Tootfinder

Accelerating Newton-Schulz Iteration for Orthogonalization via Chebyshev-type Polynomials
Ekaterina Grishina, Matvey Smirnov, Maxim Rakhuba
https://arxiv.org/abs/2506.10935

Accelerating Newton-Schulz Iteration for Orthogonalization via Chebyshev-type Polynomials
The problem of computing optimal orthogonal approximation to a given matrix has attracted growing interest in machine learning. Notable applications include the recent Muon optimizer or Riemannian optimization on the Stiefel manifold. Among existing approaches, the Newton-Schulz iteration has emerged as a particularly effective solution, as it relies solely on matrix multiplications and thus achieves high computational efficiency on GPU hardware. Despite its efficiency, the method has inherent …

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new/
[1/1]:
GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints

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

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

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

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

Stability of Mean-Field Variational Inference
Shunan Sheng, Bohan Wu, Alberto Gonz\'alez-Sanz, Marcel Nutz
https://arxiv.org/abs/2506.07856 https://

Stability of Mean-Field Variational Inference
Mean-field variational inference (MFVI) is a widely used method for approximating high-dimensional probability distributions by product measures. This paper studies the stability properties of the mean-field approximation when the target distribution varies within the class of strongly log-concave measures. We establish dimension-free Lipschitz continuity of the MFVI optimizer with respect to the target distribution, measured in the 2-Wasserstein distance, with Lipschitz constant inversely prop…

This https://arxiv.org/abs/2505.00347 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics
The rapid scaling of models has led to prohibitively high training and fine-tuning costs. A major factor accounting for memory consumption is the widespread use of stateful optimizers (e.g., Adam), which maintain auxiliary information of even 2x the model size in order to achieve optimal convergence. We therefore present SOLO in this work to spawn a novel type of optimizer that requires an extremely light memory footprint. While previous efforts have achieved certain success in 8-bit or 4-bit c…

This https://arxiv.org/abs/2502.20770 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Learning to Steer Learners in Games
We consider the problem of learning to exploit learning algorithms through repeated interactions in games. Specifically, we focus on the case of repeated two player, finite-action games, in which an optimizer aims to steer a no-regret learner to a Stackelberg equilibrium without knowledge of its payoffs. We first show that this is impossible if the optimizer only knows that the learner is using an algorithm from the general class of no-regret algorithms. This suggests that the optimizer require…

A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads
Andr\'as Strausz, Niels Pardon, Ioana Giurgiu
https://arxiv.org/abs/2506.02802 http…

A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads
Lakehouse systems enable the same data to be queried with multiple execution engines. However, selecting the engine best suited to run a SQL query still requires a priori knowledge of the query computational requirements and an engine capability, a complex and manual task that only becomes more difficult with the emergence of new engines and workloads. In this paper, we address this limitation by proposing a cross-engine optimizer that can automate engine selection for diverse SQL queries throu…

This https://arxiv.org/abs/2505.22398 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Numerical Optimization Strategies for the Variational Hamiltonian Ansatz in Noisy Quantum Environments
We conduct a benchmark of eight optimization algorithms for variational quantum chemistry using the tVHA, evaluating performance on $H_2$, $H_4$, and $LiH$ (in both full and active spaces) under noiseless and sampling noise conditions. Sampling noise fundamentally alters optimizer behavior, with gradient-based methods performing best in ideal conditions, while population-based algorithms, such as CMA-ES, show greater resilience under noise. Hartree-Fock initialization reduces the number of func…