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We study the approximation of operators acting on probability measures on a product space with prescribed marginal. Let $I$ be a label space endowed with a reference measure $\lambda$, and define $\cal M_\lambda$ as the set of probability…

Optimization and Control · Mathematics 2026-03-24 Samy Mekkaoui , Huyên Pham , Xavier Warin

We give a complete classification of conformally covariant differential operators between the spaces of $i$-forms on the sphere $S^n$ and $j$-forms on the totally geodesic hypersphere $S^{n-1}$. Moreover, we find explicit formul{\ae} for…

Differential Geometry · Mathematics 2016-10-03 Toshiyuki Kobayashi , Toshihisa Kubo , Michael Pevzner

Neural operators excel as deterministic surrogates, but inevitably collapse to the conditional mean when applied to stochastic PDEs, discarding the variance and tail structure upon which uncertainty quantification depends. Recovering this…

Machine Learning · Computer Science 2026-05-18 Kai Hidajat

We develop a divergence-minimization (DM) framework for robust and efficient inference in latent-mixture models. By optimizing a residual-adjusted divergence, the DM approach recovers EM as a special case and yields robust alternatives…

Statistics Theory · Mathematics 2025-11-25 Lei Li , Anand N. Vidyashankar

We reconstruct the rank-one, singular (point-like) perturbations of the $d$-dimensional fractional Laplacian in the physically meaningful norm-resolvent limit of fractional Schr\"{o}dinger operators with regular potentials centred around…

Functional Analysis · Mathematics 2018-04-04 Alessandro Michelangeli , Raffaele Scandone

$ \newcommand{\Z}{\mathbb{Z}} \newcommand{\eps}{\varepsilon} \newcommand{\cc}[1]{\mathsf{#1}} \newcommand{\NP}{\cc{NP}} \newcommand{\problem}[1]{\mathrm{#1}} \newcommand{\BDD}{\problem{BDD}} $Bounded Distance Decoding $\BDD_{p,\alpha}$ is…

Computational Complexity · Computer Science 2020-03-19 Huck Bennett , Chris Peikert

For every filter $\mathcal F$ on $\mathbb N$, we introduce and study corresponding uniform $\mathcal F$-boundedness principles for locally convex topological vector spaces. These principles generalise the classical uniform boundedness…

Functional Analysis · Mathematics 2020-11-03 Ben De Bondt , Hans Vernaeve

We study effects of a bounded and compactly supported perturbation on multi-dimensional continuum random Schr\"odinger operators in the region of complete localisation. Our main emphasis is on Anderson orthogonality for random Schr\"odinger…

Mathematical Physics · Physics 2021-03-03 Adrian Dietlein , Martin Gebert , Peter Müller

For $0 \leq \alpha < n$ and $m \in \mathbb{N} \cap \left(1 - \frac{\alpha}{n}, +\infty \right)$, we consider certain fractional type operators $T_{\alpha, m}$ generated by $m$-orthogonal matrices and prove that, for $0 < \alpha < n$,…

Functional Analysis · Mathematics 2026-05-05 Pablo Rocha

We formalize a transfinite Phi process that treats all possibility embeddings as operators on structured state spaces including complete lattices, Banach and Hilbert spaces, and orthomodular lattices. We prove a determinization lemma…

Functional Analysis · Mathematics 2025-08-15 Bugra Kilictas , Faruk Alpay

In this paper, we study the reducing subspaces for the multiplication operator by a finite Blaschke product $\phi$ on the Dirichlet space $D$. We prove that any two distinct nontrivial minimal reducing subspaces of $M_\phi$ are orthogonal.…

Functional Analysis · Mathematics 2018-06-29 Shuaibing Luo

The Heisenberg Oscillator Algebra admits irreducible representations both on the ring $B$ of polynomials in infinitely many indeterminates (the {\em bosonic representation}) and on a graded-by-{\em charge} vector space, the {\em…

Algebraic Geometry · Mathematics 2013-10-21 Letterio Gatto , Parham Salehyan

In this paper, by dilation technique on Schauder frames, we extend Godefroy and Kalton's approximation theorem (1997), and obtain that a separable Banach space has the $\lambda$-unconditional bounded approximation property ($\lambda$-UBAP)…

Functional Analysis · Mathematics 2025-07-04 Qiyao Bao , Rui Liu , Jie Shen

A comprehensive analysis of Sobolev-type inequalities for the Ornstein-Uhlenbeck operator in the Gauss space is offered. A unified approach is proposed, providing one with criteria for their validity in the class of rearrangement-invariant…

Functional Analysis · Mathematics 2023-03-20 Andrea Cianchi , Vít Musil , Luboš Pick

We analyze effective approximation of unitary matrices. In our formulation, a unitary matrix is represented as a product of rotations in two-dimensional subspaces, so-called Givens rotations. Instead of the quadratic dimension dependence…

Optimization and Control · Mathematics 2019-05-16 Thomas Frerix , Joan Bruna

In the first part of the paper we study the structure of Banach spaces with a conditional spreading basis. The geometry of such spaces exhibit a striking resemblance to the geometry of James' space. Further, we show that the averaging…

Functional Analysis · Mathematics 2016-07-14 D. Freeman , E. Odell , B. Sari , B. Zheng

We study deterministic and stochastic primal-dual sub-gradient algorithms for distributed optimization of a separable objective function with global inequality constraints. In both algorithms, the norm of the Lagrangian multipliers are…

Optimization and Control · Mathematics 2017-06-20 Masoud Badiei Khuzani , Na Li

Part I. We prove a one-to-one correspondence between differential symmetry breaking operators for equivariant vector bundles over two homogeneous spaces and certain homomorphisms for representations of two Lie algebras, in connection with…

Representation Theory · Mathematics 2015-01-05 Toshiyuki Kobayashi , Michael Pevzner

The fractional Laplacian $(-\Delta)^{\alpha/2}$ is a non-local operator which depends on the parameter $\alpha$ and recovers the usual Laplacian as $\alpha \to 2$. A numerical method for the fractional Laplacian is proposed, based on the…

Numerical Analysis · Mathematics 2014-11-14 Yanghong Huang , Adam Oberman

This work proposes a framework for large-scale stochastic derivative-free optimization (DFO) by introducing STARS, a trust-region method based on iterative minimization in random subspaces. This framework is both an algorithmic and…

Optimization and Control · Mathematics 2024-09-26 Kwassi Joseph Dzahini , Stefan M. Wild
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