Tootfinder

A trust-region method for optimal control of ODEs with continuous-or-off controls and TV regularization
Markus Friedemann, Gerd Wachsmuth
https://arxiv.org/abs/2508.10692 https:…

A trust-region method for optimal control of ODEs with continuous-or-off controls and TV regularization
A solution algorithm for a special class of optimal control problems subject to an ordinary differential equation is proposed. The controls possess a continuous-or-off structure and are priced by a convex function. Additionally a total variation regularization is applied to penalize switches. Our solution method combines a trust-region method and a proximal gradient method. The subproblems are solved via Bellman's optimality principle. Convergence with respect to a criticality measure is proven…

Statistical analysis of barren plateaus in variational quantum algorithms
Le Bin Ho, Jesus Urbaneja, Sahel Ashhab
https://arxiv.org/abs/2508.08915 https://…

Statistical analysis of barren plateaus in variational quantum algorithms
We investigate the barren plateau (BP) phenomenon in variational quantum algorithms using a statistical approach. Using Gaussian function models, we identify three distinct types of BPs. The first type, which we called localized-dip BPs, occurs in landscapes that are mostly flat but contain a dip point where the gradient is large in a small region around the minimum. The second type, called localized-gorge BPs, which are somewhat similar to the localized-dip BPs but contain a gorge line. The th…

Nature or Nurture: LMC-like Dust in the Solar Metallicity Galaxy M31
Geoffrey C. Clayton, Petia Yanchulova Merica-Jones, Karl D. Gordon, Marjorie Decleir, Claire E. Murray, Ralph Bohlin, Luciana Bianchi, Philip Massey, Michael J. Wolff
https://arxiv.org/abs/2507.08612

Nature or Nurture: LMC-like Dust in the Solar Metallicity Galaxy M31
Using the {\it Hubble Space Telescope}/Space Telescope Imaging Spectrograph, ultraviolet (UV) extinction curves have been measured in M31 along thirteen new sightlines, increasing the M31 sample to seventeen. This sample covers a wide area of M31 having galactocentric distances of 5 to 16 kpc, enabling the analysis of UV extinction curve variations over a large region of an external galaxy similar to the Milky Way with global galactic characteristics such as metallicity for the first time. No c…

Motion of Elastic Thin Films by Evaporation-Condensation in the Dewetting Regime
M. S. Indulekha
https://arxiv.org/abs/2508.07562 https://arxiv.org/pdf/250…

Motion of Elastic Thin Films by Evaporation-Condensation in the Dewetting Regime
In this work, we show the short-time existence of solutions of the evolution equations that represent the solid state dewetting of thin films through evaporation-condensation as a two dimensional sharp interface variational model. The evolution law is established as the $L^2$ gradient flow of surface energies in the presence of epitaxial strain. The main novelty is the presence of moving contact lines when the film formation is governed by the evaporation-condensation method.

A Computable Measure of Suboptimality for Entropy-Regularised Variational Objectives
Cl\'ementine Chazal, Heishiro Kanagawa, Zheyang Shen, Anna Korba, Chris. J. Oates
https://arxiv.org/abs/2509.10393

A Computable Measure of Suboptimality for Entropy-Regularised Variational Objectives
Several emerging post-Bayesian methods target a probability distribution for which an entropy-regularised variational objective is minimised. This increased flexibility introduces a computational challenge, as one loses access to an explicit unnormalised density for the target. To mitigate this difficulty, we introduce a novel measure of suboptimality called 'gradient discrepancy', and in particular a 'kernel gradient discrepancy' (KGD) that can be explicitly computed. In the standard Bayesian …

A preconditioned third-order implicit-explicit algorithm with a difference of varying convex functions and extrapolation
Kelin Wu, Hongpeng Sun
https://arxiv.org/abs/2509.09391 …

A preconditioned third-order implicit-explicit algorithm with a difference of varying convex functions and extrapolation
This paper proposes a novel preconditioned implicit-explicit algorithm enhanced with the extrapolation technique for non-convex optimization problems. The algorithm employs a third-order Adams-Bashforth scheme for the nonlinear and explicit parts and a third-order backward differentiation formula for the implicit part of the gradient flow in variational functions. The proposed algorithm, akin to a generalized difference-of-convex (DC) approach, employs a changing set of convex functions in each…

A gradient estimate for the linearized translator equation
Kyeongsu Choi, Robert Haslhofer, Or Hershkovits
https://arxiv.org/abs/2509.07629 https://arxiv.o…

A gradient estimate for the linearized translator equation
In this paper, we develop some analytic foundations for the linearized translator equation in $\mathbb{R}^4$, i.e. in the first dimension where the Bernstein property fails. This equation governs how the (noncompact) singularity models of the mean curvature flow in $\mathbb{R}^4$ fit together in a common moduli space. Here, we prove a gradient estimate, which gives a sharp bound for $W_v$, namely for the derivative of the variation field $W$ in the tip region. This serves as a substitute for th…

Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
Joona Pankkonen, Lauri Ylinen, Matti Raasakka, Andrea Marchesin, Ilkka Tittonen
https://arxiv.org/abs/2507.07742

Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
Parameterized quantum circuits (PQCs) are pivotal components of variational quantum algorithms (VQAs), which represent a promising pathway to quantum advantage in noisy intermediate-scale quantum (NISQ) devices. PQCs enable flexible encoding of quantum information through tunable quantum gates and have been successfully applied across domains such as quantum chemistry, combinatorial optimization, and quantum machine learning. Despite their potential, PQC performance on NISQ hardware is hindered…

Special-Unitary Parameterization for Trainable Variational Quantum Circuits
Kuan-Cheng Chen, Huan-Hsin Tseng, Samuel Yen-Chi Chen, Chen-Yu Liu, Kin K. Leung
https://arxiv.org/abs/2507.05535

Special-Unitary Parameterization for Trainable Variational Quantum Circuits
We propose SUN-VQC, a variational-circuit architecture whose elementary layers are single exponentials of a symmetry-restricted Lie subgroup, $\mathrm{SU}(2^{k}) \subset \mathrm{SU}(2^{n})$ with $k \ll n$. Confining the evolution to this compact subspace reduces the dynamical Lie-algebra dimension from $\mathcal{O}(4^{n})$ to $\mathcal{O}(4^{k})$, ensuring only polynomial suppression of gradient variance and circumventing barren plateaus that plague hardware-efficient ansätze. Exact, hardware-…

Towards understanding Accelerated Stein Variational Gradient Flow -- Analysis of Generalized Bilinear Kernels for Gaussian target distributions
Viktor Stein, Wuchen Li
https://arxiv.org/abs/2509.04008 …

Towards understanding Accelerated Stein Variational Gradient Flow -- Analysis of Generalized Bilinear Kernels for Gaussian target distributions
Stein variational gradient descent (SVGD) is a kernel-based and non-parametric particle method for sampling from a target distribution, such as in Bayesian inference and other machine learning tasks. Different from other particle methods, SVGD does not require estimating the score, which is the gradient of the log-density. However, in practice, SVGD can be slow compared to score-estimation-based sampling algorithms. To design a fast and efficient high-dimensional sampling algorithm with the adv…