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A new stochastic primal--dual algorithm for solving a composite optimization problem is proposed. It is assumed that all the functions/operators that enter the optimization problem are given as statistical expectations. These expectations…

Optimization and Control · Mathematics 2020-06-23 Pascal Bianchi , Walid Hachem , Adil Salim

In this paper, we survey recent progress on the theory of maximally monotone operators in general Banach space. We also extend various of the results and leave some open questions.

Functional Analysis · Mathematics 2013-07-23 Jonathan M. Borwein , Liangjin Yao

Weighted V-line transforms map a symmetric tensor field of order $m\ge0$ to a linear combination of certain integrals of those fields along two rays emanating from the same vertex. A significant focus of current research in integral…

Classical Analysis and ODEs · Mathematics 2025-11-05 Gaik Ambartsoumian , Rohit Kumar Mishra , Indrani Zamindar

Hidden convexity is a powerful idea in optimization: under the right transformations, nonconvex problems that are seemingly intractable can be solved efficiently using convex optimization. We introduce the notion of a Lagrangian dual…

Optimization and Control · Mathematics 2025-11-07 Venkat Chandrasekaran , Timothy Duff , Jose Israel Rodriguez , Kevin Shu

Energy momentum tensors of higher-derivative free scalar conformal field theories in flat spacetime are discussed. Two algorithms for the computation of energy momentum tensors are described, which accomplish different goals: the first is…

High Energy Physics - Theory · Physics 2022-07-06 Andreas Stergiou , Gian Paolo Vacca , Omar Zanusso

Through duality it is possible to transform left fractional operators into right fractional operators and vice versa. In contrast to existing literature, we establish integration by parts formulas that exclusively involve either left or…

Optimization and Control · Mathematics 2024-05-02 Delfim F. M. Torres

A covariant scalar-tensor-vector gravity theory is developed which allows the gravitational constant $G$, a vector field coupling $\omega$ and the vector field mass $\mu$ to vary with space and time. The equations of motion for a test…

General Relativity and Quantum Cosmology · Physics 2021-11-22 J. W. Moffat

The main focus of this paper is to study multi-valued linear monotone operators in the contexts of locally convex spaces via the use of their Fitzpatrick and Penot functions. Notions such as maximal monotonicity, uniqueness,…

Functional Analysis · Mathematics 2008-10-01 M. D. Voisei , C. Zalinescu

We propose a classical analogue of the vertex algebra in the context of classical integrable field theories. We use this fundamental notion to describe the auxiliary function of the linear auxiliary problem as a classical vertex operator.…

High Energy Physics - Theory · Physics 2015-07-14 Anastasia Doikou

Systems of ordinary differential equations (or dynamical forms in Lagrangian mechanics), induced by embeddings of smooth fibered manifolds over one-dimensional basis, are considered in the class of variational equations. For a given…

Differential Geometry · Mathematics 2018-12-07 Demeter Krupka , Zbyněk Urban , Jana Volná

We propose and study a weakly convergent variant of the forward--backward algorithm for solving structured monotone inclusion problems. Our algorithm features a per-iteration deviation vector which provides additional degrees of freedom.…

Optimization and Control · Mathematics 2022-08-15 Hamed Sadeghi , Sebastian Banert , Pontus Giselsson

In this study, we are concerned with spectral problems of second-order vector dynamic equations with two-point boundary value conditions and mixed derivatives, where the matrix-valued coefficient of the leading term may be singular, and the…

Classical Analysis and ODEs · Mathematics 2010-01-25 Douglas R. Anderson

As a contribution towards the understanding for the field equations of diffeomorphism invariant theories of pure gravity, we demonstrate in great detail that the expression for the field equations of such theories can be derived within the…

General Relativity and Quantum Cosmology · Physics 2025-12-30 Jun-Jin Peng

A new and extensive formalism is developed for monads and galaxies in non-standard enlargements. It is shown that monads and galaxies can be manipulated using order-preserving and order-reversing set-to-set maps, and that set properties…

Logic · Mathematics 2024-06-12 Niels Charlier , Hans Vernaeve

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters as well as the latent positions of the nodes in the network. The variational approach is much faster than…

Methodology · Statistics 2021-06-01 Yan Liu , Yuguo Chen

Logarithmic conformal field theories are based on vertex algebras with non-semisimple representation categories. While examples of such theories have been known for more than 25 years, some crucial aspects of local logarithmic CFTs have…

High Energy Physics - Theory · Physics 2021-07-28 Jürgen Fuchs , Christoph Schweigert

We present a parallelized primal-dual algorithm for solving constrained convex optimization problems. The algorithm is "block-based," in that vectors of primal and dual variables are partitioned into blocks, each of which is updated only by…

Optimization and Control · Mathematics 2022-05-04 Katherine Hendrickson , Matthew Hale

We prove a dichotomy for o-minimal fields $\mathcal{R}$, expanded by a $T$-convex valuation ring (where $T$ is the theory of $\mathcal{R}$) and a compatible monomial group. We show that if $T$ is power bounded, then this expansion of…

Logic · Mathematics 2024-12-24 Elliot Kaplan , Christoph Kesting

Transports preserving the angle between two contravariant vector fields but changing their lengths proportional to their own lengths are introduced as ''conformal'' transports and investigated over spaces with contravariant and covariant…

General Relativity and Quantum Cosmology · Physics 2015-06-25 Sawa Manoff

We consider convex-concave saddle-point problems where the objective functions may be split in many components, and extend recent stochastic variance reduction methods (such as SVRG or SAGA) to provide the first large-scale linearly…

Machine Learning · Computer Science 2016-11-04 P Balamurugan , Francis Bach
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