Tootfinder

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control I: Penalty Approach
Lechen Feng, Xun Li, Yuan-Hua Ni
https://arxiv.org/abs/2507.18114

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control I: Penalty Approach
This paper develops a unified nonconvex optimization framework for the design of group-sparse feedback controllers in infinite-horizon linear-quadratic (LQ) problems. We address two prominent extensions of the classical LQ problem: the distributed LQ problem with fixed communication topology (DFT-LQ) and the sparse feedback LQ problem (SF-LQ), both of which are motivated by the need for scalable and structure-aware control in large-scale systems. Unlike existing approaches that rely on convex r…

Quantum sensing of displacements with stabilized GKP states
Lautaro Labarca, Sara Turcotte, Alexandre Blais, Baptiste Royer
https://arxiv.org/abs/2506.20627

Quantum sensing of displacements with stabilized GKP states
We demonstrate how recent protocols developed for the stabilization of Gottesman-Kitaev-Preskill (GKP) states can be used for the estimation of two-quadrature displacement sensing, with sensitivities approaching the multivariate quantum Cramer-Rao bound. Thanks to the stabilization, this sensor is backaction evading and can function continuously without reset, making it well suited for the detection of itinerant signals. Additionally, we provide numerical simulations showing that the protocol c…

Quadratic flatness and Regularity for Codimension-One Varifolds with Bounded Anisotropic Mean Curvature
Mario Santilli, S{\l}awomir Kolasi\'nski
https://arxiv.org/abs/2507.18357

Quadratic flatness and Regularity for Codimension-One Varifolds with Bounded Anisotropic Mean Curvature
Given a uniformly convex norm $ ϕ$ on $ \mathbf{R}^{n+1} $ and a unit-density $ n $-dimensional varifold $ V $ in an open subset of $ \mathbf{R}^{n+1} $ with bounded mean $ ϕ$-curvature, under the hypothesis $$ \textrm{$\mathcal{H}^n \llcorner {\rm spt}\| V \| $ is absolutely continuous with respect to $ \| V \| $},$$ we prove that there exists an open dense $ \mathscr{C}^{1, α}$-regular part in $ {\rm spt} \| V \| $. Moreover we prove that the $ \mathscr{C}…

HybridTM: Combining Transformer and Mamba for 3D Semantic Segmentation
Xinyu Wang, Jinghua Hou, Zhe Liu, Yingying Zhu
https://arxiv.org/abs/2507.18575 https://

HybridTM: Combining Transformer and Mamba for 3D Semantic Segmentation
Transformer-based methods have demonstrated remarkable capabilities in 3D semantic segmentation through their powerful attention mechanisms, but the quadratic complexity limits their modeling of long-range dependencies in large-scale point clouds. While recent Mamba-based approaches offer efficient processing with linear complexity, they struggle with feature representation when extracting 3D features. However, effectively combining these complementary strengths remains an open challenge in thi…

Supersymmetry and integrability of the elliptic $\mathrm{AdS}_3 \times \mathrm{S}^3 \times \mathrm{T}^4$ superstring
Ben Hoare, Fiona K. Seibold
https://arxiv.org/abs/2507.18548

Supersymmetry and integrability of the elliptic $\mathrm{AdS}_3 \times \mathrm{S}^3 \times \mathrm{T}^4$ superstring
We construct a 1-parameter family of Ramond-Ramond fluxes supporting the elliptic $\mathrm{AdS}_3 \times \mathrm{S}^3 \times \mathrm{T}^4$ metric with constant dilaton and preserving 8 of the 16 supercharges of the undeformed background. On the supersymmetric locus, we compute the tree-level worldsheet S-matrix in uniform light-cone gauge up to quadratic order in fermions and find that it non-trivially satisfies the classical Yang-Baxter equation. Moreover, imposing classical integrability and …

DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
Shuyuan Wang, Philip D. Loewen, Michael Forbes, Bhushan Gopaluni, Wei Pan
https://arxiv.org/abs/2506.17473

DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remains underexplored as a differentiable component. The scalability of differentiating through extended iterations and horizons poses significant challenges, hindering iLQR from being an effective differentiable controller. This paper introduces DiLQR, a framework that facilitates differentiation through iLQR, allowing it …

Instance Space Analysis for the Quadratic Assignment Problem
Jeffrey Christiansen (ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications), Kate Smith-Miles (ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications)
https://arxiv.org/abs/2506.20172

Instance Space Analysis for the Quadratic Assignment Problem
For any optimisation problem where diverse algorithmic approaches are available, the task of predicting algorithm performance and selecting the algorithm most likely to perform well on a given instance holds great practical interest. However, if our test instances do not adequately represent the potential problem space, we may be misled about the strengths and weaknesses of an algorithm. In this paper we consider algorithm prediction and selection for the Quadratic Assignment Problem (QAP). We …

Enhanced quantum sensing of gravitational acceleration constant
Giorgio Stucchi, Matteo G. A. Paris
https://arxiv.org/abs/2506.20352 https://

Enhanced quantum sensing of gravitational acceleration constant
We investigate the use of quantum probes to accurately determine the strength of the local gravitational field on Earth. Our findings show that delocalized probes generally outperform localized ones, with the precision enhancement scaling quadratically with the separation between the two wavefunction components. This advantage persists under realistic position measurements, which can achieve precision not too far from the ultimate bound. We also discuss the influence of Earth's surface, demonst…

Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention
Yiwen Chen, Zhihao Li, Yikai Wang, Hu Zhang, Qin Li, Chi Zhang, Guosheng Lin
https://arxiv.org/abs/2507.17745

Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention
Recent advances in sparse voxel representations have significantly improved the quality of 3D content generation, enabling high-resolution modeling with fine-grained geometry. However, existing frameworks suffer from severe computational inefficiencies due to the quadratic complexity of attention mechanisms in their two-stage diffusion pipelines. In this work, we propose Ultra3D, an efficient 3D generation framework that significantly accelerates sparse voxel modeling without compromising quali…

Exact Hull Reformulation for Quadratically Constrained Generalized Disjunctive Programs
Sergey Gusev, David E. Bernal Neira
https://arxiv.org/abs/2508.16093 https://

Exact Hull Reformulation for Quadratically Constrained Generalized Disjunctive Programs
Generalized Disjunctive Programming (GDP) is a framework for optimization problems involving discrete decisions and nonlinear constraints. The widely used Hull Reformulation transforms GDPs into Mixed-Integer Nonlinear Programming (MINLP) problems with tighter continuous relaxations. However, it typically employs an epsilon approximation of the closure of the perspective function for nonlinear constraints, which can lead to numerical instability and a weaker continuous relaxation. We present …