English
Related papers

Related papers: Lions' representation theorem and applications

200 papers

We generalise the Riesz representation theorems for positive linear functionals on $\mathrm{C}_{\mathrm c}(X)$ and $\mathrm{C}_{\mathrm 0}(X)$, where $X$ is a locally compact Hausdorff space, to positive linear operators from these spaces…

Functional Analysis · Mathematics 2023-05-31 Marcel de Jeu , Xingni Jiang

The present paper deals with the representation theory of the reflection equation algebra, connected with a Hecke type R-matrix. Up to some reasonable additional conditions the R-matrix is arbitrary (not necessary originated from quantum…

Quantum Algebra · Mathematics 2009-11-10 P. A. Saponov

Let $r \leqslant n$ be nonnegative integers, and let $N = \binom{n}{r} - 1$. For a matroid $M$ of rank $r$ on the finite set $E = [n]$ and a partial field $k$ in the sense of Semple--Whittle, it is known that the following are equivalent:…

Combinatorics · Mathematics 2024-01-02 Matthew Baker , Tong Jin

The Herglotz representation theorem for holomorphic functions with non-negative real part is a fundamental result in the theory of holomorphic functions. In this paper, we reinterpret the Herglotz representation in the context of modern…

Functional Analysis · Mathematics 2024-11-25 Mainak Bhowmik , Poornendu Kumar

In this paper, we give new proofs of the celebrated Andr\'eka-Resek-Thompson representability results of certain axiomatized cylindric-like algebras. Such representability results provide completeness theorems for variants of first order…

Logic · Mathematics 2018-11-07 Mohamed Khaled , Tarek Sayed Ahmed

Offline reinforcement learning (RL), which refers to decision-making from a previously-collected dataset of interactions, has received significant attention over the past years. Much effort has focused on improving offline RL practicality…

Machine Learning · Computer Science 2022-11-03 Paria Rashidinejad , Hanlin Zhu , Kunhe Yang , Stuart Russell , Jiantao Jiao

Despite large language models' (LLMs) recent advancements, their bias and hallucination issues persist, and their ability to offer consistent preferential rankings remains underexplored. This study investigates the capacity of LLMs to…

Computation and Language · Computer Science 2024-10-14 Xiutian Zhao , Ke Wang , Wei Peng

Solitons in two-dimensional quantum field theory exhibit patterns of degeneracies and associated selection rules on scattering amplitudes. We develop a representation theory that captures these intriguing features of solitons. This…

High Energy Physics - Theory · Physics 2024-09-04 Clay Cordova , Nicholas Holfester , Kantaro Ohmori

The classic Reinforcement Learning (RL) formulation concerns the maximization of a scalar reward function. More recently, convex RL has been introduced to extend the RL formulation to all the objectives that are convex functions of the…

Machine Learning · Computer Science 2023-01-30 Mirco Mutti , Riccardo De Santi , Piersilvio De Bartolomeis , Marcello Restelli

This paper concerns a nonlinear elliptic equation involving a critical Sobolev growth and a lower-order term. Under a Lions's condition, we prove the existence of at least one positive solution. Our approach consists in constructing a…

Analysis of PDEs · Mathematics 2020-11-19 Zakaria Boucheche

Emotions are intimately tied to motivation and the adaptation of behavior, and many animal species show evidence of emotions in their behavior. Therefore, emotions must be related to powerful mechanisms that aid survival, and, emotions must…

Artificial Intelligence · Computer Science 2018-07-26 Joost Broekens

Based on the complete-lattice approach, a new Lagrangian duality theory for set-valued optimization problems is presented. In contrast to previous approaches, set-valued versions for the known scalar formulas involving infimum and supremum…

Optimization and Control · Mathematics 2024-01-26 Andreas H. Hamel , Andreas Löhne

Machine learning methods are solving very successfully a plethora of tasks, but they have the disadvantage of not providing any information about their decision. Consequently, estimating the reasoning of the system provides additional…

Machine Learning · Computer Science 2025-01-27 Eric Nyiri , Olivier Gibaru

We introduce a logic, called LT, to express properties of transductions, i.e. binary relations from input to output (finite) words. In LT, the input/output dependencies are modelled via an origin function which associates to any position of…

Formal Languages and Automata Theory · Computer Science 2018-05-31 Luc Dartois , Emmanuel Filiot , Nathan Lhote

In this lecture we review apprearance of the Riemann-Roch Theorem in classical function theory, Algebraic topology, in theory of pseudo-differential operators and finally in noncommutative geometry. We show also it usefulness in many…

Operator Algebras · Mathematics 2007-05-23 Do Ngoc Diep

Clifford theory establishes a relation between the representation theory of a finite group and its normal subgroups. In this paper, we establish the Clifford theory for the modular representations of finite groups. The proofs are based on…

Representation Theory · Mathematics 2025-03-05 Devjani Basu

Large language models often hallucinate with high confidence on "random facts" that lack inferable patterns. We formalize the memorization of such facts as a membership testing problem, unifying the discrete error metrics of Bloom filters…

Machine Learning · Computer Science 2026-04-07 Anxin Guo , Jingwei Li

There has been some work in the literature on limit theorems for the trace of commutators for compact Lie groups. We revisit this from the perspective of combinatorial representation theory.

Representation Theory · Mathematics 2025-02-14 Jason Fulman

Propositional linear time temporal logic (LTL) is the standard temporal logic for computing applications and many reasoning techniques and tools have been developed for it. Tableaux for deciding satisfiability have existed since the 1980s.…

Logic in Computer Science · Computer Science 2016-09-15 Mark Reynolds

We develop a mathematical framework to address a broad class of metric and preference learning problems within a Hilbert space. We obtain a novel representer theorem for the simultaneous task of metric and preference learning. Our key…

Machine Learning · Computer Science 2025-10-28 Peyman Morteza
‹ Prev 1 8 9 10 Next ›