the H2 molecule under various quantum noise conditions. The goal is to assess the stability, accuracy, and computational efficiency of commonly used gradient-based, gradient-free, and global optimization strategies within the Noisy Intermediate-Scale Quantum regime. We systematically compare six representative optimizers, BFGS, SLSQP, Ne…

On the quadratic barycentric transport problem
Nathael Gozlan (MAP5 - UMR 8145), Thibaut Le Gouic (I2M), Paul-Marie Samson (LAMA)
https://arxiv.org/abs/2509.04935 https://

On the quadratic barycentric transport problem
We investigate the structure of optimal transport plans, dual optimizers, and geodesic paths for the quadratic barycentric transport problem.

Speeding Up the NSGA-II via Dynamic Population Sizes
Benjamin Doerr, Martin S. Krejca, Simon Wietheger
https://arxiv.org/abs/2509.01739 https://arxiv.org/p…

Speeding Up the NSGA-II via Dynamic Population Sizes
Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-offs (the Pareto optima) at once, MOEAs are typically run with a large, static population of solution candidates, which can slow down the algorithm. We propose the dynamic NSGA-II (dNSGA-II), which is based on the popular NSGA-II and features a non-static population size. The dNSGA-II starts with a small initial populati…

Anisotropic fractional area measures
Xiaxing Cai
https://arxiv.org/abs/2510.05279 https://arxiv.org/pdf/2510.05279

Anisotropic fractional area measures
The anisotropic $s$-fractional area measures are introduced as the first variation of the anisotropic fractional $s$-perimeter $P_s(K,L)$, with $L$ an origin symmetric convex body and $s\in(0,1)$. As $s\rightarrow 1^-$, the anisotropic $s$-fractional area measure converges to the mixed area measure of $K$ and the moment body of $L$. The Minkowski problem of these measures are solved. Finally, a necessary condition for the convexity of optimizers in the anisotropic fractional isoperimetric inequ…

Openness of shape optimizers in higher-order and non-scalar problems
Rupert L. Frank
https://arxiv.org/abs/2509.22487 https://arxiv.org/pdf/2509.22487

Openness of shape optimizers in higher-order and non-scalar problems
We consider the first eigenvalues of the polyharmonic, Lamé and Stokes operators with Dirichlet boundary conditions on sets of given finite measure. It is shown that a quasi-open set for which this eigenvalue is minimal is open. This removes dimensional restrictions in earlier works. We use Campanato theory, which works well in the present higher order or non-scalar setting.

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[13/15]:
- Fantastic Pretraining Optimizers and Where to Find Them
Kaiyue Wen, David Hall, Tengyu Ma, Percy Liang

Join Cardinality Estimation with OmniSketches
David Justen, Matthias Boehm
https://arxiv.org/abs/2508.17931 https://arxiv.org/pdf/2508.17931

Join Cardinality Estimation with OmniSketches
Join ordering is a key factor in query performance, yet traditional cost-based optimizers often produce sub-optimal plans due to inaccurate cardinality estimates in multi-predicate, multi-join queries. Existing alternatives such as learning-based optimizers and adaptive query processing improve accuracy but can suffer from high training costs, poor generalization, or integration challenges. We present an extension of OmniSketch - a probabilistic data structure combining count-min sketches and K…

Crosslisted article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers
Robert MacKnight, Jose Emilio Regio, Jeffrey G. Ethier, Luke A. Baldwi…

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 …

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Kristi Topollai, Anna Choromanska
https://arxiv.org/abs/2510.04988 https://

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
The vast majority of modern deep learning models are trained with momentum-based first-order optimizers. The momentum term governs the optimizer's memory by determining how much each past gradient contributes to the current convergence direction. Fundamental momentum methods, such as Nesterov Accelerated Gradient and the Heavy Ball method, as well as more recent optimizers such as AdamW and Lion, all rely on the momentum coefficient that is customarily set to $β= 0.9$ and kept constant during …

SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
Hanwen Liu, Qihan Zhang, Ryan Marcus, Ibrahim Sabek
https://arxiv.org/abs/2508.17556 https://a…

SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
Query optimization is a crucial problem in database systems that has been studied for decades. Learned query optimizers (LQOs) can improve performance over time by incorporating feedback; however, they suffer from cold-start issues and often require retraining when workloads shift or schemas change. Recent LLM-based query optimizers leverage pre-trained and fine-tuned LLMs to mitigate these challenges. Nevertheless, they neglect LLMs' in-context learning and execution records as feedback for co…

Efficient Differentiable Contact Model with Long-range Influence
Xiaohan Ye, Kui Wu, Zherong Pan, Taku Komura
https://arxiv.org/abs/2509.20917 https://arxi…

Efficient Differentiable Contact Model with Long-range Influence
With the maturation of differentiable physics, its role in various downstream applications: such as model predictive control, robotic design optimization, and neural PDE solvers, has become increasingly important. However, the derivative information provided by differentiable simulators can exhibit abrupt changes or vanish altogether, impeding the convergence of gradient-based optimizers. In this work, we demonstrate that such erratic gradient behavior is closely tied to the design of contact m…

Hybrid Sequential Quantum Computing
Pranav Chandarana, Sebasti\'an V. Romero, Alejandro Gomez Cadavid, Anton Simen, Enrique Solano, Narendra N. Hegade
https://arxiv.org/abs/2510.05851

Hybrid Sequential Quantum Computing
We introduce hybrid sequential quantum computing (HSQC), a paradigm for combinatorial optimization that systematically integrates classical and quantum methods within a structured, stage-wise workflow. HSQC may involve an arbitrary sequence of classical and quantum processes, as long as the global result outperforms the standalone components. Our testbed begins with classical optimizers to explore the solution landscape, followed by quantum optimization to refine candidate solutions, and conclu…

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
Phuc Hao Do, Tran Duc Le
https://arxiv.org/abs/2508.04288 https://

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
Applying near-term variational quantum algorithms to the problem of dynamic satellite network routing represents a promising direction for quantum computing. In this work, we provide a critical evaluation of two major approaches: static quantum optimizers such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) for offline route computation, and Quantum Reinforcement Learning (QRL) methods for online decision-making. Using ideal, noise-free sim…

Locating Ab Initio Transition States via Geodesic Construction on Machine Learned Potential Energy Surfaces
Diptarka Hait, Jan D. Estrada Pab\'on, Martin St\"ohr, Todd J. Mart\'inez
https://arxiv.org/abs/2507.17968

Locating Ab Initio Transition States via Geodesic Construction on Machine Learned Potential Energy Surfaces
Efficient and reliable identification and optimization of transition state structures is a longstanding challenge in computational chemistry. Popular chain-of-states methods require hundreds if not thousands of \textit{ab initio} calculations to generate initial guesses for local quasi-Newton optimizers, with persistent risk of collapse to an alternative stationary point on the potential energy surface (PES). Here, we show that high-quality guess structures for transition state optimization can…

Drop-Muon: Update Less, Converge Faster
Kaja Gruntkowska, Yassine Maziane, Zheng Qu, Peter Richt\'arik
https://arxiv.org/abs/2510.02239 https://arxiv.o…

Drop-Muon: Update Less, Converge Faster
Conventional wisdom in deep learning optimization dictates updating all layers at every step-a principle followed by all recent state-of-the-art optimizers such as Muon. In this work, we challenge this assumption, showing that full-network updates can be fundamentally suboptimal, both in theory and in practice. We introduce a non-Euclidean Randomized Progressive Training method-Drop-Muon-a simple yet powerful framework that updates only a subset of layers per step according to a randomized sche…

NeuSO: Neural Optimizer for Subgraph Queries
Linglin Yang, Lei Zou, Chunshan Zhao
https://arxiv.org/abs/2509.23775 https://arxiv.org/pdf/2509.23775

NeuSO: Neural Optimizer for Subgraph Queries
Subgraph query is a critical task in graph analysis with a wide range of applications across various domains. Most existing methods rely on heuristic vertex matching orderings, which may significantly degrade enumeration performance for certain queries. While learning-based optimizers have recently gained attention in the context of relational databases, they cannot be directly applied to subgraph queries due to the heterogeneous and schema-flexible nature of graph data, as well as the large nu…

ANO : Faster is Better in Noisy Landscape
Adrien Kegreisz
https://arxiv.org/abs/2508.18258 https://arxiv.org/pdf/2508.18258

ANO : Faster is Better in Noisy Landscape
Stochastic optimizers are central to deep learning, yet widely used methods such as Adam and Adan can degrade in non-stationary or noisy environments, partly due to their reliance on momentum-based magnitude estimates. We introduce Ano, a novel optimizer that decouples direction and magnitude: momentum is used for directional smoothing, while instantaneous gradient magnitudes determine step size. This design improves robustness to gradient noise while retaining the simplicity and efficiency of …