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Related papers: pyBregMan: A Python library for Bregman Manifolds

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Fourier transforms are an often necessary component in many computational tasks, and can be computed efficiently through the fast Fourier transform (FFT) algorithm. However, many applications involve an underlying continuous signal, and a…

Mathematical Software · Computer Science 2022-09-27 Eric Bezzam , Sepand Kashani , Paul Hurley , Martin Vetterli , Matthieu Simeoni

PyMembrane is a software package for simulating liquid and elastic membranes using a discretisation of the continuum description based on unstructured triangulated two-dimensional meshes embedded in three-dimensional space. The package is…

Soft Condensed Matter · Physics 2023-08-25 D. A. Matoz-Fernandez , Siyu Li , Monica Olvera de la Cruz , Rastko Sknepnek

The Bergman kernels of monomial polyhedra are explicitly computed. Monomial polyhedra are a class of bounded pseudoconvex Reinhardt domains defined as sublevel sets of Laurent monomials. Their kernels are rational functions and are obtained…

Complex Variables · Mathematics 2023-08-14 Debraj Chakrabarti , Isaac Cinzori , Ishani Gaidhane , Jonathan Gregory , Mary Wright

There is a need for open-source libraries in emission tomography that (i) use modern and popular backend code to encourage community contributions and (ii) offer support for the multitude of reconstruction techniques available in recent…

This document describes concisely the ubiquitous class of exponential family distributions met in statistics. The first part recalls definitions and summarizes main properties and duality with Bregman divergences (all proofs are skipped).…

Machine Learning · Computer Science 2011-05-16 Frank Nielsen , Vincent Garcia

We introduce a Python framework designed to automate the most common tasks associated with the extraction and upscaling of the statistics of single-impact crater functions to inform coefficients of continuum equations describing surface…

Computational Physics · Physics 2014-10-31 Scott A. Norris

One of the key issues in the analysis of machine learning models is to identify the appropriate function space and norm for the model. This is the set of functions endowed with a quantity which can control the approximation and estimation…

Machine Learning · Computer Science 2021-03-30 Weinan E , Chao Ma , Lei Wu

The Blahut-Arimoto algorithm is a well-known method to compute classical channel capacities and rate-distortion functions. Recent works have extended this algorithm to compute various quantum analogs of these quantities. In this paper, we…

Information Theory · Computer Science 2024-06-10 Kerry He , James Saunderson , Hamza Fawzi

An analogue of the total variation prior for the normal vector field along the boundary of piecewise flat shapes in 3D is introduced. A major class of examples are triangulated surfaces as they occur for instance in finite element…

Numerical Analysis · Mathematics 2020-06-24 Ronny Bergmann , Marc Herrmann , Roland Herzog , Stephan Schmidt , José Vidal Núñez

We introduce \`a la Vasilevski the weighted poly-Bergman spaces in the unit disc and provide concrete orthonormal basis and give close expression of their reproducing kernel. The main tool in the description if these spaces is the so-called…

Complex Variables · Mathematics 2020-08-31 R. El Harti , A. ElKachkouri , A. Ghanmi

In this paper, we propose a Bregman frame for several classical alternating minimization algorithms. In the frame, these algorithms have uniform mathematical formulation. We also present convergence analysis for the frame algorithm. Under…

Numerical Analysis · Mathematics 2016-05-27 Tao Sun , Lizhi Cheng

Trimpack is a library of routines written in Fortran that allow to create unstructured triangular meshes in any domain and with an user-defined size distribution. The user must write a program that uses the elements of the library as if it…

Mathematical Software · Computer Science 2023-02-07 Juan M. Tizón , Nicolás Becerra , Daniel Bercebal , Claus P. Grabowsky

Information geometry applies concepts in differential geometry to probability and statistics and is especially useful for parameter estimation in exponential families where parameters are known to lie on a Riemannian manifold. Connections…

Machine Learning · Statistics 2014-05-01 Garvesh Raskutti , Sayan Mukherjee

The intention of this survey to collect in one paper many recent results and advances related with Bergman type projection acting in various spaces of analytic functions in several complex variables in the unit ball, tubular domains over…

Complex Variables · Mathematics 2025-11-14 R. F. Shamoyan , M. G. Bashmakova

In this paper we study the problems of minimizing the sum of two nonconvex functions: one is differentiable and satisfies smooth adaptable property. The smooth adaptable property, also named relatively smooth condition, is weaker than the…

Optimization and Control · Mathematics 2019-04-10 Xiaoya Zhang , Hui Zhang , Wei Peng

In this paper we develop a Bregman regularized proximal point algorithm for solving monotone equilibrium problems on Hadamard manifolds. It has been shown that the regularization term induced by a Bregman function is, in general, nonconvex…

Optimization and Control · Mathematics 2026-01-21 Shikher Sharma , Simeon Reich

Separable Bregman divergences induce Riemannian metric spaces that are isometric to the Euclidean space after monotone embeddings. We investigate fixed rate quantization and its codebook Voronoi diagrams, and report on experimental…

Machine Learning · Computer Science 2018-10-26 Erika Gomes-Gonçalves , Henryk Gzyl , Frank Nielsen

We present Manify, an open-source Python library for non-Euclidean representation learning. Leveraging manifold learning techniques, Manify provides tools for learning embeddings in (products of) non-Euclidean spaces, performing…

Machine Learning · Computer Science 2025-07-18 Philippe Chlenski , Kaizhu Du , Dylan Satow , Raiyan R. Khan , Itsik Pe'er

We present a generic branch-and-bound algorithm for finding all the Pareto solutions of a biobjective mixed-integer linear program. The main contributions are new algorithms for obtaining dual bounds at a node, for checking node fathoming,…

Optimization and Control · Mathematics 2021-12-06 Nathan Adelgren , Akshay Gupte

A restricted Boltzmann machine (RBM) is an undirected graphical model constructed for discrete or continuous random variables, with two layers, one hidden and one visible, and no conditional dependency within a layer. In recent years, RBMs…

Machine Learning · Statistics 2019-09-12 Andee Kaplan , Daniel Nordman , Stephen Vardeman
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